Mechanical Fault Prognosis through Spectral Analysis of Vibration Signals

Wang, Kang1; Xu, Zhi-Jiang2; Gong, Yi3; Du, Ke-Lin4

2022-03-01

10.3390/a15030094

ALGORITHMS   影响因子和分区

1999-4893

Vibration signal analysis is the most common technique used for mechanical vibration monitoring. By using vibration sensors, the fault prognosis of rotating machinery provides a way to detect possible machine damage at an early stage and prevent property losses by taking appropriate measures. We first propose a digital integrator in frequency domain by combining fast Fourier transform with digital filtering. The velocity and displacement signals are, respectively, obtained from an acceleration signal by means of two digital integrators. We then propose a fast method for the calculation of the envelope spectra and instantaneous frequency by using the spectral properties of the signals. Cepstrum is also introduced in order to detect the unidentifiable periodic signal in the power spectrum. Further, a fault prognosis algorithm is presented by exploiting these spectral analyses. Finally, we design and implement a visualized real-time vibration analyzer on a Raspberry Pi embedded system, where our fault prognosis algorithm is the core algorithm. The real-time signals of acceleration, velocity, displacement of vibration, as well as their corresponding spectra and statistics, are visualized. The developed fault prognosis system has been successfully deployed in a water company.

digital integrator envelope spectrum cepstrum fault prognosis Raspberry Pi

ESCI ; EI

英语

其他

Science and Technology Project Funds of Zhejiang Provincial Water Resources Department[RC2162] ; National Natural Science Foundation of China[62071212]

Computer Science

Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods

WOS:000776843400001

MDPI

20221511947783

Acceleration ; Fast Fourier transforms ; Frequency domain analysis ; Machinery ; Signal analysis ; Vibration analysis

Information Theory and Signal Processing:716.1 ; Mathematical Transformations:921.3

Web of Science

1.Zhejiang Police Coll, Comp & Informat Secur Dept, Hangzhou 310018, Peoples R China
2.Zhejiang Inst Mech & Elect Engn, Sch Automat, Hangzhou 310059, Peoples R China
3.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen 518055, Peoples R China
4.Concordia Univ, Dept Elect & Comp Engn, Montreal, PQ H3G 2W1, Canada

Wang, Kang,Xu, Zhi-Jiang,Gong, Yi,et al. Mechanical Fault Prognosis through Spectral Analysis of Vibration Signals[J]. ALGORITHMS,2022,15(3).

Wang, Kang,Xu, Zhi-Jiang,Gong, Yi,&Du, Ke-Lin.(2022).Mechanical Fault Prognosis through Spectral Analysis of Vibration Signals.ALGORITHMS,15(3).

Wang, Kang,et al."Mechanical Fault Prognosis through Spectral Analysis of Vibration Signals".ALGORITHMS 15.3(2022).