A Bearing Fault Diagnosis Method Based on Feature Selection Feedback Network and Improved D-S Evidence Fusion

Tang, Xianghong; Gu, Xiaoxiong; Wang, Jiachen; He, Qiang; Zhang, Fan; Lu, Jianguang

doi:10.1109/access.2020.2968519

Cited by 24 publications

(8 citation statements)

References 28 publications

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“…It evaluated corresponding fluctuations detrended by locally fitting vibration signals using an improved detrended wave analysis and selected wave function polynomial fitting coefficients as fault features. Tang et al proposed an improved DS evidence theory fusion method based on Cohen's kappa coefficient to solve the bearing fault diagnosis problem [20]. Soualhi et al proposed an adaptive neuro-fuzzy inference system to study bearing multisource information fusion [21].…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion

Zhang

2021

Symmetry

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The multisource information fusion technique is currently one of the common methods for rolling bearing fault diagnosis. However, the current research rarely fuses information from the data of different sensors. At the same time, the dispersion itself in the VAE method has asymmetric characteristics, which can enhance the robustness of the system. Therefore, in this paper, the information fusion method of the variational autoencoder (VAE) and random forest (RF) methods are targeted for subsequent lifetime evolution analysis. This fusion method achieves, for the first time, the simultaneous monitoring of acceleration signals, weak magnetic signals and temperature signals of rolling bearings, thus improving the fault diagnosis capability and laying the foundation for subsequent life evolution analysis and the study of the fault–slip correlation. Drawing on the experimental procedure of the CWRU’s rolling bearing dataset, the proposed VAERF technique was evaluated by conducting inner ring fault diagnosis experiments on the experimental platform of the self-research project. The proposed method exhibits the best performance compared to other point-to-point algorithms, achieving a classification rate of 98.19%. The comparison results further demonstrate that the deep learning fusion of weak magnetic and vibration signals can improve the fault diagnosis of rolling bearings.

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Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion

Zhang

2021

Symmetry

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“…Extraction and selection of the measured data play a significant role in the performance of fault detection. Various methods are used to select and extract the most appropriate features from the measured signal for fault detection [5,[16][17][18][19][20]. Overall, the feature selection method can be categorized into wrapper methods, filter methods, and hybrid methods [17].…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Fault Detection in ESP Systems Based on Vibration Measurements

Mohammad‐Alikhani

Vahedi

Khlyupin

2021

Electromechanics and Robotics

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Condition monitoring of electrical submersible pumps (ESPs) is vital since they work in a harsh and confined environment. One of the most important parts of ESP systems is the electric motor which is conventionally the induction motor. Therefore, fast and accurate diagnosis of the motor in the ESP system can cut overhaul expenses and prevent further damages and costs. In ESPs, the vibration of the system is usually measured using the downhole sensors. Accordingly, this paper aims to propose a method for bearing fault detection in the induction motor based on vibration measurements. In this method, first, the best features of the vibration signals are selected using a wrapper approach based on the genetic algorithm. Then, the model is trained using the selected features based on k nearest neighbor classifier. The proposed method is evaluated using data of an experimental set from Case Western Reserve University (CWRU) Bearing Data Center.

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“…Han et al believe that in-depth learning has become a new research direction in the field of intelligent monitoring and fault diagnosis of industrial equipment [ 9 ]. In order to improve the diagnosis accuracy of the mechanical equipment fault diagnosis model, Tang and other scholars proposed a fault diagnosis model of mechanical equipment with feature selection feedback network [ 10 ]. Wang and other scholars proposed a fault diagnosis model of mechanical equipment based on noise assisted signal enhancement and stochastic resonance and optimized the parameters by particle swarm optimization.…”

Section: Introductionmentioning

confidence: 99%

Health Evaluation and Fault Diagnosis of Medical Imaging Equipment Based on Neural Network Algorithm

Zhao

Jiang

Gao

2021

Computational Intelligence and Neuroscience

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In recent years, high-precision medical equipment, especially large-scale medical imaging equipment, is usually composed of circuit, water, light, and other structures. Its structure is cumbersome and complex, so it is difficult to detect and diagnose the health status of medical imaging equipment. Based on the vibration signal of mechanical equipment, a PLSR-DNN hybrid network model for health prediction of medical equipment is proposed by using partial least squares regression (PLSR) algorithm and deep neural networks (DNNs). At the same time, in the diagnosis of medical imaging equipment fault, the paper proposes to use rough set to screen the fault factors and then use BP neural network to classify and identify the fault and analyzes the practical application effect of the two technologies. The results show that the PLSR-DNN hybrid network model for health prediction of medical imaging equipment is basically consistent with the actual health value of medical equipment; medical imaging equipment fault diagnosis technology is based on rough set and BP neural network. In the test set, the sensitivity, specificity, and accuracy of medical imaging equipment fault identification are 75.0%, 83.3%, and 85.0%. The above results show that the proposed health prediction method and fault diagnosis method of medical imaging equipment have good performance in health prediction and fault diagnosis of medical equipment.

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A Bearing Fault Diagnosis Method Based on Feature Selection Feedback Network and Improved D-S Evidence Fusion

Cited by 24 publications

References 28 publications

Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion

Rolling Bearing Fault Diagnosis Based on Deep Learning and Autoencoder Information Fusion

Induction Motor Fault Detection in ESP Systems Based on Vibration Measurements

Health Evaluation and Fault Diagnosis of Medical Imaging Equipment Based on Neural Network Algorithm

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