Multi-source and multi-fault condition monitoring based on parallel factor analysis and sequential probability ratio test

Abstract

The monitoring of mechanical equipment systems contains an increasing number of complex content, expanding from traditional time, and frequency information to three-dimensional data of the time, space, and frequency information, and even higher-dimensional data containing subjects, experimental conditions. For high-dimensional data analysis, traditional decomposition methods such as Hilbert transform, fast Fourier transformation, and Gabor transformation not only lose the integrity of the data, but also increase the amount of calculation and introduce a lot of redundant information. The phenomenon of feature coupling, aliasing, and redundancy between the mechanical multi-source data signals will cause the inaccuracy of the evaluation, diagnosis, and prediction of industrial production operation status. The analysis of the three-way tensor composed of channel, frequency, and time is called parallel factor analysis (PARAFAC). The properties between the parallel factor analysis results and the input signals are studied through simulation experiments. Parallel factor analysis is used to decompose the third-order tensor composed of channel-time-frequency after continuous wavelet transformation of vibration signal into channel, time, and frequency characteristics. Multi-scale parallel factor analysis successfully extracted non-linear multi-dimensional dynamic fault characteristics by generating the spatial, spectral, time-domain signal loading value and three-dimensional fault characteristic expression. In order to verify the effectiveness of the space, frequency, and time domain signal loading values of the fault characteristic factors generated by the centrifugal pump system after parallel factor analysis, the characteristic factors obtained after parallel factor analysis are used as the SPRT test sequence for identification and verification. The results indicate that the method proposed in this article improves the measurement accuracy and intelligence of mechanical fault detection.

Hybrid Method with Parallel-Factor Theory, a Support Vector Machine, and Particle Filter Optimization for Intelligent Machinery Failure Identification

“…In the past century, many scholars and studies from around the world have focused on the special challenges associated with science and industrial technology regarding the development of original techniques for analyzing complex machinery, such as oil analysis, noise detection, vibration analysis, and non-destructive testing. Research regarding vibration-detection technology began before studies involving the other techniques; therefore, this technology is much more mature, and its application potential is the most extensive [8][9][10].…”

Multi-Sensor Data Driven with PARAFAC-IPSO-PNN for Identification of Mechanical Nonstationary Multi-Fault Mode

“…While traditional fault diagnosis methods cannot handle signals collected under complex conditions due to the low computing and storage capacity of computers, the current modern condition monitoring technology has been greatly improved in both theory and technology to the extent that the optimized system can realize data collection on complex equipment at multiple measurement points and throughout their whole life. The inspection system will obtain a large amount of data, machinery, and equipment fault diagnosis experience and gradually approach what is known as "big data" [4][5][6].…”