What is predictive maintenance in the first place
Predictive maintenance is the approach used to assess the status of a certain equipment (e.g. gas turbine, car engine), determining where the maintenance should take place. Predictive maintenance makes use of data analytics, AI, ML or algorithm. This approach started being used more and more in various applications, as it offers numerous advantages.
What are the alternative types of maintenance
The other types of maintenance that we are also using in our everyday life are two:
- Reactive maintenance
- Proactive maintenance
There are example of reactive maintenance and predictive maintenance we are all familiar with. Reactive maintenance is applied to most of the items we are using at home. Our washing machine for example, is repaired using reactive maintenance, as we repair it/replace it only when it breaks. In this case, we react to the event of a failure.
Proactive maintenance instead, is when we make a repair before a failure appear. One example we are all familiar with, is the car maintenance that is done every x km. The can could still run, or might be already damaged, and in indication is to replace the oil, replace some parts and so on.
Why is predictive maintenance important
Predictive maintenance, especially when associated to AI/ML, allows detecting the failure early on. If the failure is detected early on time, the cost of repair will be lower and the intervention time will be quicker. Moreover, if the failure is not detected on time, the machine might fail causing major issues to the user and/or industrial line.
Types of maintenance vs asset condition – Courtesy of Presenso, available at https://www.presenso.com/single-post/2017/05/24/the-economics-of-the-smart-factory-how-does-machine-learning-lower-the-cost-of-asset-maintenance-part-1/
Knowing the intervention time becomes crucial for economic point of view. Too early will cause a higher prevention cost. The machine in-fact can still run, but we are repairing it early on because its exact status is unknown. Too late instead, will cause higher repair costs. The machine in fact will collapse all of a sudden, causing issues due to its unavailability and extra costs of parts to be fixed.
Cost vs failure trade-off generic graph – Courtesy of CBM services, available at https://ivctechnologies.com/2017/08/29/reactive-preventive-predictive-maintenance/reactive-preventative-predictive-maintenance/
How can AI help with early detection of failures
The advantage of AI, is that it can process a large amount of data in a very short time. By doing that, AI can detect anomalies from a combination of data, that might not be visible to human judgement or by standard algorithms. Coming back to the car engine example, AI could be able to detect a temperature drift or a vibration change, and indicate that there is a problem in one of the pistons. The early detection can save a major engine failure, and let the engine be repaired with a minor intervention and be back on the road.
Ucar et Al. [1] is reporting the enhancement that AI is bringing in the predictive maintenance domain citing, among others, deep learning neural network, feed forward neural network, convolutional neural network, support vector machine, etc… To provide some evidences, below are the amount of AI methods that are appearing in google scholar and divided by type. The rapid increase is sign that the advantages and the possibilities, are certainly remarkable.
Courtesy of [1] – https://doi.org/10.3390/app14020898
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Author: Simone Togni
Date: 24/06/2024
Platform: aisciencetalk.blog