Fault diagnosis of pneumatic control valve based on Bat Optimization algorithm and BP neural network

• Published in: Journal of physics. Conference series Vol. 2897; no. 1; pp. 12056 - 12063
• Main Authors: Xu, Bing, Fei, Dongyi
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.11.2024
• Subjects: Algorithms; Back propagation networks; Control valves; Fault diagnosis; Optimization; Pneumatic control; Support vector machines
• ISSN: 1742-6588; 1742-6596; 1742-6596
• DOI: 10.1088/1742-6596/2897/1/012056

This article proposes a fault diagnosis model for pneumatic control valves based on a bat optimization (BA) algorithm and backpropagation (BP) neural network. The DAMADICS platform simulates the pneumatic industrial process control valve failures, and the fault signals are collected to construct the data set. A fault diagnosis model combining the Bat Optimization algorithm and Back Propagation neural network (BA-BP) is built, and four common faults are injected for experiments. The method is compared with Support Vector Machine (SVM), BP, and Bayesian optimized BP (BO-BP). Experimental results indicate that BA-BP has high diagnostic precision in the diagnosis of malfunctions in pneumatic valves.