Maintenance, Service, Industrial Solutions, Contractor, Industrial Contractors, Mechanical, Renfrow Industrial, Big Data

BigData Analytics for Predictive Maintenance

Industrial organizations are increasingly taking advantage of leading edge digital technologies to improve the effectiveness, accuracy and reliability of their enterprise maintenance systems. BigData technologies provide opportunities to develop and deploy predictive maintenance systems, which enable the timely and accurate estimation of lifecycle parameters for assets and equipment, such as RUL (Remaining Useful Life) and EoL (End of Life).

BigData technologies provide the means to collect, store and process large amounts of data that indicate the condition of the equipment, such vibration, acoustic, ultrasonic, temperature, power consumption and oil analysis datasets, as well as data from thermal images of the equipment.

But gathering the data is only the first step. To derive useful maintenance insights from these datasets, plant operators and their maintenance solutions integrators employ data mining and machine learning processes.

The bias challenges of data mining processes

Data mining processes are usually iterative. They aim to evaluate different models in terms of their predictive power and robustness.  As part of these processes, data scientists test different machine learning and statistical methods options against their effectiveness in predicting parameters or deriving insights on the maintenance process at hand. For this process, data scientists divide the available data into two datasets: one for training and one for testing.

  • The training dataset is used to train a machine learning program (i.e. algorithm) in proactively identifying situations that signal the need for maintenance, such as equipment degradation patterns.
  • The test dataset is used to evaluate the performance of the algorithm based on parameters such as the classification or prediction accuracy.

Why must training dataset be different from the testing dataset? Testing an algorithm on the data used to train it would automatically give good results. Moreover, an understanding of the datasets and their structure is essential towards identifying models that could potentially provide the required predictive insights. Data scientists are usually in charge of reviewing the datasets and proposing candidate machine learning algorithms. The latter need to be evaluated based on the available datasets.

One of the main challenges of the data mining process concerns the availability of proper datasets. For example, the identification of a failure state for a tool or machine based on sensor data requires several occurrences of the failure to be present in the datasets.

In general, tool failures are more frequent and therefore they are easier to find in relevant datasets. This is not always the case with machine failures, as plant owners or operators are not likely to have or keep historical data about failures of a given machine. Moreover, datasets can be dispersed across different systems, which makes the process of collecting them and ensuring they are suitable to be processed by a machine learning algorithm quite difficult.

Even in cases where proper data sets are available, the data mining process remains challenging, mainly in terms of associating insights found in the datasets with the real-life maintenance problems.

Specifically, all data mining processes are subject to bias problems, which stem from the fact that data scientists tend to tailor their programs to what is available in the datasets, in a way that achieves optimal performance on the test datasets.

This process, while optimal from a mathematical perspective, fails in several cases to produce effective results as it completely ignores the parameters of the business problem at hand.

For instance, the role of a specific parameter (e.g., temperature or humidity) on a degradation pattern could be short-lived or seasonal, rather than a decisive indicator that must be monitored at all times.

As another example, some failure indicators may relate to specific lots of a tool or part and should not be considered as permanent indicators of some failure mode.

Such seasonal or short-lived factors cannot be understood and excluded by a machine algorithm: As soon as they are consistently present in a dataset, the algorithm will likely consider them significant contributors to the failure.

Furthermore, these factors will be taken into account proportionally to the times they appear in the dataset, without considering their seasonality or randomness of appearance.

Domain knowledge to your rescue

To alleviate the overfitting bias, maintenance solution providers have to consider domain knowledge. The latter refers to knowledge and insights that are unique to the target industry and enterprise for which the solution is destined.

You must consider such knowledge during the implementation of any analytics project; Without domain knowledge, the predictive analytics solution will fail to address the real maintenance problem.  

Likewise, no data-driven predictive maintenance solution can be deployed without involving experts in the maintenance solution development process.

In the maintenance practice, domain knowledge is reflected in an organization’s FMEA (Failure Mode and Effects Analysis) and FMECA (Failure Modes, Effects and Criticality Analysis methodologies, which incorporate knowledge that:

  • Identifies potential failure modes for a product or process, which drives data scientists work in terms of the identification of these modes.
  • Assesses the risk associated with each one of these failure modes, which enables data scientists to seek for the proper risk factors and their probabilities in the collected data.
  • Ranks the issues in terms of importance, which steers the work to the most serious failure factors. Detecting and addressing the latter factors maximizes the value for money of the solution.

In practice, FMEA and FMECA processes provide expert knowledge in terms of the assets, their failure modes, the effects and causes of the various failures, as well as current controls and recommended actions. Moreover, they include some methods for assessing risks associated with the issues that are identified during the analysis, including prioritization of corrective actions.

The latter is based on methodologies such as assigning Risk Priority Numbers (RPNs).  RPNs are assigned for each failure mode based on the severity of a failure, the likelihood of occurrence for each cause of failure, and the likelihood of the prior detection for each cause of failure. In particular, RPNs are calculated by obtaining the product of the above listed ratings (i.e. Severity, Occurrence, Detection).

In the scope of the data mining, RPNs can then be used to prioritize algorithms and alleviate overfitting. To this end, data scientists do not select the algorithms that are the optimal from the mathematical viewpoint. On the contrary, they can select algorithms that best reflect the RPNs of the highest priority, in addition to considering their statistical presence in the dataset.

As a prominent example, data scientists should focus on predictive algorithms and attributes that focus on the most critical failures that must be avoided, as they are the most expensive and time consuming to repair, while leading to loss of prominent equipment functions.

