Mechanical equipment fault diagnosis based on wireless sensor network data fusion technology
Author:
Hao Fang1, Yang Qiuping1, Sharma Anjali2, Balyan Vipin3
Affiliation:
1. Xinxiang Vocational and Technical College , Xinxiang , Henan, 453006 , China 2. School of Biological and Environmental Sciences, Shoolini University of Biotechnology and Management Sciences , Solan 173229 , H.P , India 3. Department of Electrical, Electronics & Computer Engineering, Cape Peninsula University of Technology , Cape Town , South Africa
Abstract
Abstract
To save network energy consumption and prolong network life cycle in complex mechanical fault diagnosis, a research method of data fusion routing protocol algorithm based on wireless sensor network (WSN) is proposed. The specific content of the method is as follows: First, the low-energy adaptive clustering hierarchy algorithm is analyzed and discussed. On this basis, the prim route fusion algorithm is proposed to realize the effective utilization of energy and prolong the life of the network. Then, the WSN is abstracted as an undirected graph. From the perspective of saving the energy of the whole network, several current algorithms for building fusion trees are compared. The experimental results show that the prim algorithm consumes energy only after 700 rounds of clustering, while the leach clustering algorithm consumes energy only after 500 rounds. This shows that applying the prim algorithm can reduce the energy consumption of the whole network and prolong the life cycle of the network. However, the algorithm is carried out on the premise of uniform distribution of nodes, and there is a certain gap with the specific application of WSN in mechanical fault diagnosis. In the comparison of node energy consumption, it is found that compared with using the shortest path tree, using the central point of graph algorithm can greatly save the energy consumption of the node and has better performance. Practice has proved that this method can effectively remove redundant data information and solve the problem of unreliable data collected by a single sensor node. It is more suitable for the specific application of WSN in mechanical fault diagnosis.
Publisher
Walter de Gruyter GmbH
Subject
Behavioral Neuroscience,Artificial Intelligence,Cognitive Neuroscience,Developmental Neuroscience,Human-Computer Interaction
Reference23 articles.
1. K. Zhang, J. Chen, T. Zhang, S. He, and Z. Zhou, “Intelligent fault diagnosis of mechanical equipment under varying working condition via iterative matching network augmented with selective signal reuse strategy,” J. Manuf. Syst., vol. 57, pp. 400–415, 2020. 2. E. Casamenti, T. Yang, P. Vlugter, and Y. Bellouard, “Vibration monitoring based on optical sensing of mechanical nonlinearities in glass suspended waveguides,” Opt. Exp., vol. 29, no. 7, pp. 10853–10862, 2021. 3. Y. Liu, T. Bao, H. Sang, and Z. Wei, “A novel method for conflict data fusion using an improved belief divergence measure in dempster–shafer evidence theory,” Math. Probl. Eng., vol. 2021, no. 2, pp. 1–15, 2021. 4. M. S. B. Hossain, T. Rahman, N. Stojanovic, R. Rosales, T. Wettlin, S. Calabro, et al., “Transmission beyond 200 Gbit/s with IM/DD system for campus and intra-datacenter network applications,” IEEE Photonics Technol. Lett., vol. 33, no. 5, pp. 263–266, 2021. 5. P. L. Zou, P. Wang, and C. P. Yu, “Distributed fault detection for linear time-varying multi-agent systems with relative output information,” IEEE Access, vol. 9, pp. 42933–42946, 2021.
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