Abstract
Machine learning classification is a common method for vehicle noise and vibration (N&V) fault diagnosis which helps improve vehicle safety, comfort, and reduce maintenance cost. To improve the accuracy of classification, the model requires sufficient training data which is often expensive and time-consuming to acquire. A possible solution to resolve this limitation is to extract representative features from measurement signals and generate realistic simulated signals based on a smaller set of high-quality N&V measurement signals. A method to simulate N&V measurement signals of rotating machineries such as electrified powertrains is proposed. In its feature extraction process, tonal and broadband components of measurement data are separated. After the separation, time-varying tonal amplitudes, broadband spectra, and various related statistical features are extracted. In the simulation process, tonal and broadband signals were simulated using the extracted features with random variations added to each feature. In the current work N&V measurement signals of rotating machinery are simulated with relatively low speed fluctuation and results are presented.
Keywords
Machine learning, Data augmentation, Fault diagnosis, Electrified powertrains, Signal simulation
Subject
Acoustics and Noise Control
Date of this Version
5-16-2023
Comments
Joohyun Lee, Yangfan Liu, Patricia Davies and J. Stuart Bolton, “Simulating electrified powertrain noise and vibration measurement signals for machine learning based fault diagnosis applications,” paper NC_2023_0112 in Proceedings of NoiseCon 2023, 15-18 May, 2023, Grand Rapids, MI.