Extraction and enhancement of unknown bearing fault feature in the strong noise under variable speed condition

Yang, Jianhua; Wu, Chensi; Shan, Zhen; Liu, Houguang; Yang, Changwei

doi:10.1088/1361-6501/ac0d78

Cited by 33 publications

(9 citation statements)

References 35 publications

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“…The primary function of bearings is to provide support for the main shaft and facilitate the transmission of torque within rotating machinery. One critical safety concern is to protect workers from machine breakdowns and accidents, especially in serious cases [1,2]. Therefore, it is imperative to implement early detection and diagnosis techniques for various faults of bearings in order to proactively prevent equipment failure.…”

Section: Introductionmentioning

confidence: 99%

An optimized Laplacian of Gaussian filter using improved sparrow search algorithm for bearing fault extraction

Feng,

Yang,

Wei

2023

Meas. Sci. Technol.

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Precise detection of fault characteristics in rolling bearings is imperative for machine health management. However, due to the presence of interfering components including noise and periodic components caused by vibration sources, the extraction of weak fault-related information cannot be achieved precisely. In this study, we propose an optimized Laplacian of Gaussian (LoG) filtering technique to handle this issue. The proposed algorithm utilizes the envelope entropy and Gini of square envelope as an objection function to optimize two important parameters, namely standard deviation and filter order of the LoG filter, through an improved sparrow search algorithm (SSA) named adaptive spiral flying SSA. Afterward, the LoG filtering method with the optimal parameters is employed to filter the raw vibration data. Finally, the filtered signal undergoes envelope analysis for fault feature detection. A simulated test and two case studies demonstrate the effectiveness and superiority of the LoG technique.

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Section: Introductionmentioning

confidence: 99%

An optimized Laplacian of Gaussian filter using improved sparrow search algorithm for bearing fault extraction

Feng,

Yang,

Wei

2023

Meas. Sci. Technol.

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“…A major reason is that the vibration feature of the WDD is submerged in noise induced by the track random irregularity. To enhance the fault feature, some method has been proposed and developed, such as frequency spectrum averaging [13], stochastic resonance [14], and double-window spectrum fusion enhancement [15]. Most fault feature enhancement methods focus on identifying and enhancing the fault characteristic frequency component.…”

Section: Introductionmentioning

confidence: 99%

Fault detection and quantitative assessment of wheel diameter difference based on ensemble adaptive tunable Q-factor wavelet transform and mixed kernel principal component analysis

Sui,

Wang,

Chen

2023

Meas. Sci. Technol.

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Tread wear is inevitable for railway vehicles. Because of the complicated railway condition, the wear rates of the two wheels of a wheelset are usually unequal, which leads to the wheel diameter difference (WDD). The WDD shortens the service life of the wheelset and deteriorates the dynamic performance of railway vehicles. Therefore, it has long been desired to monitor and quantify the WDD condition. However, influenced by the random irregularity, the effective vibration features induced by the WDD may be completely submerged in noise. In this paper, an adaptive TQWT decomposition method is proposed for denoise and feature enhancement. In this method, a new evaluation index is constructed and used to determine parameters. The influence of the WDD on the vibration of the wheelset can be more obvious after denoised by the adaptive TQWT decomposition. Mixed kernel principal component analysis is introduced into the condition monitoring and quantitative assessment of the WDD. By calculating the Hoteling’s T 2 and SPE statistics, control charts are employed to identify and assess the WDD conditions quantitatively in conjunction with the corresponding control limits. The effectiveness of the proposed method is validated by the simulation and experimental study. For the experimental datasets, the proposed method gives the average fault detection rate (FDR), false alarm rate (FAR), false alarm rate (PA) of 92.74%, 2.23%, 97.47% for the T 2 statistic and the average FDR, FAR, PA of 96.57%, 2.60%, 97.29% for the SPE statistic. The results show that the proposed method can effectively monitor the WDD conditions.

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“…Wang et al [ 15 ] proposed a feature extraction method based on the combination of variational mode extraction (VME) and multi-objective information fusion band-pass filter (MIFBF). Yang et al [ 16 ] used the fractional Fourier transform (FRFT) algorithm to extract fault features from the original signals and then used stochastic resonance (SR) to enhance the weak fault feature information to complete bearing fault diagnosis according to the fault feature frequency. Yan et al [ 17 ] performed VMD decomposition of bearing signals, and the calculated multi-scale envelope dispersion entropy (MEDE) of the IMF component was used as the feature to complete bearing fault pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM

Jin

Chen

Yang

2022

Entropy

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In order to further improve the accuracy of fault identification of rolling bearings, a fault diagnosis method based on the modified particle swarm optimization (MPSO) algorithm optimized least square support vector machine (LSSVM), combining parameter optimization variational mode decomposition (VMD) and multi-scale permutation entropy (MPE), was proposed. Firstly, to solve the problem of insufficient decomposition and mode mixing caused by the improper selection of mode component K and penalty factor α in VMD algorithm, the whale optimization algorithm (WOA) was used to optimize the penalty factor and mode component number in the VMD algorithm, and the optimal parameter combination (K, α) was obtained. Secondly, the optimal parameter combination (K, α) was used for the VMD of the rolling bearing vibration signal to obtain several intrinsic mode functions (IMFs). According to the Pearson correlation coefficient (PCC) criterion, the optimal IMF component was selected, and its optimal multi-scale permutation entropy was calculated to form the feature set. Finally, K-fold cross-validation was used to train the MPSO-LSSVM model, and the test set was input into the trained model for identification. The experimental results show that compared with PSO-SVM, LSSVM, and PSO-LSSVM, the MPSO-LSSVM fault diagnosis model has higher recognition accuracy. At the same time, compared with VMD-SE, VMD-MPE, and PSO-VMD-MPE, WOA-VMD-MPE can extract more accurate features.

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Extraction and enhancement of unknown bearing fault feature in the strong noise under variable speed condition

Cited by 33 publications

References 35 publications

An optimized Laplacian of Gaussian filter using improved sparrow search algorithm for bearing fault extraction

An optimized Laplacian of Gaussian filter using improved sparrow search algorithm for bearing fault extraction

Fault detection and quantitative assessment of wheel diameter difference based on ensemble adaptive tunable Q-factor wavelet transform and mixed kernel principal component analysis

Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM

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