Filtering Misleading Repair Log Labels to Improve Predictive Maintenance Models

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Published Jun 29, 2022
Pablo del Moral Sławomir Nowaczyk Sepideh Pashami

Abstract

One of the main challenges for predictive maintenance in real applications is the quality of the data, especially the labels. In this paper, we propose a methodology to filter out the misleading labels that harm the performance of Machine Learning models. Ideally, predictive maintenance would be based on the information of when a fault has occurred in a machine and what specific type of fault it was. Then, we could train machine learning models to identify the symptoms of such fault before it leads to a breakdown. However, in many industrial applications, this information is not available. Instead, we approximate it using a log of component replacements, usually coming from the sales or maintenance departments. The repair history provides reliable labels for fault prediction models only if the replaced component was indeed faulty, with symptoms captured by collected data, and it was going to lead to a breakdown.

However, very often, at least for complex equipment, this assumption does not hold. Models trained using unreliable labels will then, necessarily, fail. We demonstrate that filtering misleading labels leads to improved results. Our central claim is that the same fault, happening several times, should have similar symptoms in the data; thus, we can train a model to predict them. On the contrary, replacements of the same component that do not exhibit similar symptoms will be confusing and harm the ML models. Therefore, we aim to filter the maintenance operations, keeping only those that can be used to predict each other. Suppose we can train a successful model using the data before a component replacement to predict another component replacement. In that case, those maintenance operations must be motivated by the same, or a very similar, type of fault.

We test this approach on a real scenario using data from a fleet of sterilizers deployed in hospitals. The data includes sensor readings from the machines describing their operations and the service logs indicating the replacement of components when the manufacturing company performs the service. Since sterilizers are complex machines consisting of many components and systems interacting with each other, there is the possibility of faults happening simultaneously.

How to Cite

del Moral, P. ., Nowaczyk, S. ., & Pashami, S. . (2022). Filtering Misleading Repair Log Labels to Improve Predictive Maintenance Models. PHM Society European Conference, 7(1), 110–117. https://doi.org/10.36001/phme.2022.v7i1.3360
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Keywords

Predictive maintenance, misleading labels, Machine Learning, Interaction

Section
Technical Papers