A multi-sensor fault detection strategy for axial piston pump using the Walsh transform method. (April 2018)

• Record Type: Journal Article
• Authors: Gao, Qiang; Tang, He-Sheng; Xiang, Jia-Wei; Zhong, Yongteng
• Is Part Of: International journal of distributed sensor networks. Volume 14:Number 4(2018)
• Publication Date: 2018-04
• Subjects: Axial piston pump -- fault diagnosis -- multi-sensor signals -- noise suppression -- Walsh transform; Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1177/1550147718772531
• Languages: English
• ISSNs: 1550-1329

Abstract

The axial piston pump is a key component of the industrial hydraulic system, and the failure of pump can result in costly downtime. Efficient fault detection is very important for improving reliability and performance of axial piston pumps. Most existing diagnosis methods only use one kind of the discharge pressure, vibration, or acoustic signal. However, the hydraulic pump is a typical mechanism–hydraulics coupling system, all of the pressure, vibration, and acoustic signals contain useful information. Therefore, a novel multi-sensor fault detection strategy is developed to realize more effective diagnosis of axial piston pump. The presence of periodical impulses in these signals usually indicates the occurrence of faults in pump. Unfortunately, in the working condition, detecting the faults is a difficult job because they are rather weak and often interfered by heavy noise. Therefore, noise suppression is one of the most important procedures to detect the faults. In this article, a new denoising method based on the Walsh transform is proposed, and the innovation is that we use the median absolute deviation to estimate the noise threshold adaptively. Numerical simulations and experimental multi-sensor data collected from normal and faulty pumps are used to illustrate the feasibility of the proposed approach.