Impact of Noise on Machine Learning-based Condition Monitoring Applications: a Case Study

In the paper, application of Machine Learning (ML) techniques for the continuous monitoring of the health status of mild mission-critical industrial equipment is considered. A meaningful real-life case-study is presented in order to show how acquisition conditions may severely impact on the performance of the system. In particular, it is shown that a wrong estimate of noise effects in the deployed system may induce a wrong choice of the best features feeding the ML monitoring algorithm, hence affecting accuracy of the target devices. The discussed results may provide an useful guidance to the practitioner in the field during the design phase of ML-based devices depending of the equipment specifications and environmental conditions.