Abstract
The properties of the time domain parameters of vibration signals have been extensively studied for the fault diagnosis of rolling element bearings (REBs). Parameters like kurtosis and Envelope Harmonic-to-Noise Ratio are the most widely applied in this field and some important progress has been made. However, since only one-sided information is contained in these parameters, problems still exist in practice when the signals collected are of complicated structure and/or contaminated by strong background noises. A new parameter, named Shock Pulse Index (SPI), is proposed in this paper. It integrates the mutual advantages of both the parameters mentioned above and can help effectively identify fault-related impulse components under conditions of interference of strong background noises, unrelated harmonic components and random impulses. The SPI optimizes the parameters of Maximum Correlated Kurtosis Deconvolution (MCKD), which is used to filter the signals under consideration. Finally, the transient information of interest contained in the filtered signal can be highlighted through demodulation with the Teager Energy Operator (TEO). Fault-related impulse components can therefore be extracted accurately. Simulations show the SPI can correctly indicate the fault impulses under the influence of strong background noises, other harmonic components and aperiodic impulse and experiment analyses verify the effectiveness and correctness of the proposed method.
Highlights
The potential failure of rotating machinery has a close relationship with the safe operation of industrial production, so a powerful fault diagnosis technology is the essential security component of industrial production
This paper introduces a characteristic parameter named Shock Pulse Index (SPI), which can be used in fault diagnosis of Rolling element bearings (REBs)
The performance of the SPI is compared with kurtosis and the Envelope Harmonic-to-Noise Ratio using simulated data and the conclusions are as follows: 1
Summary
The potential failure of rotating machinery has a close relationship with the safe operation of industrial production, so a powerful fault diagnosis technology is the essential security component of industrial production. The signal-based methods can be classified into time-domain [3], frequency-domain [4]. Background noisenoise is inevitable and it directly final diagnosis. SimulationAissimulation given hereistogiven illustrate capabilitythe of capability of the to distinguish the fault information under such high background noise the SPI to distinguish the fault information under such high background noise conditions. The number of sampling simulation samplingdata, frequency is 20,000. The number sampling points is 20,480, points is 20,480, the impulse amplitude is of
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