A novel time–frequency–space method with parallel factor theory for big data analysis in condition monitoring of complex system

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.

[1]  C. Stedmon,et al.  Monitoring organic loading to swimming pools by fluorescence excitation-emission matrix with parallel factor analysis (PARAFAC). , 2011, Water research.

[2]  H. X. Chen,et al.  Semiconductivities of passive films formed on stainless steel bend under erosion-corrosion conditions , 2018, Corrosion Science.

[3]  Richard A. Harshman,et al.  Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-model factor analysis , 1970 .

[4]  Xiaofei Zhang,et al.  Compressed sensing parallel factor analysis-based joint angle and Doppler frequency estimation for monostatic multiple-input–multiple-output radar , 2014 .

[5]  Yi Chai,et al.  Weak fault signal extraction for gearbox based on parallelizable underdetermined blind separation , 2013, 2013 Chinese Automation Congress.

[6]  Satya Prasad Ravi,et al.  Detection of Reliable Software Using SPRT on Interval Domain Data , 2011 .

[7]  Nitish V. Thakor,et al.  EEG Classification with a Sequential Decision-Making Method in Motor Imagery BCI , 2017, Int. J. Neural Syst..

[8]  Mamoun F. Abdel-Hafez,et al.  A Multihypothesis Sequential Probability Test for Fault Detection and Identification of Vehicles' Ultrasonic Parking Sensors , 2011 .

[9]  Chen Hanxin,et al.  Research on improved edge extraction algorithm of rectangular piece , 2018 .

[10]  Santiago Zazo,et al.  Using one class SVM to counter intelligent attacks against an SPRT defense mechanism , 2019, Ad Hoc Networks.

[11]  Xiaofei Zhang,et al.  BLIND PARALIND MULTIUSER DETECTION FOR SMART ANTENNA CDMA SYSTEM OVER MULTIPATH FADING CHANNEL , 2009 .

[12]  Ahmed Ramadhan Al-Obaidi.  Investigation of effect of pump rotational speed on performance and detection of cavitation within a centrifugal pump using vibration analysis , 2019, Heliyon.

[13]  Hanxin Chen,et al.  Multiple fault condition recognition of gearbox with sequential hypothesis test , 2013 .

[14]  Fumikazu Miwakeichi,et al.  Decomposing EEG data into space–time–frequency components using Parallel Factor Analysis , 2004, NeuroImage.

[15]  S. D. Jong,et al.  Three-way principal component analysis applied to food analysis: an example , 2002 .

[16]  Ahmed Ramadhan Al-Obaidi.  Experimental Investigation of the Effect of Suction Valve Opening on the Performance and Detection of Cavitation in the Centrifugal Pump Based on Acoustic Analysis Technique , 2019, Archives of Acoustics.

[17]  Janani Shruti Rapur,et al.  Automation of multi-fault diagnosing of centrifugal pumps using multi-class support vector machine with vibration and motor current signals in frequency domain , 2018 .

[18]  Hanxin Chen,et al.  Model‐based method with nonlinear ultrasonic system identification for mechanical structural health assessment , 2020, Trans. Emerg. Telecommun. Technol..

[19]  Omid A. Zargar,et al.  Detecting Cavitation in a Vertical Sea water Centrifugal Lift Pump Related to Iran Oil Industry Cooling Water Circulation System , 2014 .

[20]  Jinmin Huang,et al.  Finite Element Analysis Model on Ultrasonic Phased Array Technique for Material Defect Time of Flight Diffraction Detection , 2020 .

[21]  Rajiv Tiwari,et al.  Identification of suction flow blockages and casing cavitations in centrifugal pumps by optimal support vector machine techniques , 2017 .

[22]  Rasmus Bro,et al.  A novel strategy for solving matrix effect in three-way data using parallel profiles with linear dependencies. , 2007, Analytica chimica acta.

[23]  Liu Yang,et al.  Fault diagnosis of gearbox based on RBF-PF and particle swarm optimization wavelet neural network , 2019, Neural Computing and Applications.

[24]  Peng Chen,et al.  A New Rotation Machinery Fault Diagnosis Method Based on Deep Structure and Sparse Least Squares Support Vector Machine , 2019, IEEE Access.

[25]  Yanjun Lu,et al.  Fault Identification of Gearbox Degradation with Optimized Wavelet Neural Network , 2013 .

[26]  Liu Yang,et al.  Particle Swarm Optimization Algorithm with Mutation Operator for Particle Filter Noise Reduction in Mechanical Fault Diagnosis , 2020, Int. J. Pattern Recognit. Artif. Intell..

[27]  Xiaofei Zhang,et al.  Blind Paralind Space-Time Multiuser Detection for Asynchronous CDMA System , 2009, J. Circuits Syst. Comput..

[28]  Alessandro Ulrici,et al.  Analysis of sensory data of Aceto Balsamico Tradizionale di Modena (ABTM) of different ageing by application of PARAFAC models , 2006 .

[29]  J. Chang,et al.  Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition , 1970 .

[30]  Liu Yang,et al.  WITHDRAWN: Image quality enhanced recognition of laser cavity based on improved random hough transform , 2019 .

[31]  Liu Yang,et al.  Multi-fault Condition Monitoring of Slurry Pump with Principle Component Analysis and Sequential Hypothesis Test , 2020, Int. J. Pattern Recognit. Artif. Intell..

[32]  Richard A. Harshman,et al.  Factor analysis of tongue shapes. , 1971, The Journal of the Acoustical Society of America.

[33]  Jing-Li Luo,et al.  Silver sulfide anchored on reduced graphene oxide as a high -performance catalyst for CO 2 electroreduction , 2018, Journal of Power Sources.

[34]  J. Kruskal.  Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics , 1977 .