From Log-Data to Regressive Machine Learning Models for Predictive Maintenance : A case study

There are three ways to deal with component failure: reactive maintenance, preventive maintenance, and predictive maintenance. Reactive maintenance is to repair only once something breaks. Preventive maintenance is to repair before it breaks, independent of actual wear. Predictive maintenance is performed on the basis of real time operational data, repairing when components cross a certain degradation threshold. With classification models one can determine the health state of a component. Regression models, on the other hand, allow the user to calculate a more precise estimate of remaining useful life. Previous research on regression models have exclusively used sensory data while classification models have used both sensory data as well as log-data. Research on predictive maintenance using regression models have found most success using SVM regression, decision trees, random forest regression, artificial neural networks and LSTM models. Companies have more and more data to their disposal about the performance of their machines, but usually in the form of log-data. The goal of this research is to find if it is possible to use log-data for regression models. If this is the case, more sophisticated regression models can be used to apply predictive maintenance more accurately on a broader scale than is currently the case. The project was performed through a case study at a company in the semiconductor industry in the Netherlands, with years of log-data of their product that are gradually degrading over time. After quantifying the log-data and trying all kinds of different regression models in combination with different time scales, the results were unilaterally abysmal and were unable to make any decent prediction. The reason for this according to several experts in the field of data science is that there was no in depth understanding of the data. They say it is required to have an integral understanding of the log-data and to closely collaborate with field engineers who know the data in and out. If a field ...