A sparse multivariate time series model-based fault detection method for gearboxes under variable speed condition. (15th March 2022)

• Record Type: Journal Article
• Authors: Chen, Yuejian; Zuo, Ming J.
• Is Part Of: Mechanical systems and signal processing. Volume 167:Part A(2022)
• Publication Date: 2022-03-15
• Subjects: Sparse LPV-VAR model -- Gearbox -- Fault detection -- Multichannel non-stationary signals; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108539
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Fault detection of gearboxes under variable speed condition is considered. A sparse LPV-VAR model is proposed for baseline multichannel signals modeling. The sparse LPV-VAR model-based fault detection scheme is presented. The detection performance is validated using simulation and experimental signals. Abstract: Gearboxes often operate under variable speed condition which makes the collected vibration signal, a widely employed type of condition monitoring data, becomes non-stationary. This paper proposes a sparse linear parameter varying vector auto-regression (LPV-VAR) model-based method for fault detection of gearboxes under variable speed condition. The proposed sparse LPV-VAR model is a multivariate time-variant time series model that can represent multichannel non-stationary baseline vibration signals from a gearbox. Fault detection is based on the residuals of the sparse LPV-VAR model. The proposed sparse LPV-VAR model inherits the strengths of the sparse time series modeling and utilization of multichannel vibration signals, where the former has shown to have higher modeling accuracy than conventional non-sparse time series models, and the latter enables the removal of the correlated random noise between channels. Both simulation and experimental studies have been conducted to validate the fault detection performance of the proposed method. Results have shown that the sparse LPV-VAR model has higher modeling accuracy than the reported sparse single-variate LPV-AR and conventional non-sparse LPV-VAR models. Subsequently, the sparse LPV-VAR model-based fault detection method achieves a higher fault detection rate than using the other two models.