Machine Failure Prediction Using Multilabel Classification Methods

Abstract

Early detection of machine failure is crucial in every industrial setting as it may prevent unexpected process downtimes as well as system failures. However, machine learning (ML) models are increasingly being utilized to forecast system failures in industrial maintenance, and among them, multilabel classification techniques act as efficient methods. Therefore, this study analyzed machine failure data with five types of machine failures. Initially, a feature selection approach was also carried out in this study to determine the variables which directly cause machine failure. Furthermore, multilabel k-nearest neighbours (MLkNN), multilabel adaptive resonance associative map (MLARAM), and multilabel twin support vector machine classifier (MLTSVM) in adapted algorithms, Binary Relevance, ClassifierChain, and LabelPowerSet in problem transformation approaches, and Random Label Space Partitioning with Label Powerset (RakelD) in ensemble classifiers were employed. To train these models, both the original data set as well as data frame after the feature selection was used, and hamming loss, accuracy, macro, and micro averages were calculated for each of these classifiers. According to the results, MLkNN in adapted algorithms and LabelPowerset in problem transformation approaches performed better than other classifiers used in this study. Therefore, it can be concluded that MLkNN and LabelPowerset could be used to classify multilabel with positive results. KEYWORDS: adapted algorithms, ensemble classifiers, feature selection, machine failure, machine learning, multilabel classification, problem transformation.