Boost ROI, efficiency with predictive maintenance

What it is, why it matters and how it works
January 12, 2024 by
Pedro Jr.

Predictive maintenance (PdM) is transforming manufacturing operations and growth with technologies that include the Internet of Things (IoT), the cloud, mobile applications, artificial intelligence/machine learning (AI/ML) for analyzing and predicting information through data, and web applications for sharing complete operation data in one place. Industry 4.0 tools and technologies have turned PdM from an abstract concept into a practical solution.

What is predictive maintenance, and why is it important for manufacturers?

PdM is a type of maintenance that monitors the performance and condition of equipment during normal operation to reduce the probability of failures. It is similar to preventive maintenance or work performed on an asset before a failure occurs, not after the failure has stopped it.

The difference between preventive and predictive maintenance lies in the methods used, the amount of lead time available for a task, and the precision of scheduling. PdM uses condition monitoring tools and asset information to track equipment performance and then anticipate failure before it occurs. Ideally, PdM keeps maintenance frequency low while reducing time spent on unplanned maintenance and preventive maintenance.

An example is smart sensors. These sensors can detect a change in how assets operate, such as abnormal vibration in a part at higher-than-normal speeds. Sensors connected to maintenance software, like a Computerized Maintenance Management System (CMMS), transmit this message to the software to schedule maintenance. The software then notifies technicians of the newly scheduled task on their mobile devices.

Because predictive maintenance offers an ideal window for proactive maintenance tasks, it can help minimize the time involved in equipment maintenance, production hours lost due to maintenance, and the cost of spare parts and supplies.

The value of predictive maintenance According to Allied Market Research, the predictive analytics market in manufacturing, valued at $535 million in 2018, is projected to reach $2.5 billion by 2026. This shows the value attributed to this type of maintenance based on technology and five critical organizational factors: people, data, processes, tools and parts, and equipment. These factors, combined with technology, are known as the six pillars of a robust predictive maintenance program.

Here's how you can develop each area and use them to build a predictive maintenance program that lasts.

People: The journey to predictive maintenance starts here "No matter if your predictive maintenance plan looks good on paper if you don't have the support of the people doing the work," says Jason Afara, Solutions Engineer at Fiix®.

Every predictive maintenance pillar in your program needs people to build and maintain it. Data needs to be interpreted. Technology needs to be designed and managed. Therefore, everyone in your organization must understand how PdM works, why it is important, and what they can do to make it successful.

Getting people in your facility on board with the changes that predictive maintenance brings is essential but not always easy. It is important to gain the support of your maintenance team and create a culture of success in your facility.

Data: The link between the past, present, and future A predictive maintenance program needs information to succeed.

"Without data, you can't predict anything. If you don't have a baseline on what's normal for a pump or a conveyor, for example, you can't identify or predict anomalies," says Bryan Sapot, CEO of SensrTrx.

But with quantity also comes the need for quality.

The key is the right information coming from the plant floor.

Data is the link between the current performance of assets and the future state of the asset. Therefore, everything from production to failure modes must be constantly updated. These numbers also need to be accurate. If they differ from one system to another, it will derail your program.

Processes: Driven by people and teams People's processes involve how your maintenance team does their work. These describe how staff interacts with machines, data, each other, and everything else.

"You must understand who is responsible for what, how often data and tasks are reviewed, how you communicate, and how you plan, scale, and complete tasks," says Jason.

When it comes to team processes, it is crucial to know what processes your teams complete, how to capture asset data, and how data relates to future performance.

In short, your processes are how you work, how your maintenance team plans and does the things they need to do every day to succeed. An effective predictive maintenance program helps make your entire operation predictable, maximizing from working hours to asset performance.

Tools and Parts: Keys to a successful predictive maintenance program "Predictive maintenance is not something new," says Jason. "The difference between 20 or 30 years ago and now is that we have the tools and understanding of parts to do it better and with less cost."

Tools are the instruments used to measure the condition of assets, such as infrared cameras, and the tools used to inspect or repair equipment. Parts are the different components of the equipment.

Equipment: Not all machines were created for predictive maintenance It is important to know which equipment allows anticipating failures when designing a predictive maintenance program.

"Assets that fit a predictive maintenance program are those that provide good condition data with enough lead time to detect problems before a total failure," says Jason.

Jason also recommends applying predictive maintenance to your most critical assets with the most observable failure modes due to the time and money involved in building a PdM program.

Technology: The connection that ties all elements together Technology helps you manage, facilitate, and optimize the other pillars of predictive maintenance.

It is crucial for success to know which products are running and when, the cost of all your activities, and when the last maintenance was performed, among other factors.

There are many different technologies that can be used to manage a predictive maintenance program, from Enterprise Resource Planning (ERP) systems to Manufacturing Execution Systems (MES) and CMMS software.

A predictive maintenance program won't solve everything. But there are many benefits to having it, such as a more reliable operation that allows everyone in your organization to grow and be more efficient.

It can also generate notable financial results, according to a tenfold increase in return on investment, a 25% to 30% reduction in maintenance costs, a 70% to 75% decrease in failures, and a 35% to 45% reduction in downtime.

Leveraging those benefits involves building on key maintenance fundamentals. When those fundamentals are solid, you'll have a winning strategy.

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