Fault Diagnosis of Plunger Pump in Truck Crane Based on Relevance Vector Machine with Particle Swarm Optimization Algorithm

Abstract

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 vector 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 vectors, 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.

Monitoring and maintenance of a gantry based on a wireless system for measurement and analysis of the vibration level

“…Due to the topic of the paper, it is particularly important to refer to works in which the vibrations generated by a working machine are the input signal being the basis for the diagnosis. In the work [6] the authors monitored the behavior of a plunger pump in a truck crane through the acquisition of its vibrations in three dimensions. The spectra of the machine operations in a correct state were known, along with its operations spectra in five incorrect diagnostic states.…”

“…The drives are connected by rollers with toothed gears which reduce the rotary speed of the roller in the main lifting mechanism. In addition, the figure presents the location of vibration sensors which are placed on the rollers (1,6), pinion bearings (2,5) and on the supporting beam. Sensors 3 and 4 are, in fact, auxiliary ones and their task is to monitor the vibration level of the background.…”

A multi-fault diagnosis method for piston pump in construction machinery based on information fusion and PSO-SVM

“…Lu et al [12] created an EEMD paving and optimized support vector regression method to detect faults and estimate the fault sizes of a piston pump based upon discharge pressure signals. On the other hand, vibration signal also reflects the performance of the piston pumps and the faults of piston pumps are generally accompanied by changes in vibration signal [14][15][16][17][18]. Jiang et al [14] found the fault vibration signals of hydraulic pump were often modulated by the impact and the fault features were concealed when it became fault.…”

Analytical investigation on geared rotor system with multi-body fault based on finite element method

“…With the development of artificial intelligence, intelligent fault diagnosis techniques have also been developed, such as artificial neural networks (Gaing, 2004; Wu et al., 2007), support vector machine (Chunling et al., 2009; Kim and Lee, 2009), and linear discriminant analysis (Sakai et al., 2008). The application of these diagnosis techniques has been published in many works, including bearings (Yang and Tavner 2009; El-Thalji and Jantunen, 2015; Zhang et al., 2015), pumps (Du et al., 2013; Ding et al., 2015), engines (Macián et al., 2003), etc. However, most of the researches have put forward the fault diagnosis of the gear system, but most of them have the fault form of a single part – the multi-part coupling fault has not been put forward.…”