The combination of insights from the FMECA/FMEA with knowledge derived from the data sets can lead to systems that better solve today’s maintenance problems. At the same time, they lead to systems with better value for the investment, meaning they contribute to many organizations’ top maintenance goals.

Taking advantage of domain knowledge

To take advantage of domain knowledge in BigData predictive maintenance systems, the following best practices and recommendations should be taken into account:

  • Assemble the right team: It is essential that you incorporate domain experts into your BigData team. They should work closely with data scientists, database experts, and programmers to deliver a solution that is not only technically robust, but also optimal for the business. Domain experts (e.g., maintenance workers and engineers) should be part of the team and engage actively throughout the project (not on an occasional basis).
  • Test and learn: The data mining process is iterative. Each algorithm and piece of expert advice should be tested and validated prior to being deployed. Note that this process acts as an educational device for both data scientists and domain experts too, who will only become knowledgeable about the process with each iteration. Likewise, they are likely to come up with new ideas to test and validate in the next cycle.
  • Invest in domain knowledge and processes: Predictive maintenance based on digital technologies is not possible without proper FMECA/FMEA processes in place. Therefore, investments in digital technologies should also be accompanied with investments in complementary assets, such as domain knowledge and processes.
  • Remember that successful predictive maintenance solutions aren’t easily replicated: It’s not easy to replicate a successful solution in different contexts (e.g. different plants and equipment). Each plant is likely to have its own unique FMECA/FMEA processes and therefore different domain knowledge and datasets. Thus, each new BigData project for predictive maintenance presents new challenges and there is only little room to exploit components from other deployments without changing and customizing them.
  • Communicate new data-driven knowledge: While expert knowledge is an important driving factor of data mining, don’t forget that data processing is able to derive insights and knowledge that was not known beforehand. Therefore, there should be a two-way interaction between data scientists and domain experts during the mining process. One the one hand, experts will provide knowledge that drives the specification of proper algorithms. On the other hand, the application of certain data processing algorithms on the maintenance datasets is likely to reveal new insights that data scientists should communicate back to the domain experts.
  • Collaborate: BigData from predictive maintenance is all about stakeholders’ collaboration. Data scientists, maintenance experts, IT experts should closely collaborate in developing the proper machine learning and data mining experts. Moreover, commitment from the C-Suite is also essential in order to lower resistance barriers and to successfully manage change.

Overall, successful BigData analytics for predictive maintenance requires that business goals and expert knowledge are well understood, alongside the maintenance datasets. The importance of domain knowledge is proven, not only in the case of enterprise maintenance , but also in a variety of use cases for other industrial sectors.

It’s essential that you structure such domain knowledge and that you engage relevant experts in your next enterprise maintenance BigData project. We hope that the above best practices will help you make this project a success.

Article Provided By: Prometheus Group

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

Maintenance, Service, Industrial, Industrial Solutions, Mechanical, Electrical, Contractors, Renfrow Industrial

What Is Facility Management?

When you’re running a business so small it fits into one tiny office, facility management is not something worth losing sleep over. But as your business grows, that one office of yours may turn into a building and a single machine transform into a production line.

Before you know it, you’ll have a bunch of assets that need to be taken care of on an ongoing basis.

This is where facility management enters the scene: It ensures you a well-organized environment in which both your business and employees can thrive.

Facility management: the basics

At its core, facility management is a profession that focuses on the efficient maintenance of an organization’s buildings and equipment in a way that offers the best value to the building owner and users alike.

It’s also a multi-disciplinary support service that can be applied in any niche or industry. Among its many applications is that it can ensure safety, functionality and comfort in the built environment as well as compliance with existing legal requirements.

In North America, the facility management market is experiencing increased patronage from a wide variety of businesses: The Transparency Market Research 2017 report estimated a compound annual growth rate of 13.6 percent between 2017 and 2024.

Still, if you have never used professional facility management services before, you may be wondering if, or when, it should become the next step for your business. Here are the signs that it’s time for a business to start thinking about adopting facility management:

1.Your maintenance cost is escalating.

It’s an accepted fact that maintenance costs money, but these costs should not run down your business.

When you notice that repairs and servicing costs are rising inexplicably every year, some common money-wasters to check include abuse or under-utilization of existing equipment, wasteful stocking of inventory and spare parts and unused office space. Wired reported in 2013 that over the previous 30 years, the United States had added about 2 billion square feet of office space to its existing stock, which is not something today’s highly mobile workforce needs. Having more space to maintain automatically increases your maintenance costs.

Another factor that quickly adds to your bottom line is poorly managed maintenance personnel and other staffing expenses. Over a 30-year period, while the operating and maintenance costs of a building account for 6 percent of total costs, personnel costs alone account for a staggering 92 percent, according to a British study reported on by Researchgate.

If you’re running a system where you frequently call on independent plumbers, electricians, heating engineers and other technicians, the costs quickly pile up. Not to mention the fact that engaging these contractors also carries the risk of quality control issues, especially if:

  • You have a very large facility.
  • You are managing multiple locations.
  • You have no real way to track whether tasks are being carried out properly.

One of our clients, Joe Romero from Myriad Genetics, had this exact problem. He had been hired as a facility manager and noticed that his predecessor had been tracking everything manually, which meant the company had no clue whether maintenance tasks were actually being completed.

When Romero implemented facility management software, he could see whether outside contractors were doing the work they were billing for. Long story short, he had to replace his primary maintenance vendor because he found out that that vendor was not doing the work he had been contracted to do.

