Affiliations: State Key Laboratory of Mechanical System and
Vibration, Shanghai Jiao Tong University, Shanghai, China | School of Mechanical and Electronic Engineering,
Zhengzhou University of Light Industry, Zhengzhou, Henan, China
Note: [] Corresponding author: W. Du, State Key Laboratory of Mechanical
System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China.
Tel.: +86 21 3420 4350; Fax: +86 21 3420 6529; E-mail:
[email protected]
Abstract: Promptly and accurately dealing with the equipment breakdown is very
important in terms of enhancing reliability and decreasing downtime. A novel
fault diagnosis method PSO-RVM based on relevance vector machines (RVM) with
particle swarm optimization (PSO) algorithm for plunger pump in truck crane is
proposed. The particle swarm optimization algorithm is utilized to determine
the kernel width parameter of the kernel function in RVM, and the five
two-class RVMs with binary tree architecture are trained to recognize the
condition of mechanism. The proposed method is employed in the diagnosis of
plunger pump in truck crane. The six states, including normal state, bearing
inner race fault, bearing roller fault, plunger wear fault, thrust plate wear
fault, and swash plate wear fault, are used to test the classification
performance of the proposed PSO-RVM model, which compared with the classical
models, such as back-propagation artificial neural network (BP-ANN), ant colony
optimization artificial neural network (ANT-ANN), RVM, and support vectors,
machines with particle swarm optimization (PSO-SVM), respectively. The
experimental results show that the PSO-RVM is superior to the first three
classical models, and has a comparative performance to the PSO-SVM, the
corresponding diagnostic accuracy achieving as high as 99.17% and 99.58%,
respectively. But the number of relevance vectors is far fewer than that of
support vector, and the former is about 1/12–1/3 of the latter, which
indicates that the proposed PSO-RVM model is more suitable for applications
that require low complexity and real-time monitoring.
