Intelligent Model-based Integrity Assessment of Nonstationary Mechanical System

Authors

  • Hanxin Chen School of Mechanical and Electrical Engineering Wuhan Institute of Technology, Wuhan 430073, China; School of Artificial Intelligence, Nanchang Institute of Science and Technology, Nanchang, 330108, China
  • Yuzhuo Miao School of Mechanical and Electrical Engineering Wuhan Institute of Technology, Wuhan 430073, China
  • Yongting Chen Wuhan Britain-China School, Wuhan, China
  • Lu Fang School of Mechanical and Electrical Engineering Wuhan Institute of Technology, Wuhan 430073, China
  • Li Zeng School of Mechanical and Electrical Engineering Wuhan Institute of Technology, Wuhan 430073, China
  • Jun Shi School of Mechanical and Electrical Engineering Wuhan Institute of Technology, Wuhan 430073, China

DOI:

https://doi.org/10.13052/jwe1540-9589.2022

Keywords:

Integrity assessment, nonstationary mechanical system, model-based, improved particle filter

Abstract

The fault diagnosis model for nonstationary mechanical system is proposed in the condition monitoring. The algorithm with an improved particle filter and Back Propagation for intelligent fault identification is developed, which is used to reduce the noise of the experimental vibration signals to delete the negative effect of the noise on the feature extraction of the original vibration signal. The proposed integrated method is applied for the trouble shoot of the impellers inside the centrifugal pump. The principal component analysis (PCA) method optimizes the clean vibration signal to choose the optimal eigenvalue features.The constructed BP neural network is trained to get the condition models for fault identification. The proposed novel model is compared with the BP neural network based on traditional PF and particle swarm optimization particle filter (PSO-PF) algorithm. The BP neural network diagnosis method based on the improved PF algorithm is much better for the integrity assessment of the centrifugal pump impeller. This method is much significant for big data mining in the fault diagnosis method of the complex mechanical system.

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Author Biographies

Hanxin Chen, School of Mechanical and Electrical Engineering Wuhan Institute of Technology, Wuhan 430073, China; School of Artificial Intelligence, Nanchang Institute of Science and Technology, Nanchang, 330108, China

Hanxin Chen obtained PHD in School of Mechanical and Aerospace Engineering at Nanyang Technological University in Singapore from July, 2001 to February, 2006. He was post-doc research fellow in Department of mechanical engineering at University of Alberta in Canada from March, 2006 to March, 2008. He was research associate in School of Physics at University of Windsor in Canada in 2012 and research scientist in Department of Control at Sheffield of University from December, 2012 to May, 2015. He is distinguished professor as “Chutian Scholar” of Hubei Province at Wuhan Institute of Technology from April, 2008. His research area is about condition monitoring and fault diagnosis of engineering system, structural health monitoring (SHM) etc.

Yuzhuo Miao, School of Mechanical and Electrical Engineering Wuhan Institute of Technology, Wuhan 430073, China

Yuzhuo Miao is pursuing masters in School of Mechanical and Electrical Engineering at Wuhan Institute of Technology in China. His research interests are Structural Health Monitoring, fault diagnosis and nondestructive testing, etc.

Yongting Chen, Wuhan Britain-China School, Wuhan, China

Yongting Chen is student at Wuhan Britain-China School in China from Sept. 2019 to Jun. 2012. He joined the research project that is the National Natural Science Foundation of China (Grant No 51775390) as part-time research student in 2019.

Lu Fang, School of Mechanical and Electrical Engineering Wuhan Institute of Technology, Wuhan 430073, China

Lu Fang graduated from Yangtze Normal University Normal University in 2017 and obtained master degree in School of Mechanical and Electrical Engineering at Wuhan University of technology in China in 2000. His research interests are Structural Health Monitoring, fault diagnosis, and non-destructive testing etc.

Li Zeng, School of Mechanical and Electrical Engineering Wuhan Institute of Technology, Wuhan 430073, China

Li Zeng otained Ph.D in School of Materials physics and chemistry at Huazhong University of Science and Technology in China. She is Associate Professor in School of Mechanical and Electrical Engineering at Wuhan Institute of Technology in China. Her research interests are erosion-corrosion and protection of pipelines in oil and gas field.

Jun Shi, School of Mechanical and Electrical Engineering Wuhan Institute of Technology, Wuhan 430073, China

Jun Shi is lecture at Wuhan Institute of Technology from 2017. His research area is reliability of the pipeline, simulation analysis by FEM, Design for higher pressure machine.

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Published

2021-03-16

Issue

Section

Advanced Practice in Web Engineering