Diagnosis of bearing fault signals based on empirical standard autoregressive power spectrum signal decomposition method

Abstract

Signal decomposition is an essential tool for the time-frequency analysis of bearing fault signals. Increasing attention has been devoted to developing methods that effectively extract fault characteristic information from bearing vibration signals. This paper proposes a novel signal decomposition method, called Empirical Standard Autoregressive Power Spectrum Decomposition (ESARPSD), to diagnose bearing faults. First, the normalized autoregressive power spectrum of the bearing fault signals is plotted, and their boundary is obtained based on the lowest minima principle. The signal is then filtered using filter banks. Each component is subsequently demodulated through resonance, and the corresponding envelope spectrum is observed to identify the corresponding fault frequency. Zero-phase filter banks are employed to address noise interference issues that impede decomposition and frequency aliasing when the signal-to-noise ratio is low. Moreover, through normalized autoregressive power spectrum and resonance demodulation techniques, adaptive signal decomposition can accurately separate the target high-frequency vibration signals and detect the fault frequency.The accuracy and performance of the proposed ESARPSD method were validated using simulated signals and actual experimental data. The results demonstrate that this method can effectively decompose multi-frequency signals and identify all fault characteristics.

Adaptive local binarization feature mode decomposition and its application in combined failure identification of rolling bearings

“…Hence, condition monitoring and fault identification of rolling bearings are crucial for ensuring the safe and stable functioning of equipment. At present, scholars have made a great many studies on the fault diagnosis of bearings and proposed many feasible solutions, including oil sample analysis [3], acoustic emission inspection [4], vibration signal analysis [5], etc. Since vibration signals are collected when bearing failures contain rich fault information, it is crucial and significant to study on judging the operating state of bearings based on these signals in the field of bearing fault identification [6].…”

“…This plays the role of load transmitting. When some part of the bearing has a damage-type defect, the impact vibration is caused by a rolling element striking a defective spot when rotating past [5]. Unlike vibrations of a bearing in normal running, this one is characterized by a short duration and a wide frequency range and may trigger the resonance of some parts.…”