Abstract
In rotary machinery, bearings are susceptible to different types of mechanical faults, including ball, inner race, and outer race faults. In condition-based monitoring (CBM), several techniques have been proposed in fault diagnostics based on the vibration measurements. For this paper, we studied the fractal characteristics of non-stationary vibration signals collected from bearings under different health conditions. Using the detrended fluctuation analysis (DFA), we proposed a novel method to diagnose the bearing faults based on the scaling exponent (α1) of vibration signal at the short-time scale. In vibration data with high sampling rate, our results showed that the proposed measure, scaling exponent, provides an accurate identification of the health state of the bearing. At the end, we evaluated the performance of the proposed method under different data quality issues, data loss and induced noise.
Highlights
Using the detrended fluctuation analysis (DFA), the fractal characteristics of vibration signals can be inferred by calculating scaling exponent
The vibration signals in faulted bearings had higher deviation compared to the normal ones
We studied the fractal characteristics of bearing vibration signals from
Summary
MHM records and analyzes the raw vibrational data signals gathered by means of accelerometers. It extracts the useful features from the measurements and converts them to proper actions applied to the machinery through condition-based maintenance. Fault diagnostic techniques can be classified into signal-based, model-based, and hybrid methods. The signal-based methods depend mainly on the raw data extracted from the machinery by sensors. The detection of machine faults is carried out through different analysis techniques applied on the extracted data. The main advantage of this method is that the mathematical modeling for the physical setup is not needed and classical signal processing techniques can be exploited in noise reduction and feature extraction
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