An Adaptive Generalized Demodulation Method for Multimedia Spectrum Analysis is Applied in Rolling Bearing Fault Diagnosis

Abstract

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.

Keywords

• Fault diagnosis
• rolling bearing
• adaptive generalized demodulation
• Internet of Things
• multimedia