Unbalanced Fault Diagnosis Based on an Invariant Temporal-Spatial Attention Fusion Network

Liu, Jianhua; Yang, Haonan; He, Jing; Sheng, Zhenwen; Chen, Shou

doi:10.1155/2022/1875011

Cited by 4 publications

(3 citation statements)

References 50 publications

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“…To tackle the difficulty of severe class imbalance in faulty samples, ref. [27] proposed the invariant temporal-spatial attention fusion network (ITSA-FN) to evaluate the bearing health status with imbalance conditions. In order to rapidly predict the important performance indicators of mechanical devices, ref.…”

Section: Introductionmentioning

confidence: 99%

Graded Evaluation of Health Status of Hydraulic System with Variable Operating Conditions Based on Parameter Identification

Lin

Zhang

et al. 2023

Applied Sciences

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In industrial production, the effective and reliable performance of hydraulic systems is closely associated with product quality, personal safety, economic efficiency, etc. It is of utmost significance to perform the health status evaluation of systems. In this paper, a least-squares recursive parameter identification algorithm is proposed to realize the graded evaluation of the health status of the hydraulic system under variable operating conditions. First, a nonlinear model of the hydraulic system is established based on a mechanism analysis. Based on the system identifiable model obtained by parameter linearization, the least squares recursive algorithm is used to get the system parameters. Second, the system measurable data are graded and labeled under the same operating condition, and the variable parameter ranges under different health states are obtained by the parameter identification algorithm. Finally, under variable operating conditions, the estimates of variable parameters are compared with the range of health state parameters to complete the system health state graded evaluation. The feasibility of the proposed evaluation method is verified by MATLAB simulation software.

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Section: Introductionmentioning

confidence: 99%

Graded Evaluation of Health Status of Hydraulic System with Variable Operating Conditions Based on Parameter Identification

Lin

Zhang

et al. 2023

Applied Sciences

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“…ere is close cooperation between di erent parts and components of electromechanical equipment, forming an organic whole [1][2][3][4][5]. Generally, the operation environment of electromechanical equipment is complex, and it is under high-intensity working conditions for a long time [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Analysis and System Design of Mechanical Fault Diagnosis Based on Deep Neural Network

Zhao

2022

Mathematical Problems in Engineering

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The operating environment of mechanical equipment is complex, and it is in high-intensity working conditions for a long time. The condition monitoring and fault diagnosis of equipment are very important. As a kind of precision part commonly used in mechanical equipment, the healthy operation of the rolling bearing is a necessary condition to ensure the reliable operation of the whole equipment. This research takes rolling bearing as the research object and is devoted to mechanical fault diagnosis analysis and system design. This paper studies the structure and working principle of rolling bearing, analyzes the types and locations of rolling bearing faults, and puts forward the overall framework of the fault diagnosis system and the workflow of the fault diagnosis system. This paper also studies the relevant theory of deep learning, proposes a neural network model framework for rolling bearing fault diagnosis, and preprocesses the rolling bearing vibration data. It uses a dropout algorithm and GRU to reduce the model parameter size and reduce the risk of overfitting.

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“…Furthermore, the development of artificial intelligence technologies has increased their application in a variety of industries, such as mechanical fault diagnostics. Intelligent fault diagnosis has two main forms: machine learning combined with manual feature extraction [ 14 , 15 ] or deep learning with automated feature extraction [ 16 – 18 ]. Deep learning-based approaches have gained a lot of attention and popularity as a result of their ability to achieve good end-to-end fault diagnosis and automated fault feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis

Cai

Wang

Yao

et al. 2022

Computational Intelligence and Neuroscience

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Fault diagnosis of rotating machinery is an attractive yet challenging task. This paper presents a novel intelligent fault diagnosis scheme for rotating machinery based on ensemble dilated convolutional neural networks. The novel fault diagnosis framework employs a model training strategy based on early stopping optimization to ensemble several one-dimensional dilated convolutional neural networks (1D-DCNNs). By varying the dilation rate of the 1D-DCNN, different receptive fields can be obtained to extract different vibration signal features. The early stopping strategy is used as a model update threshold to prevent overfitting and save computational resources. Ensemble learning uses a weighted mechanism to combine the outputs of multiple 1D-DCNN subclassifiers with different dilation rates to obtain the final fault diagnosis. The proposed method outperforms existing state-of-the-art classical machine learning and deep learning methods in simulation studies and diagnostic experiments, demonstrating that it can thoroughly mine fault features in vibration signals. The classification results further show that the EDCNN model can effectively and accurately identify multiple faults and outperform existing fault detection techniques.

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Unbalanced Fault Diagnosis Based on an Invariant Temporal-Spatial Attention Fusion Network

Cited by 4 publications

References 50 publications

Graded Evaluation of Health Status of Hydraulic System with Variable Operating Conditions Based on Parameter Identification

Graded Evaluation of Health Status of Hydraulic System with Variable Operating Conditions Based on Parameter Identification

Analysis and System Design of Mechanical Fault Diagnosis Based on Deep Neural Network

Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis

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