Gear fault feature extraction and classification of singular value decomposition based on Hilbert empirical wavelet transform

Abstract

Abstract. Vibration signal of gearbox systems carries the important dynamic information for fault diagnosis. However, vibration signals always show non stationary behavior and overwhelmed by a large amount of noise make this task challenging in many cases. Thus, a new fault diagnosis method combining the Hilbert empirical wavelet transform (HEWT), the singular value decomposition (SVD) and Elman neural network is proposed in this paper. Vibration signals of normal gear, gear with tooth root crack, gear with chipped tooth in width, gear with chipped tooth in length, gear with missing tooth and gear with general surface wear are collected in different speed and load conditions. HEWT, a new self-adaptive time-frequency analysis, was applied to the vibration signals to obtain the instantaneous amplitude matrices. Singular value vectors, as the fault feature vectors were then acquired by applying the SVD. Last, the Elman neural network was used for automatic gearbox fault identification and classification. Through experimental results, it was concluded that the proposed method can accurately extract and classify the gear fault features under variable conditions. Moreover, the performance of the proposed HEWT-SVD method has an advantage over that of Hilbert-Huang transform (HHT)-SVD, local mean decomposition (LMD)-SVD or wavelet packet transform (WPT)-PCA for feature extraction.

Machine learning for fault analysis in rotating machinery: A comprehensive review

“…Although precision and recall metrics are not given, they can be evaluated through a confusion matrix. Chemseddine et al [ 42 ] used a neural network approach for gear FD of a gearbox. The gears in the gearbox contain CRC, CT, MT, and WT faults.…”

“…For this purpose, they deemed vibration [ 11 , 14 , 15 , 21 , 22 ], acoustic [ 11 , 23 , 24 ], thermal [ 13 ], current [ 6 , 7 , 9 , 25 , 26 ], pressure [ 27 ], and other characteristic data [ [27] , [28] , [29] ] as the main source for IFDP of rotating machines. Afterwards, the distinctive features are extracted by employing feature extraction methods such as statistical feature extraction [ [30] , [31] , [32] ], Fourier Transform [ [33] , [34] , [35] ], Wavelet Transform [ [36] , [37] , [38] ], Empirical Mode Decomposition [ 28 , 39 , 40 ] or other techniques [ 6 , 7 , 41 , 42 ]. The features may also be extracted automatically by employing deep learning approaches including convolutional neural networks, autoencoders, long-short term machines, etc.…”

Gearboxes fault detection under operation varying condition based on MODWPT, Ant colony optimization algorithm and Random Forest classifier

“…A nominal speed of 3600 r/min electric dc motor is considered as a source of motion between the two shafts and different resistive torques are generated by a magnetic power brake that is coupled to the output shaft. 17,18 The efficiency of the suggested method is tested using six pinions with different health states. The first one is a faultless pinion, and it is referred as good (G), while the rest have various types of defects, such as a tooth root crack (TRC), a chipped tooth in length (CTL), a chipped tooth in width (CTW), a missing tooth (MT), and general surface wear (GSW) (Figure 3).…”

“…The first one is a faultless pinion, and it is referred as good (G), while the rest have various types of defects, such as a tooth root crack (TRC), a chipped tooth in length (CTL), a chipped tooth in width (CTW), a missing tooth (MT), and general surface wear (GSW) (Figure 3). 17,18 Three pinions are installed simultaneously, on the input shaft of the gearbox. With a simple axial movement of the wheel of the output shaft, the engagement of each of them is achieved (Figure 4).…”

Hybrid Method with Parallel-Factor Theory, a Support Vector Machine, and Particle Filter Optimization for Intelligent Machinery Failure Identification

“…In the past century, many scholars and studies from around the world have focused on the special challenges associated with science and industrial technology regarding the development of original techniques for analyzing complex machinery, such as oil analysis, noise detection, vibration analysis, and non-destructive testing. Research regarding vibration-detection technology began before studies involving the other techniques; therefore, this technology is much more mature, and its application potential is the most extensive [8][9][10].…”