An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder

Shen, Changqing; Qi, Yumei; Wang, Jun; Cai, Guoyin; Zhu, Zhongkui

doi:10.1016/j.engappai.2018.09.010

Cited by 158 publications

(74 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, considering the noise in signals and non-linearity of signals, Jiang et al [28] utilized a stacked multilevel-DAE to extract more robust and discriminative fault features. Shen et al [29] proposed a stacked CAE for anti-noise and robust fault diagnosis. Martin et al [30] adopted a fully unsupervised deep VAE method for some latent fault feature extraction by variational inferences.…”

Section: Introductionmentioning

confidence: 99%

A Novel Autoencoder with Dynamic Feature Enhanced Factor for Fault Diagnosis of Wind Turbine

2020

View full text Add to dashboard Cite

Due to the complicated operating environment and variable operating conditions, wind turbines (WTs) are extremely prone to failure and the frequency of fault increases year by year. Therefore, the solutions of effective condition monitoring and fault diagnosis are urgently demanded. Since the vibration signals contain a lot of health condition information, the fault diagnosis based on vibration signals has received extensive attention and achieved impressive progress. However, in practice, the collected health condition signals are very similar and contain a lot of noise, which makes the fault diagnosis of WTs more challenging. In order to handle this problem, this paper proposes a model called denoising stacked feature enhanced autoencoder with dynamic feature enhanced factor (DSFEAE-DF). Firstly, a feature enhanced autoencoder (FEAE) is constructed through feature enhancement so that the discriminative features can be extracted. Secondly, a feature enhanced factor which is independent of manual judgments is proposed and embedded into the training process. Finally, the DSFEAE-DF, combining one noise adding scheme, stacked FEAEs and dynamic feature enhanced factor, is established. Through experimental comparisons, the superiorities of the proposed DSFEAE-DF are verified.

show abstract

Section: Introductionmentioning

confidence: 99%

A Novel Autoencoder with Dynamic Feature Enhanced Factor for Fault Diagnosis of Wind Turbine

2020

View full text Add to dashboard Cite

show abstract

“…At present, the methods used in gearbox fault extraction include Wavelet transform (WT), Empirical mode decomposition (EMD), Local mean decomposition (LMD), and Ensemble empirical mode decomposition (EEMD), these methods successfully extract the fault information, but they will show their own weaknesses when extracting composite faults [7][8][9][10]. When using WT to decompose signals, we need to give a basis function and the number of decomposing layers ahead of time, that is, WT is not adaptive [11][12][13][14][15]. The flaw of EMD is that its results show the phenomenon of mode aliasing and endpoint effects.…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm

Zhou

Guo

Wang

et al. 2019

Entropy

View full text Add to dashboard Cite

In recent years, a new method of fault diagnosis, named variational mode decomposition (VMD), has been widely used in industrial production, but the decomposition accuracy of VMD is determined by two parameters, which are respectively the decomposition layer number k and the penalty factor α, if the parameters are not properly selected, there will be over-decomposition or under-decomposition. In order to find an approach to determine the parameters adaptively, a method to optimize VMD by using the immune fruit fly optimization algorithm (IFOA) is proposed in this paper. In this method, permutation entropy is used as the fitness function, firstly, the immune fruit fly optimization algorithm is used to search the combined parameters of k and α in VMD, searching for the best combination parameters of k and α by iteration, and then uses the combined parameters to perform VMD, finally, the center frequency is determined through frequency spectrum analysis. The method mentioned is applied to the fault extraction of a simulated signal and a measured signal of a wind turbine gearbox, and the fault frequency is successfully extracted. Using ensemble empirical mode decomposition (EEMD) and singular spectrum decomposition (SSD) to compare with the proposed method, which validated feasibility of the proposed method.

show abstract

“…The operational status of the rolling element bearing often directly affects the performance of the whole machine. Consequently, the fault identification and diagnosis of rolling bearings are of great significance to ensure the safe and reliable operation of mechanical equipment [1][2][3]. Vibration signals caused by rolling bearing faults have been extensively studied and powerful diagnostic methods have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

Cui

Yang

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

Vibration analysis is one of the main effective ways for rolling bearing fault diagnosis, and achallenge is how to accurately separate the inner and outer race fault features from noisy compoundfaults signals. Therefore, a novel compound fault separation algorithm based on parallel dual-Qfactorsand improved maximum correlation kurtosis deconvolution (IMCKD) is proposed. First, thecompound fault signal is sparse-decomposed by the parallel dual-Q-factor, and the low-resonancecomponents of the signal (compound fault impact component and small amount of noise) are obtained,but it can only highlight the impact of compound faults, and failed to separate the inner and outerrace compound fault signal. Then, the MCKD is improved (IMCKD) by optimizing the selection ofparameters (the shift order M and the filter length L) based on the iterative calculation method withthe Teager envelope spectral kurtosis (TEK) index. Finally, after the composite fault signal is filteredand de-noised by the proposed method, the inner and outer race fault signals are obtained respectively.The fault characteristic frequency is consistent with the theoretical calculation value. The results showthat the proposed method can efficiently separate the mixed fault information and avoid the mutualinterference between the components of the compound fault.

show abstract

An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder

Cited by 158 publications

References 27 publications

A Novel Autoencoder with Dynamic Feature Enhanced Factor for Fault Diagnosis of Wind Turbine

A Novel Autoencoder with Dynamic Feature Enhanced Factor for Fault Diagnosis of Wind Turbine

Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm

Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

Contact Info

Product

Resources

About