Because of these problems, some businesses form an internal team or look for a single vendor to take over their back-office responsibilities. A good example is GoDaddy, which was able to realize 10 percent cost savings by employing integrated facility services. Another interesting note in that success story was how one reason GoDaddy went with integrated facilities services provider ISS was because ISS was already operating in all geographical regions GoDaddy was planning to expand to.

2. You’re having difficulties in asset management and tracking.

Knowing that over 40 percent of small business track their assets manually or don’t track them at all is concerning. While this practice causes minimal problems early on, real issues will start popping up as soon as you start to scale.

Facility management can help you manage and track assets and inventory better if you are experiencing any of the following:

  • Asset register is inadequate or doesn’t exist at all.
  • It’s becoming increasingly difficult to track the assets owned by the company.
  • The organization cannot confidently declare its asset position.
  • The current condition of any asset and its location is unknown.
  • If any equipment, machine or tool were to go missing, no one would notice.
  • You keep buying replacements for equipment only to find out later that you already had them.

Stanley Healthcare reported that a mobile solution for inventory tracking can reduce search times for needed equipment by 90 percent, as well as help a company realize significant cost savings by avoiding unnecessary inventory purchases (improving inventory invisibility) and equipment loss (shrinkage control).

3. You’re seeing a rising backlog of uncompleted maintenance tasks.

Multiple research sources, like this one from Steelcase Global, confirm how employee engagement positively correlates with workplace satisfaction. In other words, happy employees are productive employees. Without a designated facility management service, however, it is only a matter of time before they become frustrated and distracted because of leaking taps, broken light bulbs or an air conditioning unit in the staff canteen that isn’t working properly.

Even if these tasks are instructed to call appropriate services, these tasks are often put off untll later — so they start piling up. Soon, the business is faced with a considerable deferred maintenance list and very little hope of resolving everything. Research from Rick Biedenweg and his colleagues at Pacific Partners Consulting Group discovered that every $1 deferred in maintenance costs results in $4 of capital renewal needs in the future, so this is something you definitely want to avoid.

Preventing the creation of a backlog of uncompleted maintenance tasks is just one of many responsibilities of a facilities manager.

4. Recurring safety issues

Recurring safety issues are an indication that you are operating in a potentially dangerous environment. The simple truth is that improving safety at your facility is not a matter of choice — it is required by law. Every year, OSHA issues over 40 000 citations, with the most common repeated offenses being:

  • lack of personal protective equipment (PPE)
  • absence of a hazard-communication program for chemicals
  • failure to maintain logs of accidents and injuries
  • lack of safety training

Facility management takes these factors into accounts and can help you reach and maintain the highest levels of operational safety using a combination of technology and human expertise and following these extensive Environmental, Health, and Safety Guidelines.

When it’s time for a change

If any of the above situations describes the current situation in your business, it’s clear that time and valuable resources are being wasted. It doesn’t matter if you are going to form an in-house facility management team or outsource everything to an independent contractor. The point is that you understand how facility management can result in significant cost savings, improved safety performance and better overall service delivery for your company.

Article Provided By: Entrepreneur

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

Industrial, Maintenance, Service, Industrial Solutions, Industrial Contractors, Mechanical, Renfrow Industrial

How to Become A Data-Driven Manufacturer

Modern technology allows for successful organizations to focus their strategies on data-driven decision making. With the availability of so much data — and the tools — to make sense of it, any business that ignores how data can impact operations and processes is leaving improvements and efficiencies on the table.

Personally and professionally in business, data informs many day-to-day functions and plays a major role in big-picture decisions. In the industrial sector, data-driven manufacturing is a prevalent practice – and one that is well worth the technology investment. For example, a single manufacturing facility can execute hundreds to thousands of operational processes per day. That’s a lot of data that can be collected and be put to good use, but at the same time be extremely overwhelming. So you are probably wondering – how do I make sense of and effectively utilize this data?

Intelligence Manufacturing: How to Use the Data You Collect

In order to become a data-driven manufacturer, you have to not only have the capability to collect information from your machines, but also know how to use that data to make informed decisions. As mentioned above, the amount of data that can be collected in a manufacturing facility is massive. Below, we’ll look at the “whys” and “hows” of collecting and using data to increase the productivity, efficiency and results of your manufacturing operations.

  • Data-driven manufacturing can improve production efficiency: By collecting and analyzing data from manufacturing processes, you can identify areas where production volume or expected quality slipped below the acceptable baseline. From there, you can identify common factors that could help you determine why these inefficiencies occurred.
  • Intelligence can improve repair and maintenance efficiency: Collecting and actively reviewing data from machinery operations can help you proactively identify maintenance issues before they occur. In the short term, this gives you the benefit of more predictability and control over production downtime. Long term, with enough data on hand, you can schedule maintenance more efficiently and optimize your MRO processes.
  • Streamlined data entry and storage: By committing to data-driven manufacturing, you must, by necessity, standardize data collection processes (more on that shortly). By doing so, you will inherently streamline all processes around your data, easing the burdens of ad-hoc, manual data entry and processing.

Now that we’ve looked at the benefits of data-driven decision making, we’ll explain how you can put it into practice and become a true leading manufacturer.

Obtaining the right equipment is the first step — whether through machinery with data collection and connectivity functionality built-in, or by retrofitting existing machines with sensors. Connectivity is the second key component. Wireless or wired, your data collection tools should seamlessly transmit data to centralized storage for further analysis. Analysis is the final piece of the puzzle. With so much data on hand, the potential is vast, but additional training, and potentially more personnel, may be required to realize the full benefits that will be well worth your investment.

