Automatic signal quality check and equipment condition surveillance based on trivalent logic diagnosis theory. (March 2019)

• Record Type: Journal Article
• Authors: Song, Liuyang; Wang, Huaqing; Chen, Peng
• Is Part Of: Measurement. Volume 136(2019)
• Page Start: 173
• Page End: 184
• Publication Date: 2019-03
• Subjects: Vibration measurement -- Condition monitoring -- Fault diagnosis -- Histograms -- Measurement errors; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2018.12.036
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Establishing trivalent logic diagnosis theory. Combining principal component analysis and histogram with trivalent logic theory. Realizing automatic signal quality check and equipment condition surveillance. Verifying the effectiveness of the method by simulation signals and real signals. Abstract: In the field of fault diagnosis, inadequate signals measured for equipment condition monitoring may cause incorrect diagnostic results and reduce the accuracy and reliability of the equipment diagnosis system. This paper proposes a method of signal quality check and equipment condition surveillance based on trivalent logic theory, signal histogram analysis and principal component analysis (PCA), in order to automatically evaluate the quality of measured signals to ensure that the signals are real and valid for the condition diagnosis of equipment, and automatically judge the equipment state for condition surveillance. The novelty of this paper are summarized as: (1) Trivalent logic has been expanded appropriately into the trivalent logic diagnosis theory, so that it can be applied to verify the signal quality in the acquisition process for fault diagnosis and equipment condition surveillance; (2) In order to directly and effectively extract features of a signal following any probability density distribution, the histograms of the signal measured for equipment condition diagnosis is used to substitute time domain symptom parameters which have been generally used in equipment diagnosis technology; (3) PCA is used to integrate the histograms to realize signal quality check and equipment condition surveillance on the basis of the trivalent logic diagnosis theory. By the method proposed in this paper, the moment when the signal for equipment condition diagnosis is relatively stable can be found, and the unfavorable signal can be avoided for ensuring the accuracy and reliability of the equipment condition diagnosis. Simulation signals and real signals measured in various conditions from a blower are respectively used to verify the effectiveness of the proposed method.