Virtual Sensors for Fault Diagnosis: A Case of Induction Motor Broken Rotor Bar
IEEE Sensors Journal
broken rotor bar, data stream, drift detection, ensemble learning, fault, Virtual sensor
This article presents an industrial implementation of a virtual sensor in the process of fault detection of an induction motor. An ensemble-learning soft-sensor is developed to detect broken rotor bar that is essential to prevent irreparable damage. Most of the existing diagnostic methods assume that the data distribution is static and that all data is available during the training, while in real applications, the data become available as data streams. The proposed method is inspired by the ensemble learning algorithm, which is combined with a new drift detection mechanism. The advantages of the proposed approach are three-fold. First, a fair comparison with other algorithms show the effectiveness of the soft sensor scheme. Second, the presented concept change detection algorithm is capable of detecting a new class in the data stream as well as data distribution change, and last but not least, the efficacy of the proposed algorithm is demonstrated using benchmark concept drift data streams.
Hosseinpoor, Zahra; Arefi, Mohammad Mehdi; Razavi-Far, Roozbeh; Mozafari, Niloofar; and Hazbavi, Saeede. (2021). Virtual Sensors for Fault Diagnosis: A Case of Induction Motor Broken Rotor Bar. IEEE Sensors Journal, 21 (4), 5044-5051.