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.
Highlights
rolling element bearings (REBs) directly affects the functioning of the motor, which accounts for almost 40–50% of motor fault [6, 7]
For the sake of research of the nonlinear dynamic characteristics of the REB system containing surface defect, a theoretical model is presented by Rafsanjani et al In order to investigate the linear stability of the defective bearing rotor system with changes in the parameters of the conversion system, the classic Floquet theorem was embedded in this model
Feature extraction based on the traditional time domain parameters, for example, crest factor, peak-to-peak value, kurtosis, root-mean square, shape factor, and standard deviation etc. [16,17,18]; secondly, frequency domain parameters, such as power spectral density, power spectrum [19]; thirdly, analysis based on time-frequency domain methods, for instance, spectrogram and wavelet transform [20,21,22]; based on multiple parameters the mixed feature extraction realized, for example, the method of [23, 24] have extracted blended parameter features of time domain, frequency domain, and time-frequency
Summary
Find the step size that satisfies formula (9) and obtain the ε value of each signal. 0.24 average recognition rate of 40 replicates. 0.24 average recognition rate of 40 replicates E first experiment verifies the identification effect of four bearing states under load 0 HP and compares with the traditional ZC methods. En, the bearing state data at load 0 are used as the training set to test the fault identification effect of the bearing state data under other loads. The bearing state data under any load are used as the training set to test the fault identification effect of the bearing state data under other loads. E first part of the experiment is to verify the effect of the method in practical application, and the remaining two parts are used to test the robustness of the method The bearing state data under any load are used as the training set to test the fault identification effect of the bearing state data under other loads. e first part of the experiment is to verify the effect of the method in practical application, and the remaining two parts are used to test the robustness of the method
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