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.
Highlights
With the rapid growth in modernization program and the constant expansion of resource exploitation, the truck cranes are facing a heavy demand on their services, and the maximum lifting weight has exceeded 1000 tons
The plunger pump constitutes the key part of truck crane, and the quality of the pump affects the performance of the hydraulic system, even the whole equipment, directly
A novel PSO-relevance vector machines (RVM) diagnostic method based on relevance vector machine with particle swarm optimization algorithm for plunger pump in truck crane is presented
Summary
With the rapid growth in modernization program and the constant expansion of resource exploitation, the truck cranes are facing a heavy demand on their services, and the maximum lifting weight has exceeded 1000 tons. W. Du et al / Fault diagnosis of plunger pump in truck crane based on RVM with PSO field. The number of support vectors increases linearly with the size of training sample set, which requires much higher computational complexity when classifying a very large data set Solving these problems is very critical for the real time monitoring and one line fault diagnosis. RVM classifier utilizes significantly fewer relevance vectors, while providing a similar classification performance as compared with SVM approach This feature makes the RVM classifier more suitable for the application that requires low complexity and real time diagnosis. The proposed PSO-RVM model is utilized to diagnosis the plunger pump in truck crane, in which PSO is used to determine the kernel width parameter.
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