Different Approaches to Artificial Intelligence–Based Predictive Maintenance on an Axle Test Bench with Highly Varying Tests

Maintenance measures are widespread in the industrial environment, and various approaches to maintenance using artificial intelligence are increasingly gaining ground. Predictive assessments of system conditions ensure greater reliability and cost reductions through longer service life. The implementation of a machine learning and a deep learning algorithm for predictive maintenance through early damage detection on an electric rear axle test bench is presented in this paper. The algorithms were selected based on extensive literature research. This paper deals with the question of whether the approach of condition-based or statistical predictive maintenance provides more benefit for a development test bench with highly varying tests. The chosen method of deep learning and machine learning can predict damage for a specific device under test with an accuracy of up to 99% using only one torque signal. In fact, the machine learning approach was found to be sensitive to abnormal behavior on the test bench as well, leaving no abnormality undetected. Although the deep learning model was more resistant to other damage, a pretrained model can be applied to any similar device under test and deliver almost identical results. The methods presented can be adapted to various industrial applications with some adjustments, even without access to big data. This enables predictive maintenance from the very first applications.