Fault Diagnosis of Synchronous Belt of Machine Tool Based on Improved Back Propagation Neural Network

Shi, Yanyan; Li, Zeqi; Tianxiang, Zhao; Yu, Xue-Lian; Yin, Chunmei; Bai, Yi-Shi

doi:10.1166/jno.2021.3161

Cited by 3 publications

(1 citation statement)

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“…In 2021, Yaochen Shi used wavelet decomposition to extract features of vibration acceleration, then combine improved back propagation neural network (BPNN) for fault diagnosis of machine tool synchronous belt. Compared to traditional BPNNs, it can achieve higher accuracy [30]. In 2022, Xiao used the wavelet decomposition and BPNN to process vibration acceleration and diagnosis fault of gearbox, and the accuracy can reach over 92% [31].…”

Section: Introductionmentioning

confidence: 99%

Thermal-vibration correlation study for high-temperature superconducting maglev intelligent monitoring based on back propagation neural network analysis

Pang,

Zheng,

Zhao

et al. 2024

Supercond. Sci. Technol.

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The internal temperature rise inside the high-temperature superconducting (HTS) superconductor resulting from irregular magnetic field (MF) above the permanent magnet guideway (PMG) is a major factor contributing to the decline of levitation performance. Real-time monitoring of the temperature rise inside YBCO superconductor is an important issue for the safe operation of the maglev train systems. However, the existing temperature rise testing method involves destructive intrusion less or more, easily affected by strong MF, occupying limited space and sensors prone to detachment. This paper innovatively proposes a non-contact internal temperature rise testing method combining artificial intelligence (AI) methods. Vibration is common signal of a maglev train system, which inspires to establish a fundamental thermal-dynamic levitation force synchronous testing device for YBCO superconductor. Then, a set of temperature rise-vibration dataset exposed to different alternating MF frequencies is created. The wavelet transform is chosen to extract the frequency band energy of vibration, and the backpropagation neural network is used to identify the corresponding temperature rise. The recognition accuracy can reach over 99.9%, which firstly proves the effectiveness of AI algorithms in the thermal-vibration correlation analysis for the HTS maglev system. The results can provide the foundation reference for the intelligent monitoring and fault diagnosis of thermal-dynamics stabilities of HTS maglev train in the future.

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Section: Introductionmentioning

confidence: 99%

Thermal-vibration correlation study for high-temperature superconducting maglev intelligent monitoring based on back propagation neural network analysis

Pang,

Zheng,

Zhao

et al. 2024

Supercond. Sci. Technol.

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Fault Diagnosis of Support Vector Machine Analog Circuits Based on Improved Particle Swarm Optimization

Yang,

Song

2023

Journal of Nanoelectronics and Optoelectronics

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The development of electronic circuits requires that the reliability and security of circuit equipment and system operation are also increasing. In addition, due to the complexity of the operating environment, it is very important to strengthen the fault diagnosis and real-time testing technology of analog circuits in circuit systems. Based on this, this paper studied the fault diagnosis of analog circuits with Support Vector Machine (SVM), and introduced Improved Particle Swarm Optimization (IPSO) algorithm to optimize the parameters of SVM. In other words, the dynamic weight setting and factor improvement of Particle Swarm Optimization (PSO) algorithm aim to accelerate algorithm performance improvement, and information extraction and diagnosis model construction are carried out on the basis of considering circuit fault characteristics. Through the performance test and application analysis of the improved algorithm proposed in the study, the error value of the improved algorithm was basically stable at 0.0103 in the late stage of classification training, and its prediction accuracy rate was more than 80%, and the classification consumption time was less. At the same time, the accuracy of fault feature extraction results in training and test scenarios was above 94%, and the search performance was obviously better than other comparison algorithms, which effectively improved the fault diagnosis accuracy and efficiency. The IPSO algorithm model can effectively identify analog circuit fault information, and shows good information optimization performance. It has certain validity and rationality in circuit fault diagnosis and security assurance.

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Research on Measurement of Tooth Profile Parameters of Synchronous Belt Based on Point Cloud Data

Zhang

Pang

Teng

2022

Sensors

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Accurately detecting the tooth profile parameters of the synchronous belt is crucial for the transmission’s load distribution and service life. However, the existing detection methods have low efficiency, are greatly affected by the manual experience, and cannot realize automatic detection. A measurement method based on point cloud data is proposed to solve this issue. The surface space points of the synchronous belt are acquired by a line-structured light sensor, and the raw point clouds are preprocessed to remove outliers and reduce the number of points. Then, the point clouds are divided into plane and arc regions, and different methods are used for fitting. Finally, the parameters of each tooth are calculated. The experimental results show that the method has high measurement accuracy and reliable stability and can replace the original detection method to realize automatic detection.

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Fault Diagnosis of Synchronous Belt of Machine Tool Based on Improved Back Propagation Neural Network

Cited by 3 publications

References 0 publications

Thermal-vibration correlation study for high-temperature superconducting maglev intelligent monitoring based on back propagation neural network analysis

Thermal-vibration correlation study for high-temperature superconducting maglev intelligent monitoring based on back propagation neural network analysis

Fault Diagnosis of Support Vector Machine Analog Circuits Based on Improved Particle Swarm Optimization

Research on Measurement of Tooth Profile Parameters of Synchronous Belt Based on Point Cloud Data

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