Article Provided By: ATS

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

Industrial, Service, Maintenance, Industrial Solutions, Industrial Contractor, Electrical, Maintenance, Renfrow Industrial

Take Baby Steps on Industrial Internet of Things

Collect usable data as the first step toward effective analysis.

The Industrial Internet of Things (IIoT) offers manufacturing organizations almost unimaginable potential to change the way managers and operators do their jobs by connecting production equipment to the cloud. In fact, Accenture, the global management consulting and professional services firm, has forecast that IIoT could add as much as $14.2 trillion to the global economy by 2030 by increasing productivity and detecting production problems early, while they can be corrected efficiently and economically.

For many, figuring out how to install the sensors and interconnections necessary to link all their hardware to a central database can seem like an overwhelming and expensive prospect. Large manufacturers typically have the ability to invest in implementing facility-wide or enterprise level IIoT platforms. Small to medium-sized organizations, however, often feel hamstrung by far smaller IT budgets and staffs, as well as by management’s unwillingness to accept the temporary disruptions to production a facility-wide conversion to IIoT might cause.

First things first

For manufacturing organizations like these, consider the “baby steps approach,” sometimes also called a discrete implementation. Essentially, this means that, early on, it’s important to stop worrying so much about uploading lots of data to the cloud and doing trending analysis, but focus instead on collecting usable data from equipment where it wasn’t being collected before. Think about taking on one small project at a time and mastering it before moving on to the next challenge.

This approach to IIoT focuses on making progress incrementally in a way that doesn’t come with the same financial, personnel or workflow interruption consequences that larger systems would. It offers a more realistic and economical starting point for many smaller manufacturing organizations. Starting with one specific application, rather than trying to apply IIoT to the whole facility, makes implementing IIoT far more manageable and affordable. What’s more, it forces managers to focus on a specific problem, ensuring a quick payback on the effort. By providing a fast return on a modest investment, it also inspires confidence that future steps will provide equally positive results.

Compressed air is an expensive resource in any manufacturing facility, so monitoring the performance of the compressed air system is a good first step in a discrete implementation of IIoT. Keeping these systems operating at peak efficiency offers big payback because losses due to leakage waste lots of energy. Inline sensors can be installed to monitor compressed air system variables such as pressure, flow, humidity, temperature and power consumption. Once collected, this data can be sent to a platform for conversion into a form that’s useful to the maintenance team. This data collection point can be the start for a facility-wide system for monitoring leakage and other system losses.

Five ways to think small about IIoT

Keeping these five tactics in mind can simplify the early stages of IIoT implementation for small to medium-sized companies:

1. Make your first bite a nibble, not a gulp. In just about every organization, the larger the budget for a project, the more people who must approve it and the more intense the payback monitoring will be. However, it can be tough to show a return on investment (ROI) quickly with an IIoT implementation because the implementation team is learning as they go. Rather than asking for all the money up front, it’s easier to ask for a few hundred dollars to cover the cost of some smart sensors, demonstrate the value of the information they deliver, and build management confidence in the concept.

2. Focus on the biggest trouble spots. Take the time to review maintenance records to determine which machines or processes in your facility represent the most significant sources of maintenance problems. These are the points where you should begin collecting data. Equipment with hard-to-find parts or difficult or expensive to repair also should be at the top of your list, as well as any machines that could represent a danger to personnel or other pieces of equipment if they fail because a problem went undetected.

3. Determine what you need to monitor to improve that asset’s operating efficiency. Component parameters such as temperature, pressure, humidity and vibration provide important clues that can indicate an asset’s condition and spot trouble before it happens. Collect the pertinent data and take a methodical approach to using this information. Assume that you’ve installed some smart sensors to monitor the voltage level of one of the components of a specific piece of equipment; an elevated voltage over an extended period might be an indication of future problems.

The next step is to decide how frequently this data should be reported back to the controller. Too long of an interval runs the risk of missing a fast-emerging problem, making it impossible to react in a timely way. Too short an interval can generate a flood of data that’s never analyzed and never used.

4. Choose a robust internet infrastructure. The solution chosen should include a centralized collection server capable of receiving and transmitting data from all the sensors and other devices that could eventually be integrated into the network.

One method of data transmission is through the programmable logic controller (PLC), which is easy to implement. Choosing the right protocol to connect sensors with controllers and actuators is critical to the success of any IIoT implementation. IO-Link is a cost-effective open communications protocol that supports simple, scalable, point-to-point communications between sensors or actuators and the controller. It also allows two-way communications to receive data and then download a parameter to the device/actuator.

As a result, processes can be adjusted remotely. The advantages of IO-Link include the automatic detection and parameterization of the IO-Link device, device monitoring, diagnostics, changes on the fly and reduced spare part costs. Ultimately, the key to unlocking the power of smart sensors is in making diagnostic information easy to access. IO-Link allows for cyclic data exchange capabilities so that programmers easily can send the information directly to where it is required, either to a human-machine interface (HMI) screen, a signal light or a maintenance request. If sensor or actuator parameters need to be changed or calibrated, this can be done remotely, even while the production line is running, ensuring that shutdowns, stoppages and unnecessary costs are avoided.
A different approach to data transmission is outside of the PLC, which has the benefit of not adding data/message traffic to the controller’s scan time. One especially economical approach to gathering data is as simple as attaching a wireless smart sensor to a piece of equipment to support remote advanced condition monitoring.
Information can be transmitted directly to a robust data platform on the user’s mobile device, which trends, assesses and monitors machine health quickly and accurately. Filtration is a good example – the state of a filter’s life can be assessed by comparing the differential pressure of the inlet to the outlet side of the filter.

