New transfer learning fault diagnosis method of rolling bearing based on ADC-CNN and LATL under variable conditions

Huo, Chunran; Jiang, Quan; Shen, Yehu; Qian, Chenhui; Zhang, Qingkui

doi:10.1016/j.measurement.2021.110587

Cited by 43 publications

(21 citation statements)

References 31 publications

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“…Different protection states of bogie traction motor bearings in transit have different protection effects directly affecting the stability and safety of later urban rail vehicle transportation, so the vibration signals of bogie traction motor bearings in different protection states during highway transportation are monitored and evaluated [1]. The bogies of rail vehicles in the road transportation condition, the transport vehicles can't provide matching power supply and other hardware conditions to the bogie traction motor bearing data acquisition system [2,3]. At the same time, due to the long transportation distance, multi-channel acquisition, and other factors, the data acquisition system data transmission and storage bring great challenges.…”

Section: Introductionmentioning

confidence: 99%

Compressed Sensing of Traction Motor Bearing Vibration Signals based on Discrete Fourier Transform and Generalized Orthogonal Matching Pursuit Algorithm

Liu

Shen³

et al. 2023

J. Phys.: Conf. Ser.

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For the bogie traction motor bearings of rail vehicles under road transportation conditions, its vibration signal full-time domain data acquisition data volume, storage pressure, subsequent data analysis difficulties, and other problems. This study proposes a data compression acquisition and data reconstruction method for bearing vibration signals based on compressed sensing (CS), which can achieve compressed sampling, transmission, and storage of the original signal with fewer observations, and restore the original signal by combining reconstruction algorithms to achieve low error reconstruction. We study the effects of different sparse transform methods on signal sparsity, as well as the signal sparse performance based on different sparse transforms and the signal reconstruction effect of different reconstruction algorithms, and propose a sparse representation method based on the discrete Fourier transform (DFT) and the generalized orthogonal matching pursuit (gOMP) algorithm for CS. Through simulation and experimental comparison, it is concluded that the method can restore and reconstruct the original high-dimensional signal with small reconstruction error, and solve the problems of massive data, inconvenient transmission, and difficult data analysis in engineering.

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Section: Introductionmentioning

confidence: 99%

Compressed Sensing of Traction Motor Bearing Vibration Signals based on Discrete Fourier Transform and Generalized Orthogonal Matching Pursuit Algorithm

Liu

Shen³

et al. 2023

J. Phys.: Conf. Ser.

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“…The models studied by the above scholars all use the one-dimensional CNN network model, but CNN has more advantages in processing two-dimensional data, and it is easier to extract feature information from images [24,25]. Therefore, researchers began to convert the authentic sign into a picture for fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Diesel Engine Fault Diagnosis Method Based on Optimized VMD and Improved CNN

et al. 2022

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The safe operation of diesel engines performs a vital function in industrial production and life. Because diesel engines often work in harsh environmental conditions, they are prone to failure. Therefore, this paper proposes a fault analysis method based on a combination of optimized variational mode decomposition (VMD) and improved convolutional neural networks (CNN) to address the necessary need for preventive maintenance of diesel engines. The authentic vibration sign is first decomposed by using the (VMD) algorithm, then the greatest range of decomposition layers is decided by using scattering entropy and the useful components are preferentially chosen for reconstruction. The continuous wavelet transform (CWT) records preprocessing method is then delivered to radically change the noise-reduced vibration sign into a time-frequency map, which is fed into the CNN for model coaching and extraction of fault features. Finally, fault classification is realized by support vector machine (SVM) with excellent classification performance. Through preset fault experiments on diesel engines, it is established that the technique proposed in this paper can successfully identify fault states, and the classification accuracy is higher than alternative methods.

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“…Rolling bearings play an indispensable role in equipment, which are prone to breakdown since they often operates with awful conditions, such as high temperature, heavy loads, high rotating speed, and etc., and almost 45%-55% of rotating machinery failures are rolling bearing faults. [1][2][3] Unexpected failures may boost the cost of operation, maintenance and even lead to catastrophic casualties. 4 To ensure the safety and reliability of the rotating machinery, accurate and efficient diagnosis of incipient faults is extremely important.…”

Section: Introductionmentioning

confidence: 99%

“…Conventionally, the fault diagnostic techniques collect and process various signals with the goals of resuming from malfunctions or faults and precluding from future failures as early as possible. 2,5 Data-driven fault recognition approaches related to artificial intelligence techniques or machine learning techniques, such as support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural network (ANN), etc., have been extensively studied to improve existing techniques with the goal of more accurately and effectively dealing with various complex problems, such as varying load effect and noise contamination. [6][7][8] Additionally, deep learning methods have been widely used for condition recognition over the past decades.…”

Section: Introductionmentioning

confidence: 99%

Cross-domain intelligent fault diagnosis of rolling bearing based on distance metric transfer learning

Zhou

Huang

et al. 2022

Advances in Mechanical Engineering

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Rolling bearings are present ubiquitously in mechanical equipment, timely fault diagnosis has great significance in guaranteeing the safety of mechanical operation. In real world industrial applications, the distribution of training dataset (source domain) and testing dataset (target domain) is often different and varies with operating environment, which may lead to performance degradation. In this study, a cross-domain fault diagnosis of rolling bearing method based on distance metric transfer learning (DMTL) and wavelet packet decomposition (WPD) is proposed. The Mahalanobis distance is adopted for learning the intrinsic similarity or dissimilarity between instances and learned by simultaneously maximizing the intra-class distances and minimizing the inter-class distances for target domain. The features of source domain and target domain are first extracted from original vibration signals by WPD which is a powerful tool in dealing with non-stationary signals and can provide meticulous analysis. Then, the DMTL model is adopted to eliminate the error propagation across different components, which can weaken the weight of low-quality instances and enhance the weight of high-quality samples. Finally, the k-nearest neighbor (KNN) classifier is applied to accomplish the cross-domain intelligent fault-type classification. The superiority and effectiveness of the proposed fault diagnosis model is validated by two diagnosis cases. The experimental results demonstrated that the proposed method performs better than other compared methods in recognizing various fault types and has the capability in handling the complex cross-domain adaptation scenarios.

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New transfer learning fault diagnosis method of rolling bearing based on ADC-CNN and LATL under variable conditions

Cited by 43 publications

References 31 publications

Compressed Sensing of Traction Motor Bearing Vibration Signals based on Discrete Fourier Transform and Generalized Orthogonal Matching Pursuit Algorithm

Compressed Sensing of Traction Motor Bearing Vibration Signals based on Discrete Fourier Transform and Generalized Orthogonal Matching Pursuit Algorithm

Diesel Engine Fault Diagnosis Method Based on Optimized VMD and Improved CNN

Cross-domain intelligent fault diagnosis of rolling bearing based on distance metric transfer learning

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