The development of new electric traction machines is a time-consuming process as it involves intensive testing on motor test benches. Machine-Learning-empowered monitoring offers the opportunity to anticipate costly failures early and hence reducing development time. However, machine learning (ML) for process monitoring requires large amounts of training data, especially as the targeted fault states are scarce and yet diverse in their appearances.
Therefore, we propose to use synthetic time series data to leverage the high cost of acquiring training data from experiments in real test benches. In this article, we present a novel scheme to generate synthetic data based on a sub-dimensional time series representation. We introduce a highly flexible model by mapping the data to a latent representation and approximating the latent data distribution by a Gaussian Mixture Model. In addition, we propose the Frechet InceptionTime Distance (FITD) as a new distance measure to evaluate the generated data. It allows extracting characteristics at different scales by using multiple kernel sizes. In this way, we ensure that the synthesized data contains characteristics similar to those present in the real data. In our experiment, we train two types of fault detectors, one based on real data of a motor test bench and the other based on synthetic data. We also consider employing fault-aware conditional architectures to generate training data for different fault types explicitly. Our final results show that using synthesized data in the training process increases the performance in terms of classification accuracy score (CAS) up to 29%.
