Abstract
The spatial information of the signal is neglected by the conventional frequency/time decompositions such as the fast Fourier transformation, principal component analysis, and independent component analysis. Framing of the data being as a three-way array indexed by channel, frequency, and time allows the application of parallel factor analysis, which is known as a unique multi-way decomposition. The parallel factor analysis was used to decompose the wavelet transformed ongoing diagnostic channel–frequency–time signal and each atom is trilinearly decomposed into spatial, spectral, and temporal signature. The time–frequency–space characteristics of the single-source fault signal was extracted from the multi-source dynamic feature recognition of mechanical nonlinear multi-failure mode and the corresponding relationship between the nonlinear variable, multi-fault mode, and multi-source fault features in time, frequency, and space was obtained. In this article, a new method for the multi-fault condition monitoring of slurry pump based on parallel factor analysis and continuous wavelet transform was developed to meet the requirements of automatic monitoring and fault diagnosis of industrial process production lines. The multi-scale parallel factorization theory was studied and a three-dimensional time–frequency–space model reconstruction algorithm for multi-source feature factors that improves the accuracy of mechanical fault detection and intelligent levels was proposed.
Highlights
Robotic system prediction and fault diagnosis integrate the latest achievements in the fields of machinery, electronics, computers, sensors, automatic control, artificial intelligence, and so on and are increasingly replacing operators to complete dangerous, frequent, and repetitive long-term work
Centrifugal pump (CP) vibration measurement was generally used in the quality control and condition monitoring
The hydraulic and mechanical vibrations of CP are caused by flow distribution and the high-speed interaction between the impellers and the volutes, especially when the impeller is rotating in the tongue area of the volute
Summary
Robotic system prediction and fault diagnosis integrate the latest achievements in the fields of machinery, electronics, computers, sensors, automatic control, artificial intelligence, and so on and are increasingly replacing operators to complete dangerous, frequent, and repetitive long-term work. With the rapid advancement of computer technology, the parallel factor model is fitted by trilinear alternating least square (TALS), which greatly improves the running time and convergence accuracy of the signal processing algorithm. The optimization of the nonlinear relationship between process state variables, failure modes, and multi-source vibration signatures in time, frequency, and spatial feature vector space after feature extraction is ensured by the parallel factorization theory.[35] Remote fault diagnosis is an advanced diagnostic method for implementing industrial robot fault diagnosis. Equation A 1⁄4 1⁄2a1; a2; Á Á Á ; aI is defined as the I Â F matrix, equations B 1⁄4 1⁄2b1; b2; Á Á Á ; bJ , and C 1⁄4 1⁄2c1; c2; Á Á Á ; cK are defined as the J Â F matrix and the K Â F matrix
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