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A multi-sensor fault detection strategy for axial piston pump using the Walsh transform method

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  • Qiang Gao
  • He-Sheng Tang
  • Jia-Wei Xiang
  • Yongteng Zhong

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.

Suggested Citation

  • Qiang Gao & He-Sheng Tang & Jia-Wei Xiang & Yongteng Zhong, 2018. "A multi-sensor fault detection strategy for axial piston pump using the Walsh transform method," International Journal of Distributed Sensor Networks, , vol. 14(4), pages 15501477187, April.
    • Handle: RePEc:sae:intdis:v:14:y:2018:i:4:p:1550147718772531
    DOI: 10.1177/1550147718772531
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    References listed on IDEAS

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    1. Wang, Yung-Hung & Yeh, Chien-Hung & Young, Hsu-Wen Vincent & Hu, Kun & Lo, Men-Tzung, 2014. "On the computational complexity of the empirical mode decomposition algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 159-167.
    2. Li-Ching Wu & Hsin-Hao Chen & Jorng-Tzong Horng & Chen Lin & Norden E Huang & Yu-Che Cheng & Kuang-Fu Cheng, 2010. "A Novel Preprocessing Method Using Hilbert Huang Transform for MALDI-TOF and SELDI-TOF Mass Spectrometry Data," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-15, August.
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    1. Liu, Yun & Heidari, Ali Asghar & Ye, Xiaojia & Liang, Guoxi & Chen, Huiling & He, Caitou, 2021. "Boosting slime mould algorithm for parameter identification of photovoltaic models," Energy, Elsevier, vol. 234(C).

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