Abstract
Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is a powerful method that can extract the periodic characteristics of signal effectively, but this method needs to evaluate the fault cycle a priori, and moreover, the results obtained in a complex environment are easily affected by noise. These drawbacks reduce the application of MOMEDA in engineering practice greatly. In order to avoid such problems, in this paper, we propose an adaptive fault diagnosis method composed of two parts: fault information integration and extracted feature evaluation. In the first part, a Teager energy spectrum amplitude factor (T-SAF) is proposed to select the intrinsic mode function (IMF) components decomposed by ensemble empirical mode decomposition (EEMD), and a combined mode function (CMF) is proposed to further reduce the mode mixing. In the second part, the particle swarm optimization (PSO) taking fractal dimension as the objective function is employed to choose the filter length of MOMEDA, and then the feature frequency is extracted by MOMEDA from the reconstructed signal. A cyclic recognition method is proposed to appraise the extracted feature frequency, and the evaluation system based on threshold and weight coefficient removes the wrong feature frequency. Finally, the feasibility of the method is verified by simulation data, experimental signals, and on-site signals. The results show that the proposed method can effectively identify the bearing state.
Highlights
Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is a powerful method that can extract the periodic characteristics of signal effectively, but this method needs to evaluate the fault cycle a priori, and the results obtained in a complex environment are affected by noise. ese drawbacks reduce the application of MOMEDA in engineering practice greatly
In order to avoid such problems, in this paper, we propose an adaptive fault diagnosis method composed of two parts: fault information integration and extracted feature evaluation
A Teager energy spectrum amplitude factor (T-SAF) is proposed to select the intrinsic mode function (IMF) components decomposed by ensemble empirical mode decomposition (EEMD), and a combined mode function (CMF) is proposed to further reduce the mode mixing
Summary
In order to obtain the characteristic frequency more accurately, appropriate sampling frequency is defined to make the fault cycles of bearing with low-frequency characteristic in a smaller range, multipoint kurtosis is used as a metric for determining the period, and the PSO algorithm is used to select the filter length. E frequency obtained from the filtered signal in each cycle is shown in Figure 10(a); the abscissa is cycle number, the ordinate is the characteristic frequency, and the red dotted line indicates the theoretical fault frequency value corresponding to the fault type. According to equation (5), the weight coefficient of the inner ring is 1 after the fifth cycle, so it is determined as the inner ring fault. e simulation result illustrates the feasibility of the method, which is further verified by experiments
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