Another example is measuring temperature trends for the hydraulic power unit – an increase in the pump outlet’s temperature can indicate the pump is losing efficiency and starting to fail. Being able to monitor equipment performance issues and evaluate the data onsite helps managers identify problems early and fix them before an equipment failure occurs.

5. Balance monitoring frequency with operational costs. Cloud-based solutions allow for around-the-clock monitoring, as well as alerting operations or maintenance personnel when conditions exceed preset limits. But there can be a point of diminishing returns. In the early stages of the implementation, focus on maximizing the quality of the data being collected, rather than the amount. Installing hundreds of sensors to generate masses of data doesn’t necessarily produce any useful information. It’s far more important to focus on collecting the right actionable data.

Article Provided By: Plant Engineering

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

Electrical, Maintenance, Service, Industrial Solutions, Industrial Contractors, Industrial, Renfrow Industrial, South Carolina, Electrical Industry

7 Guiding Principles of a Maintenance 4.0 Strategy

Formulating a digital strategy is not easy, but these guidelines can help you get off the sidelines and into the game.

It is not uncommon for organizations to struggle with many issues related to digitalization. With the hype around digitalization at fever pitch, it is easy to become overwhelmed by the multitude of options available in the marketplace. But the strongest contributing factor to implementation challenges is a failure to devise a strategy for an extensive period of uncertainty.

Formulating a Maintenance 4.0 strategy is not easy. An aggressive strategy based on over investing in unproven technologies or a conservative strategy of merely waiting on the sidelines are unrealistic options.

Guiding principles

The seven guiding principles for a Maintenance 4.0 strategic plan are:

1. Invest based on the business case. The primary obligation to shareholders does not change just because of the changes occurring within the manufacturing arena. What does this mean from a practical perspective? Strategic choices require due diligence and investments must be made based on expected returns to the business. If you cannot justify the investment to your shareholders, it should not be made.

2. Take an incremental approach. Adopting Maintenance 4.0 does not mean eliminating existing processes and technologies. Yes, there are legacy systems that are no longer effective, but the default should be to adopt existing practices. It’s likely that your organization already uses some so-called Maintenance 4.0 practices. Big bets on new, still-evolving product categories should be minimized.

3. Upgrade existing maintenance practices. In support of incrementalism, industrial plants should evaluate maintenance best practices that can be adopted in parallel to Maintenance 4.0.

4. Adaptability. The fast pace of innovation has significant implications for industrial plants adopting a new strategy. Will a solutions breakthrough that occurs in 2020 be redundant by 2025? An accelerated speed of change is the new normal, and companies must identify ways to incorporate new Maintenance 4.0 solutions while minimizing disruptions to operations.

5. Data as an asset. Big data is the oxygen of Maintenance 4.0. Although vast amounts of data are generated by sensors embedded within industrial machinery, most of the data is not yet used today. A guiding principle for a Maintenance 4.0 strategy is that data governance practices must be instituted and the underlying value of operational data should be captured.

6. O&M collaboration. Successful implementation of Maintenance 4.0 cannot happen unless the views of plant-level employees are considered as part of the requirements process. Without allocating resources to training and on boarding, Maintenance 4.0 will be stuck in the planning phase.

7. Share risk with external vendors. Industrial players are unable to keep up with the rapid pace of change. Fortunately, OEMs and other service providers are finding ways to address market opportunities and overcome challenges to their own underlying businesses. Industrial plants should spend the time understanding the strategic road map of their most important OEM suppliers and consider mutually beneficial ways to align investments and plans.

Setting your strategy

In Figure 1, the S curve of technology innovation is applied to the Maintenance 4.0 strategy. The S curve refers to the stages of a new technology’s performance as it matures. In the first phase, it evolves slowly. After a breakthrough occurs, performance improves rapidly. Next, the pace of improvement declines. Finally, with maturation, greater performance is difficult to achieve.

It should be noted that the S curve is not an exact model, and not all technologies follow the curve. Given the pace of innovation, even if Maintenance 4.0 follows the S curve, there is no way of knowing the duration of Rapid Improvement 4.0 (Stage 2). One can look back at this period as merely the beginning of emergence, attributable to new areas of innovation in the data-science discipline – specifically automated machine learning.

Three strategic postures for applying good strategy in uncertain business environments are articulated in the HBR article “Strategy Under Uncertainty” (see Figure 2):

  • Shape the Future – Shapers are organizations that drive their industry toward new structures.
  • Adapt to the Future – Adapters choose where and how to compete within the given structure.
  • Reserve the Right to Play – Organizations invest incrementally to “stay in the game” without committing to new strategies.

These models also can be applied to plants adopting Maintenance 4.0 practices. Let’s review each.

Reserve the right to play. With this approach, industrial players build intelligence around new solution offerings without altering current practices. Although this option seems to be the safest approach, it may be the riskiest. Plants that wait and expect to catch up at a later date may miss the financial and competitive advantages of adopting Maintenance 4.0. Sometimes, strategic patience is a virtue; other times, it is a mistake.

Shape the future. Similarly, there is an obvious downside for industrial plants that pursue “shape the future.” Industrial plants that have built their own internal machine learning capabilities fit into this category. It requires significant investment in recruiting big data scientists and building out Maintenance 4.0 competencies. At the same time, the level of investment and risk may not justify the potential reward, especially because newer solutions may provide the same value at a lower cost.

