A Walsh transform-based Teager energy operator demodulation method to detect faults in axial piston pumps. (February 2019)

• Record Type: Journal Article
• Authors: Gao, Qiang; Tang, Hesheng; Xiang, Jiawei; Zhong, Yongteng; Ye, Shaogan; Pang, Jihong
• Is Part Of: Measurement. Volume 134(2019)
• Page Start: 293
• Page End: 306
• Publication Date: 2019-02
• Subjects: Axial piston pump -- Fault diagnosis -- Walsh transform -- Teager energy operator -- Signal processing; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2018.10.085
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A denoising method based on Walsh transform and soft threshold is proposed. Four experimental examples on piston pumps demonstrate application of the method. Compared with EEMD and wavelet denosing method, the method has higher accuracy and operation speed. Abstract: Vibration signals carry a great deal of information that represents the health conditions of axial piston pumps. However, in the working conditions, the vibration signals of the axial piston pumps are often contaminated by the presence of heavy background noises. In order to solve the problem, a hybrid method of Walsh transform denoising and Teager energy operator (TEO) demodulation is proposed. Walsh transform has not been applied to the field of signal denoising. The soft threshold-based Walsh transform denoising method proposed in the present study combines the advantages of Walsh transform and soft threshold denoising algorithm which can rapidly and adaptively de-noise raw signals. Since the TEO could estimate both of the kinetic and potential energy of a signal, a TEO-based demodulation would be more suitable to extract faults, when compared to the traditional Hilbert demodulation. Simulations and experimental investigations were implemented to illustrate the feasibility of the proposed method. These diagnostic results show that the proposed method enables the efficient and rapid recognition of axial piston pumps faults.