Gearbox Incipient Fault Detection Based on Deep Recursive Dynamic Principal Component Analysis

Abstract

As a part of the energy transmission chain, gearboxes are considered as important components in rotating machines, and the gearbox failure results in costly economic losses. Therefore, it is necessary to detect the appearance of incipient gearbox faults by implementing an appropriate detected model. The incipient failure characteristics of the gearbox are weak and hidden in a set of time-varying series signals the vibration signals, which is difficult to effectively extract under the background of strong noise. The PCA method is not effective in detecting weak fault features in time-varying signals, so this paper proposes a method based on Deep Recursive Dynamic Principal Component Analysis (Deep RDPCA) to detect incipient faults in gearboxes. The proposed approach is modeled via both the deep decomposed theorems and time-varying dynamic model based on traditional PCA to extract characteristic of time-varying and weak fault information under the background of strong noise. The proposed method could get a better real-time reflection for changed system by introducing ''Moving Window'' technologies, so that the incipient fault of gearbox could be detected accurately, too. Finally, the effect of Deep RDPCA-based fault diagnosis is compared with the results of PCA, DPCA, RDPCA, Deep PCA, and Deep DPCA methods. It is concluded that the proposed method can effectively capture the time-varying relationship of process variables and accurately extract the weak fault characteristics in the vibration signal, which effectively improves the fault detection performance. INDEX TERMS Gearbox, fault diagnosis, gear failure experiment, feature extraction, Deep RDPCA.

Diagnostics and prognostics for complex systems: A review of methods and challenges

“…Yu et al 196 employed dynamic ICA (DICA) combined with auto-associative kernel regression for fault detection to address the nonlinearity and multimodality in complex industrial systems. Shi et al 198 introduced an incipient fault detection method in gearbox systems using Deep Recursive Dynamic Principal Component Analysis, in order to detect faults in time-varying, noisy vibration signals. Ahmad et al 199 introduced an RUL estimation method for bearing based on the dynamic regression models.…”

Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review

“…Experiments show that under the condition of Gaussian and non-Gaussian interference and the resonance frequencies of multiple faults are easily coupled with each other, the LEASgram method has significant fault identification and separation capabilities. In recent years, there are many new feature strategies partially based AI technologies designed for gearbox such as multiclass relevance vector machine (mRVM) [130], multiview sparse (MVSF) [8], deep recursive dynamic principal component analysis (Deep RDPCA) [131], Resonance Residual Technique [132] and deep learning based methods [133], [134].…”

Locomotive Gear Fault Diagnosis Based on Wavelet Bispectrum of Motor Current

“…erefore, x 11 (t) is a signal where quadratic phase coupling is absent, except over a relatively short-time interval (blocks [13][14][15][16].…”

“…us, the diagnosis of gear faults has significant importance for both safety and reliability [4][5][6][7][8][9]. Vibration signal processing is widely used to detect gear faults, but its application is limited by transducer installation [10][11][12][13][14][15]. As the acceleration sensor does not have enough installation space or brings additional maintenance costs, the vehicle becomes more complicated [16].…”