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Strategies for Implementing Dynamic Mode Decomposition in Automotive Noise, Vibration, and Harshness Testing
Technical Paper
2021-01-5066
ISSN: 0148-7191, e-ISSN: 2688-3627
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Automotive Technical Papers
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English
Abstract
Implementing a new technique for regular industrial applications is an enormous challenge for researchers. Dynamic mode decomposition (DMD) is one such new technique that is underused by the industry. It is a data-driven technique developed for time-domain signal processing. In all automotive Noise, Vibration, and Harshness (NVH) studies, time-domain signals represent the measured parameter. Currently, Short-time Fourier Transform (STFT) is the only technique used for time-domain signal processing in NVH, but it presented several challenges in parameter selection. The type of window, size of the window, and sampling rate influence the output of STFT, and it uses an extensive amount of data for processing. Even a minor change in the signal affects the whole STFT results. But DMD requires only snapshot size and rank as inputs and uses a small quantity of data for processing. Irrespective of the noise content (Signal-to-Noise Ratio, SNR) present in the signal, DMD produced more consistent results. These features make the DMD most suitable for processing time-domain signal data. We found very few DMD applications in industrial problems because of insufficient literature about implementation techniques. In this paper, we have devised a strategy to implement DMD for NVH signal processing on a day-to-day basis. This study shows the DMD’s capability to extract key parameters when applied to various types of signals. We planned this study such that it covered many of the signal types that are encountered in NVH studies. As a first experiment, we varied the SNR of a pure-tone signal (deterministic stationary signal) from 100 dB to 10 dB to represent an experimental signal. Results of both DMD and STFT showed consistent values for varying SNR. In the second experiment using real-time signal, we varied the sample duration from 0.544 s to 0.023 s and observed that the accuracy of STFT results deteriorated as the sample size reduced, but DMD showed consistent results with less than 5% error. While DMD used a high sampling rate for better results, STFT used an extensive amount of data for the same accuracy levels. We also noticed that DMD can give 90% accurate results with a sample size of 0.2722 s duration for a signal with an SNR of 10 dB and 0.544 s duration for the signal with less than 10 dB of SNR, sufficient for 90% accurate results. We conclude that real-time, i.e., nondeterministic random, signal with a 48 kHz sampling size is sufficient for consistent results, and DMD can replace the STFT in day-to-day signal processing applications.
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Citation
Chinnasamy, L. and Ramachandran, K., "Strategies for Implementing Dynamic Mode Decomposition in Automotive Noise, Vibration, and Harshness Testing," SAE Technical Paper 2021-01-5066, 2021, https://doi.org/10.4271/2021-01-5066.Data Sets - Support Documents
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