Mechanical Fault Detection Based on Machine Vision and Blind Source Separation

doi:10.16183/j.cnki.jsjtu.2020.154

Abstract

Abstract:

In order to solve the difficulties in extracting the required signal from a complex signal environment, and overcome the shortcomings of traditional methods for signal acquisition and processing, and the uncertain location of multi-source fault vibration signals, the multi-source fault of mechanical rotor is studied, and a fault detection method for rotating machinery based on machine vision and blind source separation is proposed. First, the basic mathematical principles of machine vision and blind source separation problem are introduced. Next, the acquired high-speed video is analyzed based on the blind source signal separation method and the overdetermined visual blind source separation method to achieve the separation and positioning of multi-source vibration signals. The experimental results show that the detection method proposed in this paper can accurately locate the multi-source faults of rotating machinery. This method combines the measurement methods of machine vision with the signal processing method of blind source separation to achieve an effective separation and identification on the multi-source faults.

Key words: multi-source fault, machine vision, blind source seperation, separation and identification

CLC Number:

TH133

PENG Cong, LIU Bin, ZHOU Qian. Mechanical Fault Detection Based on Machine Vision and Blind Source Separation[J]. Journal of Shanghai Jiaotong University, 2020, 54(9): 953-960.

Figures/Tables 10

Fig.1

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Tab.1

PI coefficients and global matrix table in different diversity modes

分集模式	PI系数	G_A
未分集	0.5790	$1.000000 0.474490 0.699818 1.000000$
二重分集	0.5947	$1.000000 0.003566 0.920868 1.000000$
四重分集	0.8876	$1.000000 0.840490 0.979818 1.000000$

Tab.1

Fig.5

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Fig.9

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