Towards Highly Automated Machine-Learning-Empowered Monitoring of Motor Test Stands

Artificial Neural Network Based Optimal Feedforward Torque Control of Interior Permanent Magnet Synchronous Machines: A Feasibility Study and Comparison with the State-of-the-Art

“…The CNN-based condition monitoring system is able to detect those motor faults in the deteriorated current response with extremely high accuracy (errors less than 0.15%). In [58], it presents a deep neural network (DNN) based condition monitoring and data evaluation system for highly automated laboratory test benches. The proposed approach aims at a fully automated implementation of predictive maintenance, test process automation, fault detection and downtime reduction of motor test stands.…”

“…As artificial neural networks are a promising technology, machine learning-based approaches in electrical drives have already been reported in numerous publications (see the recent overview preprint [30] with 259 references). Exemplary applications of ANNs in the field of electrical drive systems are: ANN-based speed, current or speed and current controllers [31][32][33][34][35][36][37][38], ANN-based parameter/system identification [39][40][41], ANN-based temperature or resistance estimation [42,43], ANN-based direct/predictive torque or model predictive control [44][45][46], ANN-based torque observers [47], ANN-based current waveform prediction [48], ANN-based encoderless control [49][50][51][52], ANN-based torque ripple reduction [53,54], ANN-based condition monitoring or fault detection [55][56][57][58], ANN-based optimal pulse patterns [59], and ANN-based multi-objective optimization for machine design [60].…”