Modeling and Optimization for Fault Diagnosis of Electromechanical Systems Based on Zero Crossing Algorithm

Abstract

The demand of system security and reliability in the modern industrial process is ever-increasing, and fault diagnosis technology has always been a crucial research direction in the control field. Due to the complexity, nonlinearity, and coupling of multitudinous control systems, precise system modeling for fault diagnosis is attracting more attention. In this paper, we propose an improved method of electromechanical systems fault diagnosis based on zero-crossing (ZC) algorithm, which can present the calculation model of zero-crossing rate (ZCR) and optimize the parameters of ZC algorithm by establishing a criterion function model to improve the diagnosis accuracy and robustness of ZC characteristic model. The simulation validates the influence of different signal-to-noise ratio (SNR) on ZC feature recognition ability and indicates that the within-between distance model is effective to enhance the diagnose accuracy of ZC feature. Finally, the method is applied to the diagnosis of motor fault bearing, which confirms the necessity and effectiveness of the model improvement and parameter optimization and verifies the robustness to the load.

Research on bearing ZC feature selection method based on DWCMI

“…As be seen from formula (17), the interaction between the two features strengthens the correlation between them and the classification label. This leads to the provision of more classification information and enhances the importance of improving the classification effect.…”

“…This duration is typically quantified using an over-threshold function known as ZCDR, which represents the time interval between ZC points of signal. Considering that ZC features are susceptible to noise, the literature [17] enhances the ZC algorithm model and optimizes the noise reduction threshold within the model through the construction of discriminant functions to achieve adaptive noise reduction.…”

“…, M − 1). C i denote the count of ZC whose duration falls in the range τ j and τ j +1 , according to the literature method [17], C i can be calculated as:…”

System Design for the Detection of Induction Motor Faults using Multi-Domain Feature Fusion

“…These feature have proved their capability in various detection problems in different applications [19,20]. Standard deviation [22] Skewness [22] Kurtosis [22] Shannon energy [23] Log energy [24] Zero crossing rate [25] Total harmonic distortion [26] Rms [27] Energy [23] Crest factor [28] Shape factor [29] Impulse factor [30] Margin factor [31]…”