An enhanced diagnosis method for weak fault features of bearing acoustic emission signal based on compressed sensing

Liu, Chang; Liao, Mengliang; Yang, Qi

doi:10.3934/mbe.2021086

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…From the perspective of signal processing, it enables the signal to decompose in different frequency bands, subdivides the frequency band into many levels and further subdivides the high-frequency portion of the band that is not subdivided by a wavelet analysis. According to the random time frequency resolution, the wavelet packet decomposes the signal into the corresponding frequency band components, which provides efficient and powerful results for the non-stationary description of dynamic signal, fault feature frequency, weak information extraction, and early fault diagnosis [ 26 ]. In this study, wavelet packet transform was used to decompose the compressed signal.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Acoustic Emission Signal Fault Diagnosis Based on Compressed Sensing for RV Reducer

Yang

Liu

et al. 2022

Sensors

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The rotate vector (RV) reducer has a complex structure and highly coupled internal components. Acoustic emission (AE) signal, which is more sensitive to a weak fault, is selected for fault diagnosis of the RV reducer. The high sampling frequency and big data are the challenges for AE signal store and analysis. This study combines compressed sensing (CS) and convolutional neural networks. As a result, data redundancy is significantly reduced while retaining most of the information, and the analysis efficiency is improved. Firstly, the time-domain AE signal was projected into the compression domain to obtain the compression signal; then, the wavelet packet decomposition in the compressed domain was performed to obtain the information of each frequency band. Next, the frequency band information was sent into the input layer of the multi-channel convolutional layer, and the energy pooling layer mines the energy characteristics of each frequency band. Finally, the softmax classifier was used to classify and predict different fault types of RV reducers. The self-fabricated RV reducer experimental platform was used to verify the proposed method. The experimental results show that the proposed method can effectively extract the fault features in the AE signal of the RV reducer, improve the efficiency of signal processing and analysis, and achieve the accurate classification of RV reducer faults.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Acoustic Emission Signal Fault Diagnosis Based on Compressed Sensing for RV Reducer

Yang

Liu

et al. 2022

Sensors

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“…For example, in Ref. [11], weak fault features are extracted based on wavelet packet decomposition and particle swarm optimization. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rotating Machinery Based on Two-Stage Compressed Sensing

You

Deng

et al. 2023

Machines

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Intelligent on-site fault diagnosis and professional vibration analysis are essential for the safety and stability of rotating machinery operation. This paper represents a fault diagnosis scheme based on two-stage compressed sensing for triaxial vibration data, which realizes fault diagnosis for rotating machinery based on compressed data and data reconstruction for professional vibration analysis. In the 1st stage, the triaxial vibration signals are compressed using a pre-designed hybrid measurement matrix; these compressed data can be used both for time-frequency transform and for vibration data reconstruction. In the 2nd stage, the frequency spectra of the triaxial vibration signals are fused and further compressed using another pre-designed joint measurement matrix, which inhibits the high-frequency noises simultaneously. Finally, the fused spectra are employed as feature vectors in sparse-representation-based classification, where the proposed batch matching pursuit (BMP) algorithm is utilized to calculate the sparse vectors. The two-stage compression scheme and the BMP algorithm minimize the computational cost of on-site fault diagnosis, which is suitable for edge computing platforms. Meanwhile, the compressed vibration data can be reconstructed, which provides evidence for professional vibration analysis. The method proposed in this study is validated by two practical case studies, in which the accuracies are 99.73% and 96.70%, respectively.

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“…It has been applied in numerous fields, involving the detection and structural integrity evaluation of pressure vessels, 2 pressure pipelines, 3 offshore oil platforms 4 and aerospace, 5 as well as the fault diagnosis and trend analysis of rolling bearings. 6 However, many existing SHM studies based on AE technology have mainly focused on single structures. It is rarely used in more complex structures, for example, multi-part coupled structures.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A fault source localization method for aircraft engine casing with dual-sensors based on acoustic emission

Liu,

Wang,

Jin

et al. 2023

Structural Health Monitoring

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Accurate estimation of the position of the fault source in the aircraft engine is the key to achieve engine structural health monitoring (SHM). In this paper, a convolutional neural network and graph convolutional network (CNN–GCN)-based dual-sensor acoustic emission (AE) localization method is proposed for locating the fault source in the engine casing with multi-part coupling features. Firstly, the time–frequency map data sets of AE signals at different locations are established by using continuous wavelet transform to analyze the effect of multi-part coupling features on AE signals. Secondly, combined with its reverberation mode, multi-modal and dispersion characteristics, the effectiveness of CNN–GCN model is trained, verified and tested. Finally, the sensitivity of the localization results to the sensor types is analyzed, and the sensor combination mode with high localization accuracy is obtained. These results show that the proposed method in this paper can be used as an effective means for locating the fault source of the engine casing with complex coupling interface features.

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An enhanced diagnosis method for weak fault features of bearing acoustic emission signal based on compressed sensing

Cited by 6 publications

References 24 publications

Acoustic Emission Signal Fault Diagnosis Based on Compressed Sensing for RV Reducer

Acoustic Emission Signal Fault Diagnosis Based on Compressed Sensing for RV Reducer

Fault Diagnosis of Rotating Machinery Based on Two-Stage Compressed Sensing

A fault source localization method for aircraft engine casing with dual-sensors based on acoustic emission

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