Adapt to the future. This approach is the middle path for industrial plants. These organizations recognize the uncertainty associated with disruptive technologies and position themselves to react when opportunities emerge. Of equal importance is how to fit this Maintenance 4.0 into existing processes.


How is this relevant for Maintenance 4.0? With Maintenance 4.0 in its infancy, industrial plants may be tempted to wait on the sidelines until solution winners and losers can be easily identified. This is a bad idea. The average industrial plant misses 17 days in production every year, costing billions of dollars in lost revenue. If the cost of downtime for one minute of production in the automotive industry can reach $50,000, the risk of not pursuing Maintenance 4.0 is far greater than the risk of waiting.

Article Provided By: Plant Services

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

Maintenance, Service, Industrial Solutions, Industrial Contractors, Industrial, Mechanical, Electrical, Renfrow Industrial, True Definition of Preventive Maintenance

What Is Preventive Maintenance? The True Definition

By implementing a preventive maintenance (PM) program, your facility will be on its way to higher efficiency, accuracy, and profitability in no time.


Preventive maintenance (also called “preventative maintenance”) is a systematic approach to building operations that aims to identify and correct equipment failures before they actually happen. Unlike reactive maintenance, which occurs when equipment has already broken down, preventive maintenance is a more proactive approach to keeping assets in optimal working order.

To perform preventive maintenance, a facilities team will conduct several types of routine check-ups on equipment. Procedures may include (but are not limited to) testing, inspections, part replacements, measurements, lubrication, tightening and adjustments of equipment. These procedures help facilities team stay on top of maintenance and deliver the right type of repairs when they are needed. Issues that are detected early can be fixed faster, which may extend the overall useful life of systems and equipment within a building.


With the right tools and resources, facility managers can use PM to transform the way they care for their buildings and grounds. Here are five reasons why adopting a preventive maintenance plan can benefit your facilities department and your organization as a whole.

Advantage #1: You’ll save money and waste less energy.

Costly repairs and replacements occur less often when equipment is maintained. While it’s nearly impossible to prevent all reactive maintenance, the likelihood of a major breakdown is minimal when a facilities department employs a preventive maintenance program. Regular maintenance can also lead to reduced energy bills. Equipment that works as expected is more likely to run efficiently and use less energy than unmaintained equipment.

Advantage #2: You’ll extend the useful life of critical equipment.

Boilers, HVAC systems, plumbing, air handling units and roofing are just a few examples of assets in a building that require consistent maintenance for optimal performance. Checking equipment regularly means they’ll last longer, since issues are spotted early and can be dealt with as needed. Equipment also remains in its best shape, therefore extending its useful lifetime.

Advantage #3: You’ll create a safer work environment for your team.

A majority of facility managers would agree that maintaining safety in their buildings is a top priority. A preventive maintenance plan can keep buildings safer and more secure. Equipment that meets safety standards is less likely to pose risk or cause serious harm to operators. With regular maintenance, dangerous and catastrophic equipment failures are reduced, making your facility a safer place for staff and occupants alike.

Advantage #4: You’ll significantly reduce unexpected disruptions and asset breakdowns.

Regular equipment checks mean you won’t be caught off guard if something goes wrong or breaks down. A preventive maintenance plan will indicate when parts need to be replaced. This means that there will be little-to-no unexpected downtime, since you’ll have replacement parts and service technicians on hand for timely repairs. Replacing parts before they are damaged also means there will be less wear-and-tear compared to machines that are serviced using run-to-failure maintenance.

Advantage #5: You’ll be able to plan ahead and budget for upcoming maintenance.

Following a maintenance schedule means you’ll be better prepared to stick to your department’s allocated budget. Repairs are planned in advance, so all costs associated with replacement parts and repair personnel are factored into the department’s budget. A preventive maintenance plan will also help you keep track of asset names, parts and contact information for repair personnel. As maintenance is performed throughout the year, you’ll know exactly what annual expenses to calculate for upcoming maintenance.

Article Provided By: Facility Executive

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

Industrial, Service, Maintenance, Industrial Solutions, Industrial Contractors, Mechanical, Renfrow Industrial, Spartanburg, Charleston, South Carolina, Industrial Automation

Four Ways Industrial Automation Will Evolve

Industrial automation has continued evolving as machines become more connected and smarter while reducing maintenance costs for companies.

Industrial automation traces its roots back to the 1970s when the original distributed control system (DCS) was developed. Once Dick Morley, a mechanical engineer, developed the first programmable logic controller (PLC), several startups released human interface software to help add innovative automation solutions to a wide range of industries.

Industrial automation hasn’t stopped evolving. New sensors, amplifiers, displays, controls, recorders, valves, actuators, and more are being released constantly. Many manufacturers focus on niche applications, and most suppliers only recently have begun expanding products beyond their initial applications and local geographies.

Automation uses control systems to operate equipment such as machinery, networks, and navigation with little or no human involvement. Industrial automation is often accomplished by mechanical, hydraulic, and electronic systems. US car manufacturers have been early and heavy adopters of industrial robots, with General Motors being the first to establish an automation department.

Manufacturers use industrial robots and intelligent machines to care for various operations that require speed, stamina, and accuracy. Industrial automation can reduce energy usage, materials used, and labor waste. Industrial robots typically help perform long production runs for large manufacturers, but new capabilities may soon bring industrial automation to small and medium industries.

Four ways industrial automation will evolve

Industrial robots now being developed and deployed will be multi-functional so that a single machine can be used for several different tasks. Industrial automation systems also will have the ability to make decisions and work autonomously without human help. This will allow humans to work in more rewarding jobs with more efficiency and productivity than before.

