Research into rolling bearing fault diagnosis methods is of great significance because rolling bearings are a key part of mechanical equipment. The effect of iterative generalized demodulation (IGD) on the demodulation of the fundamental frequency component is obvious in the fault diagnosis of rolling bearings at variable speeds. However, there is a problem; the frequency curve of the demodulation octave frequency component overlaps, and multiple determinations of the bandpass filter parameters produce an artificial error that leads to the misdiagnosis of faults. Therefore, a method for rolling bearing fault diagnosis based on adaptive generalized demodulation (AGD) is proposed. First, the resonance band is intercepted by the fast kurtogram and its envelope results. Second, the adaptive chirp mode decomposition (ACMD) algorithm is used to decompose the envelope signal, the relationship between the time and frequency of the signal is clearly characterized by the form of multimedia pictures, and the instantaneous frequency of each signal component is calculated. Third, the instantaneous frequency is used as the phase function to perform generalized demodulation for each signal component. Last, all the demodulated signals are accumulated, and a fast Fourier transform (FFT) is used to extract the fault's characteristic frequency. The proposed method is compared with IGD by using simulation signals and actual bearing signals collected by sensors under the Internet of Things (IoT). An adaptive diagnosis function is realized through this proposed method at variable speeds. Moreover, the average frequency spectrum identification rate of rolling bearing faults is improved by more than 2.6 times compared with that of the IGD in the simulation signal verification and by more than 1.7 times compared with that of the IGD in the real signal verification. This method is strongly immune to noise. INDEX TERMS Fault diagnosis, rolling bearing, adaptive generalized demodulation, Internet of Things, multimedia.
Instantaneous frequency estimation of rolling bearing is a key step in order tracking without tachometers, and time-frequency analysis method is an effective solution. In this paper, a new method applying the variational mode decomposition (VMD) in association with the synchroextracting transform (SET), named VMD-SET, is proposed as an improved time-frequency analysis method for instantaneous frequency estimation of rolling bearing. The SET is a new time-frequency analysis method which belongs to a postprocessing procedure of the short-time Fourier transform (STFT) and has excellent performance in energy concentration. Considering nonstationary broadband fault vibration signals of rolling bearing under variable speed conditions, the time-frequency characteristics cannot be obtained accurately by SET alone. Thus, VMD-SET method is proposed. Firstly, the signal is decomposed into several intrinsic mode functions (IMFs) with different center frequency by VMD. Then, effective IMFs are selected by mutual information and kurtosis criteria and are reconstructed. Next, the SET method is applied to the reconstructed signal to generate the time-frequency representation with high resolution. Finally, instantaneous frequency trajectory can be accurately extracted by peak search from the time-frequency representation. The proposed method is free from time-varying sidebands and is robust to noise interference. It is proved by numerical simulated signal analysis and is further validated by lab experimental rolling bearing vibration signal analysis. The results show this method can estimate the instantaneous frequency with high precision without noise interference.
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