Predictive maintenance (PdM) is a prominent way of maintaining equipment in the industrial business. It is frequently seen as the panacea for maintenance concerns these days. However, if you do not have a clear plan or strategy in place, this maintenance program may become extremely difficult and costly. A firm foundation must be followed by a thorough plan of action in order to attain success. Here’s a guide to assist you to reduce the procedure into manageable phases for your institution.

What is Predictive Maintenance?

Predictive maintenance (PdM) is a strategy that relies on various data analysis tools and problem detection methods. It is used to detect abnormalities in your operation and potential flaws in equipment and procedures so that you may correct them before they cause failure. By avoiding unforeseen reactive repairs, predictive maintenance allows the frequency of maintenance to be minimized while avoiding the expenses associated with performing too much preventative maintenance.

Who uses Predictive Maintenance?

Generally, operations & maintenance teams and reliability crew monitor impending equipment failure and repair duties using predictive maintenance technologies and asset management systems. Let’s check out how they need to implement predictive maintenance.

What are the major steps to implement Predictive Maintenance?

1. Check out the existing data and find out your critical assets

Before embarking on the predictive maintenance roadmap, you must first assess your present equipment. Perform a thorough examination and make a note of any downtime, flaws, or audit fines. Examine the preventative and reactive maintenance conducted on each asset in detail. Now you must prioritize your assets depending on the criticality of the situation. You should base your judgment on the information gained from your analyses in this case.

Most industrial devices can collect and analyze essential data sets, and many manufacturers currently do so for event recording or historical analysis. Data pertaining to machine current, torque, or pressure may be all that is required to detect early warning signals of issues and a variety of failure types.

This is a critical phase in developing a PdM strategy. Because it is doubtful that you will be able to implement predictive maintenance for all equipment pieces with legacy PdM solutions, you must choose which ones. After determining which assets are critical, you must determine which assets require high and frequent maintenance. After you’ve taken care of these, you may start planning for the remainder of your equipment. On the contrary, there are no limitations with respect to assets or tags that can be monitored with an AI-based PdM solution.

2. Apply adequate sensors

When compared to analyzing the same data for its intended purpose, gathering data is far easier. Use sensors (IoT or otherwise) to gather and exchange data in the following stage. The whole predictive maintenance procedure is primarily reliant on these sensors to connect the assets to a central system that archives the data flowing in. WLAN or LAN-based connections, as well as cloud technology, primarily serve the major role here. Depending on how the system is configured, the assets can then communicate, collaborate, analyze data, propose corrective action, or act immediately.

When using the sensor, you should invest good research and time to understand which predictive maintenance technologies and prediction algorithms are appropriate for each asset. For example, infrared thermography is the ideal approach to employ on equipment that may leak air or steam, whereas vibration analysis is best utilized on spinning equipment but not on slow-rotating equipment (less than 5 rpm). For slow-spinning machinery, oil analysis and acoustic analysis are preferable. A good PdM solution should provide 100% plant coverage i.e., for both static and rotating equipment.

3. Establishing the equipment parameters

When your system is operational, sensors will continuously gather data, and the technology will eventually discover certain patterns and trends in the data. Based on the data, it will create a prediction model for your equipment, defining when an asset is likely to fail. Obviously, the greater the amount of data collected, the higher the accuracy rate.

At some point, your model will become so accurate that even little changes in performance will suggest when a failure is imminent. This will send an alert to your staff, informing them that it needs to be examined and maintained right away.

4. Plan the action if an alert is notified

By now, when your PdM solution has predicted the patterns, as soon as any data falls out of place, an alarm is delivered to the team. When you receive any alarm, your team should be prepared with a good strategy that defines who is responsible for doing maintenance, which actions need to be completed for each specific asset, and the days/times of preferred maintenance. Once the job is properly split, the process becomes smoother and easier to implement. It’s recommendable that all members from top management to machine operators are involved in the approach. This requires designating a strategy owner, who is then accountable for the communication and coordination of the plan, assuring change management measures, etc.

5. Make sure that proper systems are in place

You have now discovered the defects in your machinery. You must respond to these concerns immediately, assign them to your staff, and ensure that inspections and maintenance are carried out appropriately. Without this, you will be able to notice mistakes but will be unable to take rapid corrective action. Failure of equipment is still likely, which implies your original labor and expenditure were in vain. When a sensor detects a reading outside of parameters, CMMS software connects to it to produce, allocate, and manage a work order. This simplifies the whole predictive maintenance process, ensuring that the proper personnel are assigned the right assignment and have all the information they need to inspect and repair the equipment.


Predictive maintenance is to determine the ideal time to do maintenance on an asset so that maintenance frequency is as low as possible and dependability is as good as possible without incurring needless expenditures. Maintenance managers employ predictive maintenance, sensor data, artificial intelligence, and machine learning to assist their teams in making better decisions about when to execute maintenance.

With UptimeAI’s “AI Expert,” operations teams can go beyond traditional predictive analytic solutions that stop at raising alarms (often false). Instead, “AI Expert” provides an AI-based assistant combining deep learning, domain expertise, and self-learning workflows to achieve the highest operational efficiency. Try UptimeAI.