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 & techniques.

Predictive maintenance involves a condition-based process, forecasting when a physical asset failure can occur and facilitate maintenance at the right time. This is achieved through monitoring the condition of the asset through data from machine sensors and smart technology to alert the maintenance team when a piece of equipment is at risk of failing.

Even though predictive maintenance can be carried out via visual inspections of equipment, the effective way to establish a predictive maintenance strategy is by using an Enterprise Asset Management (EAM) to track sensor data or meter readings.

In recent times, digitalization via the Internet of Things (IoT), has aided in the improvement of predictive maintenance routines.

Predictive maintenance aims at 

  • minimizing the number of unexpected breakdowns and maximizing asset uptime, which improves asset reliability
  • reducing operational costs by optimizing the time you spend on maintenance work (in other words, doing maintenance only when you need to do it practically eliminates any chance of you wasting time doing excessive maintenance)
  • improving your bottom line by reducing long-term maintenance costs and maximizing production hour.


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.


Condition-monitoring equipment

Under predictive maintenance, each asset is monitored using condition monitoring equipment we discuss in this article. Specifically, the machines are fitted with sensors that capture data about the equipment 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 primary way through which maintenance personnel observes 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.

However, by using condition-monitoring sensors and predictive maintenance, you can have an accurate representation of what’s happening inside the asset without any 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 for its intended purpose. By using 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. It allows maintenance techs to make sense of what’s happening in the machines and identify any assets that (will) need attention.


Predictive formulas

This is where predictive maintenance goes beyond condition-based care. The data collected previously is analyzed using predictive algorithms that identify trends to detect when an asset will require repair, servicing, or replacement.

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


Laying the groundwork for PdM is essential for creating a system that will be sustainable for many years. The key is to start small and scale up as the organization adjusts to this new way of doing things.

1 – Identify critical assets

Start by identifying critical equipment and systems to be included in the program. Assets with high repair/replacement costs that are critical to production are often the best candidates for a PdM program.

2 – Establish a database

For the PdM program to be successful, another factor to consider is the presence of sufficient information that can offer actionable insights into machine behaviour. Historical data for each pilot equipment will be available from sources like CMMS, hard copy files, enterprise software from other departments, maintenance records and charts, etc.

3 – Analyze and establish failures modes

At this point, the organization will need to perform an analysis of the previously identified critical assets to establish their failure modes.

4 – Make failure predictions

With the most critical assets and failure modes identified, the next step is designing the right modelling approach that will form the basis for failure predictions.

The result of this stage is to deliver a fully automated system that:

  • monitors operating conditions via installed sensors
  • understands and predicts patterns created by data anomalies
  • and creates alerts when there is a deviation from established thresholds

5 – Deploy to pilot equipment

This is where predictive modelling is put to the test and validated by deploying the technology to a selected group of pilot equipment.

If the process is executed properly, there will be significant improvements to the company’s operations, even though noticeable impacts might take a few months to kick in, depending on the size and complexity of your organization.


  • Improved Yield due to increased equipment uptime
  • Improved margin due to reduced maintenance cost
  • Enhanced Efficiency due to increased equipment uptime
  • High reputation due to perfect order fulfilment 
  • Optimizing planned downtime
  • Minimizing unplanned downtime
  • Optimizing employee productivity
  • Increasing revenue

Predictive maintenance seeks to define the best time to do work on an asset so maintenance frequency is as low as possible and reliability is as high as possible without unnecessary costs.

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