An Introduction to Predictive Maintenance (PdM) with CMMS

Over the last couple of years, you’ve probably seen terms like Industry 4.0, big data, machine learning, and Industry of Things in every other article. In the maintenance industry, all of these trends are connected by predictive maintenance.

According to this predictive maintenance report from Market Research Future, the global predictive maintenance market is expected to grow to $6.3 Bn by 2022.

As the research suggests, it seems that this growth is driven by the rising focus on reducing the operational costs and asset downtime.

While predictive maintenance boasts many benefits, barriers to entry such as high initial implementation costs and training requirements ensure that there is plenty of discussions that need to happen before any implementation.

Hopefully this article will help you join that conversation.

1) What Is Predictive Maintenance (PdM)?

Predictive maintenance is a proactive maintenance strategy that tries to predict when a piece of equipment might fail so that maintenance work can be performed just before that happens. These predictions are based on the condition of the equipment that is evaluated based on the data gathered through the use of various condition monitoring sensors and techniques.

Like every other proactive maintenance strategy, predictive maintenance aims to:

• Improve asset reliability through fewer breakdowns and reduced downtime
• Reduce operational costs by optimizing your maintenance work

While these are the same goals as for preventive maintenance, predictive maintenance does everything more efficiently.

If you are interested in the long answer, check out our side-by-side comparison of reactive vs preventive vs predictive maintenance.

2) How Does Predictive Maintenance Work?

Predictive maintenance (PdM) relies on condition-monitoring equipment to assess the performance of assets in real-time. By combining condition-based diagnostics with predictive formulas and with a little help from the Internet of Things (IoT), PdM creates an accurate tool for collecting and analyzing asset data. This data allows for the identification of any areas that need or will need attention.

Let’s look at the major elements mentioned in the above paragraph to get a clear picture of how predictive maintenance works.

Condition-monitoring equipment

Under predictive maintenance, each asset is monitored using condition-monitoring equipment. Specifically, the machines are fitted with sensors that capture data about the equipment so as to enable evaluation of the asset’s efficiency and track wear in real-time.

This step is essential because although physical inspections of equipment have traditionally been the major way through which maintenance personnel observe assets, there has been a critical shortcoming in that procedure — the most wear and tear happens “inside” the machines which means you need to take them apart to do a proper inspection.

This is far from ideal.

However, by using condition-monitoring sensors and predictive maintenance, you can have an accurate representation of what’s happening inside the asset without any kind of productivity disruptions.

These sensors measure different kinds of parameters depending on the type of machine. Most commonly, they measure vibration, noise, temperature, pressure, and oil levels, but you can go beyond that and even measure things like electrical currents and corrosion.

The Internet of Things

It is one thing to gather data, but quite another to be able to analyze and use the data. With IoT technology, the different sensors mentioned earlier can collect and share data. PdM relies heavily on these sensors to connect the assets to a central system that stores the information coming in. These central hubs run using WLAN or LAN-based connectivity or cloud technology.

From there, the assets can communicate, work together, analyze data, and recommend remedial action or take action directly based on how the system is set up.

This exchange of information is at the core of predictive maintenance and allows maintenance technicians to make sense of what’s happening in the machines and identify any assets that need attention.

Predictive formulas

This is where predictive maintenance goes beyond condition-based maintenance. The data collected previously is analyzed using predictive algorithms that identify trends with the aim of detecting when an asset will require repair, servicing, or replacement.

These algorithms follow a set of predetermined rules that compare the asset’s current behavior against its expected behavior. Deviations are an indication of gradual deterioration that will lead to asset failure. Service technicians can then intervene as required to avoid breakdowns.

3) Predictive Maintenance And CMMS

Over the years, CMMS — and other maintenance software — have played an active role in the continuous shift from manual processes and reactive maintenance to proactive maintenance techniques — PdM included.

In fact, it’s safe to say that CMMS is at the front and center of PdM application today. Here’s why:

• CMMS provides the initial data to get PdM rolling

The information gathered over time regarding asset performance helps to form the starting point and the initial dataset before PdM implementation. Though they may be other sources, like hard copy maintenance records and soft copy files, CMMS will provide the most comprehensive and easy to access source of historical information.

• CMMS generates alerts and work orders

With condition-monitoring sensor integration, a modern CMMS can automatically create an alert or generate a work order whenever the system detects that an asset is operating outside predefined conditions and parameters. These alerts prompt the maintenance team to take action and they help to significantly minimize unexpected downtime, increase overall efficiency, and lower repair costs.

Here you can see how the creation of one similar task looks like in Limble CMMS (this is a task that is automatically generated when the vibration rate exceeds your predefined limit):

These automatic prompts may be for repair, servicing or routine maintenance tasks. Some common examples of alerts and work orders include:

• Create a preventive maintenance work order after a machine has been running for a specific length of time or cycles.
• Generate a “Fill Coolant” task when the level of coolant in the reservoir is getting too low.
• Create an inspection task to check bearings in an air-conditioner’s motor.
• Create an inspection task to check belt condition in a fan.

In essence, even though PdM generates highly accurate asset data, that information will be limited in ease of application if it is not combined with a CMMS. On the other hand, CMMS on its own cannot measure or predict machine health. By incorporating both technologies together, users get a tool that is indispensable for a modern maintenance management strategy.

• CMMS serves as a central organizational tool

CMMS pulls different information about assets together and presents them in a centralized platform that offers complete equipment “situation report.”

For example, PdM will give the raw data from the machines but CMMS goes further to include information from other modules like asset history, inventory, spare parts management, workforce management, repair schedules and more — thereby helping the user make better-informed decisions.

• CMMS facilitates data interpretation

Although PdM tools provide valuable insights about asset condition through vibration, lubricant, heat, oil analysis etc, the data generated is enormous and would be cumbersome for humans to manage manually.

With the right CMMS, users get easy to understand “snapshot” of the data that’s coming in.

For more on how to implement a Predictive Maintenance plan, check out A Complete Guide to Predictive Maintenance