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Mechanical vibration warning model based on optimized kernel bandwidth SVDD
Published:2023-11-27 author:LIU Xiaojin, CHEN Wenwu, WANG Qingfeng. Browse: 1009 Check PDF documents
Mechanical vibration warning model based on optimized kernel bandwidth SVDD


LIU Xiaojin1, CHEN Wenwu1, WANG Qingfeng2

(1.Equipment Safety Research Division, Sinopec Safety Engineering Research Institute Co. Ltd.,Qingdao 266000, China; 

2.Beijing Key Laboratory of Health Monitoring and Self-Recovery of High-End Machinery Equipment, Beijing University of 
Chemical Technology, Beijing 100029, China)


Abstract:  Support vector data description (SVDD) based on Gaussian kernel function was often used in the field of mechanical vibration fault warning field, because of its excellent anomaly detection performance, however its performance was limited by the appropriate value of kernel bandwidth. Therefore, aiming at the problems of conventional Gaussian kernel support vector data description (SVDD), such as the requirement of negative class data training model, complicated calculation, nonconvergence, and inapplicability to small value data, an optimal kernel bandwidth calculation method was proposed which could get rid of dependence on expert experience knowledge and negative class data to train SVDD hypersphere. A mechanical vibration fault early warning model based on optimizing SVDD kernel function bandwidth was constructed. Firstly, the influence of kernel bandwidth value on SVDD hypersphere was characterized by information entropy of spatial matrix complexity. Then, the particle swarm optimization algorithm (PSO) was used to find the value  of kernel function bandwidth parameter σ when the spatial matrix complexity was maximum, and the convergence of the objective function was realized fleetly. Considering the influence of penalty parameter on the description boundary of SVDD hypersphere, the penalty parameter was introduced to correct the optimization results, and the mechanical vibration fault warning model driven by historical normal operating state data was constructed. Finally, the practicability and reliability of the proposed method was verified by six public laboratory data and four engineering case data, and the proposed method was compared with the conventional SVDD kernel function bandwidth calculation method. The research results show that comparing with conventional methods,the incipient fault warning model trained by the optimized kernel function bandwidth calculation method has a 100% qualification rate, the hypersphere description boundary is well fitted, and there is no problem of non-convergence.

Key words: mechanical equipment fault warning; Gaussian kernel function; support vector data description(SVDD); kernel function bandwidth; penalty parameter; hypersphere; space matrix complexity; particle swarm optimization (PSO) algorithm
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