In line with the advancement of Industry 4.0 which provides opportunities for the utilization of sensors and Machine Learning (ML) technology, make Predictive Maintenance (PdM) practices much easier. Regarding implementing PdM with ML, manufacturers need to provide data that supports the machine learning process. However, the majority of data is unlabeled and still requires manual labeling to support the learning process, which is risky, costly, and labor-intensive. Therefore, the current research uses the integration of Active Learning (AL) and Semi-Supervised Learning (SSL) to solve labeling problems and support PdM models with a better level of generalization. First, unlabeled multi-sensor data stored on the main server database and slight labeled data becomes the research sample. Second, the AL scheme selects the most valuable unlabelled samples, to label and add to the training data set. Third, the SSL scheme to optimize the data usage, using the remaining samples to be labeled. Finally, based on the augmented training data set, the fault diagnostic model is trained to support the failure class prediction. Regarding the selection of the ML algorithm, the result of trained Random Forest Classification (RFC) could predict a fault diagnostic model of approximately 99,85%.

Adelina Zian Andriani

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Enabling Predictive Maintenance Using Machine Learning in Industrial Machines With Sensor Data