A fortune teller hovering her hands over an illuminated crystal ball attempting to predict maintenance needs.

What is Predictive Maintenance?

Predictive maintenance (PdM) is a proactive maintenance technique that uses real-time asset data (collected through sensors), historical performance data, and advanced analytics to forecast when asset failure will occur. Using data collected by condition-monitoring devices during normal operation, predictive maintenance software uses advanced formulas (called algorithms) to compare real-time data against known measurements, and accurately predicts asset failure.

Advanced PdM techniques may also incorporate cutting edge technology such as machine learning and artificial intelligence (AI). The result of PdM is that maintenance work can be scheduled and performed before an asset is expected to fail with minimal downtime.

Difference Between Preventive and Predictive Maintenance

Preventive maintenance (PM) and predictive maintenance (PdM) share a common goal – to stop asset failures before they happen. However, they differ in their approach.

In a typical PM program, maintenance activities are commonly scheduled according to the manufacturer’s recommendations. Maintenance technicians can also identify the need for maintenance through regular inspections. While useful, these methods are only capable of identifying the most obvious problems based on one’s sense of sight, sound, touch, and smell. (For health reasons, we do not encourage anyone to diagnose equipment problems via taste.) Once issues are discovered, maintenance activities are usually scheduled on a strict, time-based or usage-based interval.

Predictive maintenance relies on sensors to identify the need for maintenance. Not only are sensors more accurate than human senses, but they can detect internal wear that cannot be directly observed, is too dangerous for humans to inspect, or would otherwise require equipment to be shut down and opened up. Maintenance events are then scheduled based on an asset’s real condition and performance, and performed only when needed. More about how predictive maintenance works is described in the following section.

How Predictive Maintenance Works

Think for a moment about the weather forecast provided by your local TV news station. To provide an accurate weather forecast, meteorologists collect and analyze weather data obtained from multiple sources, such as Doppler Radars, satellites, and surface-level weather stations. These devices measure conditions such as air temperature, wind speed, and barometric pressure, and send the data to a database. With the assistance of computer-based modeling and analytics tools, meteorologists are able to turn the stored data into a weather forecast presented to viewers.

Based on the forecast, viewers can prepare for the days ahead, including how to dress, what road conditions are expected, and how travel times may be affected. Predictive maintenance works in a similar way. Although you cannot control all events, with an accurate prediction you can often prevent asset failure. Predictive maintenance consists of 3 main components: 1) capturing sensor data, 2) communicating data, and 3) making predictions via data analysis.

Capturing Sensor Data

A thermographic image of a pipeline without thermal insulation, being monitoed by a condition-monitoring sensor as part of a predictive maintenance (PdM) program.

As with condition-based maintenance (CbM), predictive maintenance utilizes sensors and nondestructive testing to evaluate an asset’s performance and condition. Condition-monitoring sensors can perform “spot checks” at regular intervals or continuously monitor assets while they are in normal operation. Common condition-monitoring technologies include:

  • Infrared thermography: Detects temperature using infrared imaging.
  • Acoustic monitoring: “Listens” for sonic and ultrasonic frequencies.
  • Current analysis: Measures voltage and electrical current.
  • Corona detection: Identifies electrical discharge.
  • Vibration analysis: Monitors displacement, velocity, or acceleration to identify vibration patterns.
  • Oil analysis: Checks lubrication of machinery and assesses oil condition.

Communicating Data

Once sensors have captured equipment condition and performance data, it must be stored and analyzed. One advanced communication technology is called the Internet of Things (IoT) where equipment sensors send and share information via a wired or wireless internet connection. Data is sent to, and stored in, a database where it awaits analysis.

Making Predictions

As its name implies, predictive maintenance is based on the ability to make assumptions about when an asset will fail. This capability is what sets PdM apart from CbM. Collected data is analyzed to identify trends and forecast when an asset is expected to fail. The algorithms use predetermined rules to compare an asset’s current performance against its expected performance, determine the level of deterioration, and estimate when maintenance will be needed. Computerized maintenance management system (CMMS) software provides historical equipment data used in predictive algorithms, as well as creates and tracks maintenance work orders based on the predictive analysis.

Advantages and Disadvantages of Predictive Maintenance

Advantages of PdM

Remember that no maintenance technique should be used in a vacuum. A comprehensive maintenance strategy will include a variety of approaches and techniques. Below are some of the advantages of PdM:

  • Improved Ease of Maintenance Scheduling: Since the need for service is known well before work is actually required, activities can be scheduled when equipment is available for maintenance.
  • Increased Asset Uptime: Assets can remain in operation until maintenance is truly warranted. With other maintenance strategies, excessive downtime is created from too much, too little, or unscheduled maintenance work.
  • Combined Benefits of Other Maintenance Techniques: PdM combines the preemptive repair or replacement concept of preventive maintenance with asset performance data collected with condition-based maintenance to help you optimize maintenance resources.
  • Lower MRO Inventory Costs: An effective PdM program helps maintenance teams plan ahead, reducing the need for last-minute purchases of under-stocked parts, expedited shipping costs, or costs incurred by overstocking inventory.

Disadvantages of PdM

Even with all its benefits, be aware of some of the potential drawbacks of predictive maintenance.

  • Large Upfront Cost: A predictive maintenance program requires a large investment in condition monitoring hardware, advanced analytical software, employee training, and man-hours to purchase and install.
  • Required Expertise: Employees must be trained to use monitoring equipment, interpret the data received from sensors, and analyze reports generated by PdM software.
  • Not Cost-Effective for All Assets: In facility-centric environments, other maintenance techniques are often cheaper and more effective than predictive maintenance. The cost of setting up PdM on low-value assets may outweigh any potential benefits.

When to Use Predictive Maintenance

The decision whether to use predictive maintenance depends on the return on investment (ROI), which is to say, will the money saved on a reduction in asset failures meaningfully exceed the costs of maintenance. Organizations should also consider an asset’s cost and criticality. It is most appropriate for manufacturing and production assets that are critical to the organization and assets with high repair and replacement costs. Organizations with remote or mobile assets, such as the oil and gas industry or those involved with fleet maintenance, can also benefit from predictive maintenance.

When Not to Use Predictive Maintenance

As we mentioned in the list of disadvantages, PdM is not suitable for some facility assets like buildings. For example, monitoring a roof for leaks would require numerous sensors to be installed without a guarantee that they will be located in areas where leaks will occur. In this case, preventive maintenance (e.g. periodic inspections) is a more appropriate option. Additionally, it is not cost-effective to set up a predictive maintenance program for equipment that is relatively cheap to replace in the event of catastrophic failure.

FTMaintenance Work Order Software

Predictive maintenance activities are best implemented with the help of CMMS software like FTMaintenance, which provides a single platform for documenting, managing, and tracking maintenance activities. FTMaintenance is comprehensive work order software that automates work order creation, notification and distribution, and closure. Mobile CMMS features allow you to manage work orders on-the-go. To see how FTMaintenance can help you improve your maintenance operations, schedule your demo today.

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