Bearing Fault Diagnosis Using Piecewise Aggregate Approximation and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Hu, Lei; Wang, Ligui; Chen, Yanlu; Hu, Niaoqing; Yu, Junsheng

doi:10.3390/s22176599

Cited by 22 publications

(8 citation statements)

References 39 publications

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“…If the sampling frequency is 25,600 Hz and the signal duration is 3.75 s. For the extraction of fault characteristics in variable-speed rolling bearings, the information contained in a signal of this duration is not enough. However, longer signals provide more reliable information [ 21 ], especially when the rolling bearing signals are of long duration under variable-speed conditions, direct processing of short signals is not conducive to observing the changes in fault frequencies of rolling bearings over time.…”

Section: Introductionmentioning

confidence: 99%

A Synchrosqueezed Transform Method Based on Fast Kurtogram and Demodulation and Piecewise Aggregate Approximation for Bearing Fault Diagnosis

Chen,

Hu,

et al. 2024

Sensors

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Synchrosqueezed transform (SST) is a time–frequency analysis method that can improve energy aggregation and reconstruct signals, which has been applied in the fields of medical treatment, fault diagnosis, and seismic wave processing. However, when dealing with time-varying signals, SST suffers from poor time–frequency resolution and is unable to deal with long signals. In order to accurately extract the characteristic frequency of variable speed rolling bearing faults, this paper proposes a synchrosqueezed transform method based on fast kurtogram and demodulation and piecewise aggregate approximation (PAA). The method firstly filters and demodulates the original signal using fast kurtogram and Hilbert transform to reduce the influence of background noise and improve the time–frequency resolution. Then, it compresses the signal by using piecewise aggregate approximation, so that the SST can deal with long signals and, thus, extract the fault characteristic frequency. The experimental data verification results indicate that the method can effectively identify the fault characteristic frequency of variable-speed rolling bearings.

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Section: Introductionmentioning

confidence: 99%

A Synchrosqueezed Transform Method Based on Fast Kurtogram and Demodulation and Piecewise Aggregate Approximation for Bearing Fault Diagnosis

Chen,

Hu,

et al. 2024

Sensors

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“…However, EMD has its limitations as it is sensitive to noise, leading to decomposition results prone to mode aliasing and distortion of the results in the presence of strong noise background. Hu et al [9] applied Piecewise Aggregate Approximation to the improved method of EMD known as complete ensemble EMD (EEMD), thereby enhancing the computational efficiency of the algorithm. Then, a fast and adaptive EMD method was proposed in [10], which can effectively extract the key feature information of fault signals and has strong practicability because of the low calculation cost.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis method for bogie bearings of high-speed trains based on weighted-sparse subspace clustering

Yang,

Liu,

Chen

et al. 2023

Meas. Sci. Technol.

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The bogie bearings are crucial components of the high-speed train transmission system, and any failure of them can severely impact the normal functioning of the vehicle. Nowadays, sparse subspace clustering (SSC) is an effective technology for diagnosing mechanical system faults. However, SSC is easily affected by noise. To address this issue, we introduce the weighted-SSC algorithm, which incorporates weighting coefficients to enhance the connections between similar sample points. Our approach involves extracting the fault characteristic parameters of the vibration signal through Wavelet Packet Transform (WPT) and Singular Value Decomposition (SVD). Subsequently, these parameters are employed to construct corresponding weighted-sparse subspaces. We also examined the selection of a hyperparameter in the weighted coefficients. The resultant sparse representation vectors are then harnessed for the purpose of transductive simi-supervised learning clustering and diagnosing the specific type of fault. To validate the effectiveness of our proposed method, we designed and built a bearing experimental platform. We compare our method with existing clustering algorithms, including K-means, SSC, Dimension Reduction Graph (DRG)-based SSC, and Ant Colony Optimization (ACO)-K-means clustering algorithms. The results demonstrate that our proposed weighted-SSC method achieves higher accuracy in fault diagnosis than the other clustering algorithms.

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“…The increasing studies on vibration detection methods in NDT for identifying the conditions of industrial equipment (e.g., applications in bearings) are attracting significant attention, owing to their optimized and controlled strategies. For example, scholars have studied the fault diagnosis methods of bearings based on vibration signals, including wavelet packet analysis [ 9 ], Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) [ 10 ], the Teager–Kaiser Energy Operator [ 11 ], support vector machines (SVMs) [ 12 , 13 ], neural networks (NNs) [ 14 , 15 ] and enhanced differential product weighted morphological filtering [ 16 ]. Given this, it was observed that when the HVCB fails, there is also an abnormal vibration signal which can be used to analyze the HVCB’s running state.…”

Section: Introductionmentioning

confidence: 99%

SF6 High-Voltage Circuit Breaker Contact Status Detection at Different Currents

Guo

Han

et al. 2022

Sensors

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Currently, the online non-destructive testing (NDT) methods to measure the contact states of high-voltage circuit breakers (HVCBs) with SF6 gas as a quenching medium are lacking. This paper aims to put forward a novel method to detect the contact state of an HVCB based on the vibrational signal. First, for a 40.5-kV SF6 HVCB prototype, a mechanical vibration detection system along with a high-current generator to provide the test current is designed. Given this, vibration test experiments are carried out, and the vibration signal data under various currents and corresponding contact states are obtained. Afterward, a feature extraction method based on the frequency is designed. The state of the HVCB contacts is then determined using optimized deep neural networks (DNNs) along with the method of adaptive moment estimation (Adam) on the obtained experimental data. Finally, the hyperparameters for the DNNs are tuned using the Bayesian optimization (BO) technique, and a global HVCB contact state recognition model at various currents is proposed. The obtained results clearly depict that the proposed recognition model can accurately identify five various contact states of HVCBs for the currents between 1000 A and 3500 A, and the recognition accuracy rate is above 96%. The designed experimental and theoretical analysis in our study will provide the references for future monitoring and diagnosis of faults in HVCBs.

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Bearing Fault Diagnosis Using Piecewise Aggregate Approximation and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Cited by 22 publications

References 39 publications

A Synchrosqueezed Transform Method Based on Fast Kurtogram and Demodulation and Piecewise Aggregate Approximation for Bearing Fault Diagnosis

A Synchrosqueezed Transform Method Based on Fast Kurtogram and Demodulation and Piecewise Aggregate Approximation for Bearing Fault Diagnosis

Fault diagnosis method for bogie bearings of high-speed trains based on weighted-sparse subspace clustering

SF6 High-Voltage Circuit Breaker Contact Status Detection at Different Currents

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