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Applications of PDM

The technique of predictive maintenance has found various applications by industry since the 1990s. With the use of machine learning, artificial intelligence (AI or KI for künstliche Intelligenz) and a proper integration of IOT devices with the PDM, faulty condition in a machine is very easy to detect far before it breaks down. This is very much in line with its definition. This helps in increasing not only the revenues and savings of an organization but also makes the working environment for employees a far safer one.

Where does PDM find its applications?

  • Transport industry – PDM is now extensively used to monitor the condition of vehicles and improve its working condition. Traditionally fleets were taken for maintenance around every six-thousand kilometres. This was very often not required and would also prevent them from operating. However, with the use of predictive maintenance, owners are enabled to decide if they would want a repair depending on the current condition of the vehicle. This helps in increasing the lifespan of the trains and decrease operating costs and enables owners to get the most out of the fleet of trains by keeping them operating for as long as possible. This also finds its use in dynamic dashboards in identifying recurring problems and drivers who have the most problematic styles of driving. This would enable maintenance technicians to prioritize the devices during maintenance on the basis of their usage. Artificial intelligence (AI or KI for German) algorithms in the logistics industry would also help technicians in designing normative plans of maintenance.
  • Manufacturing – The manufacturing sector also sees widespread use of machine learning in the applications of PDM. PDM is highly profitable for the manufacturing sector because it helps detect the worn out or malfunctioning parts of a machinery far before it finally stops working that is, it does exactly what its definition Without the use of PDM, machines would have a long downtime while the repairs take place. This would be of great financial damage to the owners. PDM also helps in the maintenance of a safe and optimal working condition for the employees. This also makes sure that the machinery is running at its fullest potential. This would indirectly increase productivity and hence revenues. PDM also helps manufacturers increase their savings because they would not have to purchase complete machines but would do with the mere maintenance of some components. There is a widespread use of vibration analysis, acoustic analysis and other well-integrated IOT devices to properly run a PDM.
  • Oil and gas sector – The machines used in the companies in this section are often placed in deep waters or such remote offshores that their condition is not visible. In such scenarios, Big Data can become extremely helpful for the companies in predicting failures of equipment and their lifetime. In this sector ultrasonic technology is widely used especially in finding out any leaks.


With the increasing amount of research that is being done in the field of AI, even more uses of predictive maintenance would be invented. Even more amount of efficiency would be achieved at even lower prices as time progress. Earlier this technique was much pricier but has now reduced due to the incorporation of technologies which work on cloud computing.