Multi-Fault Detection of Rolling Element Bearings under Harsh Working Condition Using IMF-Based Adaptive Envelope Order Analysis

Abstract

When operating under harsh condition (e.g., time-varying speed and load, large shocks), the vibration signals of rolling element bearings are always manifested as low signal noise ratio, non-stationary statistical parameters, which cause difficulties for current diagnostic methods. As such, an IMF-based adaptive envelope order analysis (IMF-AEOA) is proposed for bearing fault detection under such conditions. This approach is established through combining the ensemble empirical mode decomposition (EEMD), envelope order tracking and fault sensitive analysis. In this scheme, EEMD provides an effective way to adaptively decompose the raw vibration signal into IMFs with different frequency bands. The envelope order tracking is further employed to transform the envelope of each IMF to angular domain to eliminate the spectral smearing induced by speed variation, which makes the bearing characteristic frequencies more clear and discernible in the envelope order spectrum. Finally, a fault sensitive matrix is established to select the optimal IMF containing the richest diagnostic information for final decision making. The effectiveness of IMF-AEOA is validated by simulated signal and experimental data from locomotive bearings. The result shows that IMF-AEOA could accurately identify both single and multiple faults of bearing even under time-varying rotating speed and large extraneous shocks.

Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift‐Invariant Dictionary Learning

“…However, the time-varying running information collection is invalid when a time-domain analysis is applied. Frequency-domain analyses, such as empirical mode decomposition (EMD) [7][8][9], short-time Fourier transformation (STFT) [10] and Wigner-Ville distribution (WVD) [11], and time-frequency analyses, such as wavelet transformation (WT) [12] and its extensions [13,14], are the traditional transform-based methods, which transform the measured vibration signal into another available space. In these transformed spaces, the fault information is notably enhanced [15][16][17].…”

Feature Mining and Health Assessment for Gearboxes Using Run-Up/Coast-Down Signals

“…Generally, recent advances in this direction can be classified into two groups. Since the signal measured in this condition is non-stationary in nature, the first group resorts to some non-stationary signal analysis tools such as short-time Fourier transform (STFT), empirical mode decomposition (EMD) [10,11], wavelet, chirplet, synchro-squeezing transform [12,13] and, more recently, proposed dynamic time warping. For instance, Meltzer et al [14] proposed a polar wavelet amplitude map to realize the fault diagnosis of gears operating under non-stationary rotation speeds.…”

Extracting accurate time domain features from vibration signals for reliable classification of bearing faults

“…Many approaches have been proposed in the literature for optimal selection of frequency band, such as spectral kurtosis based methods, spectral energy based methods, wavelet based methods etc., that are discussed in Barszcz and Jabłoński (2010) and Zhao et al (2014). We have employed spectral kurtosis based fast kurtogram method proposed by Antoni (2007) for frequency range selection in our enveloping-based data validation process as described in next section.…”