1. Low maintenance

Manufacturers know downtime kills productivity, so they’re putting pressure on engineers to make sure robots of the future will require less maintenance. New industrial robots will need to be easy to deploy, operate, and maintain as the majority of employees using these robots may be lacking programming and engineering skills.

2. More intelligent

Breakthroughs in artificial intelligence will make robots smarter. Intelligent robots will be able to sense and predict changes and adjust on their own, and industrial robots will be able to solve more complex tasks without human input.

3. Highly connected

The concepts being developed by the Industry 4.0 revolution (referring to a new phase in Industrial Revolution that focuses on interconnectivity, machine learning, automation, and real-time data) include networked connections that will allow robots to collaborate, not only with humans, but with other industrial robots. Industrial automation will work with more synergy than ever before.

4. Human safety

Robots are already being used in environments and processes that are dangerous for humans, but with human-robot collaboration set to increase, how they work together will be improved. Even mobile industrial robots will be able to detect and predict human behavior to increase safety in the workplace.

Article Provided By: Plant Engineering

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

Maintenance, Service, Industrial, Industrial Solutions, Industrial Contractors, Contractor, Mechanical, Electrical, Renfrow Industrial, Spartanburg, Charleston, South Carolina, Big Data Challenges

Big Data Challenges in Factory Maintenance

Utilization of big data and the Industrial Internet of Things (IIoT) has the industry’s most forward-thinking companies upgrading not only their facilities, but the technology used in them to get ahead of the competition.

Like many other industries, manufacturing is undergoing a technology revolution. The “big data” buzzword has been tossed around the manufacturing realm for some time now, and IIoT is bringing new possibilities to downtime analysis and prevention. But new technology doesn’t come without new challenges, especially in the area of factory maintenance. So how can organizations overcome these challenges and turn big data into big rewards?

Big Data, Big Benefits

When it comes to industrial maintenance, big data makes predictive analytics faster and easier, allowing you to shift operations from a more diagnostic (or preventative) maintenance approach to a proactive one. This way, you can make more calculated decisions in regards to your machines, leading to improved production outputs and significant cost savings. Along with sophisticated tools like thermography, sonics/ultrasonics, vibration testing and more, your plant operators can see exactly what is going on with a particular machine and plan accordingly. While this technology can build a strong foundation for any modern manufacturer’s predictive maintenance (PdM) program, the challenge lies in pinpointing the right data to use—and how to use it.

Maintenance, Service, Industrial, Industrial Solutions, Industrial Contractors, Mechanical, Electrical, Contractor, Renfrow Industrial, Spartanburg, Charleston, South Carolina, Big Data ChallengesMore Information, More Challenges

Computerized maintenance management systems (CMMS) can churn out large quantities of information ready to be analyzed, but what good is all this data if you don’t know how to use it? You may face this hurdle in the early stages of implementing your predictive maintenance strategy. To overcome the burden of data-overload in a factory maintenance setting, aim to choose decision-making criteria specific to your industry or production goals up front, and only utilize data relevant to that specific criteria. Implementing systems and programs that make decisions binary, with limits set for specific actions, may also be helpful. Once those details are set, all that’s left for you to do is stick with the program and strategy.

Weighing the Costs

Even with committed employees and a full understanding of PdM benefits, the biggest challenge manufacturers face when implementing these new technologies is their initial cost. In today’s competitive global market, organizations can’t make investments based on an expectation that this technology fixes everything. The best way to work around this is to start small: take the time to identify what systems and processes are most relevant to immediate needs, and implement the necessary changes to manage them. This will help shrink down the data volume to a digestible amount, lower costs, and improve only necessary internal processes. This focus is a great way to test the predictive technology waters before diving headfirst into the investment in data.

Embracing Change

The growing use of technology in industrial maintenance will ultimately lead to a shift in processes—and let’s face it, change can be hard to overcome. Along with the regular pains of introducing and learning brand new methods, your long-time workers who are set in their experience may not be as ready to adopt a new advanced, data-centered maintenance strategy. Luckily, most maintenance employees are loyal, willing to adjust to new methods over time… your organization just needs to devote the time to properly train both your existing workforce and your new hires. Once your team sees the benefits of big data first hand, trust in the new process will be earned and maintained.

Article Provided By: ATS

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

Maintenance, Industrial, Service, Industrial Solutions, Industrial Contractors, Mechanical, Contractor, Renfrow Industrial, Spartanburg, Charleston, South Carolina

VR’s Benefits for Machine Maintenance

Virtual reality (VR) and augmented reality (AR) can help maintenance engineers in process industries such as the food industry repair industry and reduce potential downtime.

Industrial maintenance can be a costly, time-consuming process, but it is also one that is required if a plant’s systems are to remain effective, efficient and even operational. For the food industry, even a momentary period of downtime can result in lost production.

Yogurt production lines, for example, must ensure that produce is kept at certain temperatures and handled in a certain time period to minimize the risk of spoilage. Downtime due to faulty or ineffective systems can result in an entire batch needing to be disposed of, often to the tune of thousands of pounds in lost profit.

It is for this reason that many plants have made improving the quality of maintenance a priority, with increased adoption of preventive or predictive maintenance practices.

However, while incorporating predictive maintenance functionality into industrial automation systems is valuable in minimizing the frequency of maintenance, there will be times when a maintenance engineer must head out to repair a system. Depending on the role of the equipment requiring maintenance and the nature of the maintenance itself, this period of planned downtime can still have a negative impact on production.

