Fault Features Extraction and Identification based Rolling Bearing Fault Diagnosis

Qin, Bo; Sun, Geng; Zhang, L. Y.; Wang, J. G.; Hu, Jinfei

doi:10.1088/1742-6596/842/1/012055

Cited by 7 publications

(6 citation statements)

References 12 publications

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“…In recent years, with the proposal and promotion of 'Made in China 2025' and 'Industry 4.0', the industrial robot industry has shown huge potential for development. As one of the core fault features [5], and it is difficult to effectively characterize the mapping relationship between fault features and equipment status in the face of a large amount of complex data. In recent years, with the rapid development of deep learning and artificial intelligence theory, research on neural networks and deep learning theory in the field of fault diagnosis has been widely carried out.…”

Section: Introductionmentioning

confidence: 99%

Network lightweight method based on knowledge distillation is applied to RV reducer fault diagnosis

Liu

Wang

et al. 2023

Meas. Sci. Technol.

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As a core component of industrial robots, the RV reducer directly affects the normal operation of the robot, so it is of great significance to monitor its status and diagnose faults. In the field of fault diagnosis, intelligent diagnosis methods based on deep learning have shown great advantages in accuracy and efficiency. However, as the network depth and scale increase, the exponentially growing model computation and parameter amounts require higher hardware requirements for computers, making it difficult to deploy on embedded platforms with limited computing resources. This makes it difficult for deep learning-based fault diagnosis methods to be applied in practical industrial settings that emphasize real-time performance, portability, and accuracy. This paper proposes a network lightweight method based on knowledge distillation. Using the two-dimensional time-frequency map of vibration signals as the model input, the improved MobileNet-V3 network is used as the teacher network, and the simplified CNN network is used as the student network. The method of knowledge distillation is applied to condense the knowledge and transfer it to the student network. The proposed method is validated using an RV reducer fault simulation experiment platform, and the results show that the proposed method reduces computation and parameter amounts by about 170 times at an accuracy rate of 94.37%, and run time is shortened by nearly one-third, and a generalization verification was conducted using the rotating mechanical fault simulation experiment platform. The models were also deployed on embedded devices to verify that the method proposed in this paper effectively reduces the deep learning network model's demand for hardware resources of the operating environment. This provides an effective reference for deploying and implementing deep learning-based fault diagnosis on embedded systems with lower hardware configurations.

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Section: Introductionmentioning

confidence: 99%

Network lightweight method based on knowledge distillation is applied to RV reducer fault diagnosis

Liu

Wang

et al. 2023

Meas. Sci. Technol.

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“…At this stage, the specific methods used for rolling bearing fault classification include SVM [18], extreme learning machine [19], BP neural network [20], etc. In small samples, SVM has strong generalization ability and relatively simple structure.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis based on Multiscale Permutation Entropy and SOA-SVM

Zhang¹,

Wang²,

Ren³

et al. 2022

Preprint

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The service conditions of underground coal mine equipment are poor, and it is difficult to accurately extract the fault characteristics of rolling bearings. In order to better improve the accuracy of fault identification of rolling bearings, a fault detection method based on multiscale permutation entropy and SOA-SVM is proposed. First, the whale optimization algorithm is used to select the modal analysis number K and the penalty factor α of the variational mode decomposition algorithm. Then, the vibration signal of rolling bearings is dissolved according to the optimized variational mode decomposition algorithm, and the multi-scale permutation entropy of the main intrinsic mode function is calculated. Finally, the feature values of the matrix are entered into the SVM algorithm optimized by the seagull optimization algorithm to obtain the classification result. The experimental results based on the published rolling bearing datasets of Western Reserve University show that the identification success rate of the proposed method can reach 98.75%. The fault detection of the rolling bearings can be completed accurately and efficiently.

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“…Compared with ELM, KELM only needs to select the kernel function and its related parameters to obtain the output weight, which has fewer adjustable parameters, better generalization performance, etc. (Qin et al 2017;Su et al 2021) applied it to the diagnosis of rolling bearings then gained better accuracy. On the other hand, kernel function makes the KELM be very sensitive to parameter settings.…”

Section: Introductionmentioning

confidence: 99%

Multi-fault diagnosis of rolling bearing using two-dimensional feature vector of WP-VMD and PSO-KELM algorithm

jiang

Li²,

Shen

2022

Soft Comput

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In order to achieve accurate fault diagnosis of rolling bearing under random noise, a new fault diagnosis method based on wavelet packet-variational mode decomposition (WP-VMD) and kernel extreme learning machine (KELM) optimized by particle swarm optimization (PSO) is proposed in this paper. Firstly, the time-frequency domain feature vectors of the original rolling bearing fault signals are effectively obtained by preprocessing of WMD and decomposition and reconstruction of VMD.Then, the extracted two-dimensional feature vector is input into the KELM neural network for fault identification, and combined with PSO, KELM parameters were optimized. The experimental results show that the proposed method can effectively diagnose the rolling bearing under random noise, with the features of fast speed, stable performance and high accuracy. By comparison, this paper obtains better accuracy and real-time performance with fewer features, which provides a simple and efficient solution for fault diagnosis of rolling bearings.

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Fault Features Extraction and Identification based Rolling Bearing Fault Diagnosis

Cited by 7 publications

References 12 publications

Network lightweight method based on knowledge distillation is applied to RV reducer fault diagnosis

Network lightweight method based on knowledge distillation is applied to RV reducer fault diagnosis

Rolling Bearing Fault Diagnosis based on Multiscale Permutation Entropy and SOA-SVM

Multi-fault diagnosis of rolling bearing using two-dimensional feature vector of WP-VMD and PSO-KELM algorithm

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