A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence the fault classification performance. Feature selection plays a vital role in selecting the most re...

The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the ...

The rolling element bearingsare most critical components in a machine. Condition monitoring and fault diagnostics of these bearings are of great concern in industries as most rotating machine failures are often linked to bearing failures. This paper presents a methodology for fault diagnosis of rolling element bearings based on discrete wavelet transform (DWT) and wavelet packet transform (WPT)...

This paper presents a rolling bearing fault diagnosis approach by integrating wavelet packet decomposition (WPD) with multi-scale permutation entropy (MPE). The approach uses MPE values of the sub-frequency band signals to identify faults appearing in rolling bearings. Specifically, vibration signals measured from a rolling bearing test system with different defect conditions are decomposed int...

Induction motors plays the most important role in any industry. Induction motor faults results in motor failure causing breakdown and great loss of production due to shutdown of industry and also increases the running cost of machine with reduction in efficiency. This needs for early detection of fault with diagnosis of its root cause. In this research paper a wavelet based fault classification...

Feature extraction from vibration signal is still a challenge in the area of fault diagnosis and remaining useful life (RUL) estimation of rotary machine. In this paper, a novel feature called phase space similarity (PSS) is introduced for health conditionmonitoring of bearings. Firstly, the acquired signal is transformed to the phase space through the phase space reconstruction (PSR). The simi...

Based on rolling bearing fault signal modulation model in the process of spreading, an improved method that combining Hilbert envelop extraction algorithm and large parameter setting rules in stochastic resonance (SR) is proposed for features extraction. Firstly, Hilbert transform can effectively eliminate the interference of high frequency carrier signal. Secondly, parameters setting rules in ...

The choice of demodulation band for envelope analysis of faulty bearings is often made by spectrum comparison with a healthy bearing, to choose resonance frequencies where the largest change occurred as a result of the fault. It has recently been established that the so-called “spectral kurtosis” gives a very similar indication of the band to be demodulated without requiring historical data. Th...