Valuable tools

Technologies such as virtual reality (VR) and augmented reality (AR) are proving to be valuable tools in helping maintenance engineers repair equipment and systems faster and more effectively than ever before. Maintenance staff can use a VR headset or even their mobile phones to see an augmented view of equipment, highlighting the performance data of individual components and the best way of accessing them.

For example, let’s say that a dairy plant’s manufacturing execution system (MES) highlights that a rotary evaporator, used to standardize the dry matter of milk in the early production stages, requires maintenance. As the evaporator consists of several components, a maintenance engineer could use AR to see a virtual representation of the components in the evaporator and identify which needs attending to.

This is only possible if the AR application can access the real-time operational data from the evaporator. In this instance, the engineer could use the AR functionality of an Industrial Internet of Things (IIoT) platform.

The value of AR in food machinery maintenance extends beyond the dairy sector to any segment that features high levels of automation. Meat processing plants, for example, can contain any number of complex systems, from deboning equipment to mincing machines, that could prove challenging for an engineer to maintain efficiently. By using AR to pick out the problem part and the best means of accessing it, this process can be sped up to reduce the financial impact of sudden downtime.

By using a purpose-built AR application, the engineer can view real-time system data from the IIoT platform and see which components are performing inefficiently. In this case, it could be that the evaporator’s compressor requires lubrication. The engineer can then resolve this in the least disruptive way possible, minimizing the impact that necessary maintenance has on production.

With this, we can see why VR and AR are far more than quirky technologies for industrial businesses. If used properly alongside an IIoT platform that supports predictive maintenance, they can help plant managers achieve the next level of industrial maintenance efficiency, where the unavoidable downtime associated with maintenance can be minimized to mere moments.

Article Provided By: Plant Engineering

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

Maintenance, Industrial, Service, Industrial Solutions, Industrial Contractors, Mechanical, Electrical, Contractor, Renfrow Industrial, Spartanburg, Charleston, South Carolina

The Ultimate Preventive Maintenance Checklist

Enacting a preventive maintenance plan can yield great benefits: improved equipment operation, increased part quality and lower overall downtime to name a few. However, realizing these benefits does require an investment of time and effort to create and implement the plan.

Your preventive maintenance plan should be tailored to your facility, but there are many foundational elements common across the board. The easiest way to carry out these steps is often in the form of a checklist. Here is a sample template so you can begin building a preventive maintenance checklist of your own.

The Preventive Maintenance Pre-Check

Before you begin, take a look at this pre-checklist that contains some basic information that should be core to any preventive maintenance plan:

  1. Assess and inventory the current state of your facility: Set benchmarks based on current performance for efficiency, quality, uptime, maintenance and more — and be sure you understand every aspect of your facility that will undergo preventive maintenance.
  2. Locate documentation: For machinery, much preventive maintenance can be done in accordance with manufacturer recommendations and specs.
  3. Create documentation of your own: A facilities preventive maintenance checklist is a good start and quick reference, but be sure to comprehensively document your preventive maintenance plan and processes in a place where all employees can easily access and review it.

With these initial steps completed, you’re ready to start building a preventive maintenance checklist. Feel free to use the checklist guide below as a starting point:

The Ultimate Preventive Maintenance Checklist

Preventive Maintenance for Machines

  1. Ensure that machinery is clear of debris, before and after every shift.
  2. Wipe machine surfaces of lubricant, dirt and other loose debris each day.
  3. Regularly inspect tools for sharpness.
  4. Check for and replace worn or damaged tools.
  5. Routinely check all machinery fluid levels and air filters and replace as needed.
  6. Calibrate machines regularly.

Preventive Maintenance for Handling Equipment

  1. Regularly check belts for damage.
  2. Review calibration and programming for gantry machines. This is often a good opportunity to identify inefficiencies.
  3. Clean belts and other equipment in direct contact with materials and inventory at least once a day.
  4. Check and maintain motors and other power sources at least twice a year.

Preventive Maintenance for Facility Infrastructure

  1. Ensure that adequate space exists between machinery.
  2. Confirm that safety and caution areas are sufficiently marked.
  3. Keep walkways and other means of ingress and egress clear of debris and any other material.
  4. Guarantee that wires are properly secured and do not present a hazard.
  5. Check stairway and walkway railings regularly.
  6. Inspect structural building elements at least once a year — structural damage can further harm your capital investments and your inventory, creating exponential losses.
  7. Comprehensively check and repair building systems (electrical, plumbing, network) at least once a year.
  8. Examine fire detectors twice a year and remain in compliance with local regulations.
  9. Assess external grounds, including parking facilities, for hazards.
  10. Check your roof at least once a year.

Preventive Maintenance for Network and Data Systems

Note: As connected systems become increasingly common, good “data hygiene” is just as important for industrial facilities as it is for any other organization.

  1. Review your network security practices.
  2. Regularly review and identify the most immediate threats to your network security (i.e., have there been data breaches in the news recently?).
  3. Assure that employees comply with safe practices such as password security and good email practices (avoiding phishing schemes, etc.).
  4. Change Wi-Fi and other network passwords at least twice a year.

The items above cover a broad range of aspects for a facility, and they can all contribute to the more efficient, safer, more productive operation of your business. Keep this list handy and use it to tailor your plan as needed for your operation.

Article Provided By: ATS

If you would like to discuss how Renfrow Industrial can help you call us at 1-800-260-8412 or email info@renfrowindustrial.com.

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