Health Indicator of Bearing Constructed by rms-CUMSUM and GRRMD-CUMSUM With Multifeatures of Envelope Spectrum

Identification of initial fault time for bearing based on monitoring indicator, WEMD and Infogram

“…Meanwhile, the IFT is also the division point of the healthy and unhealthy conditions [6]. However, since the IFT is easily submerged due to weak fault signatures and heavy background noise, the initial fault of bearing is very difficult to be detected timely [8][9][10]. Therefore, it is necessary to identify rapidly and accurately the IFT for improving the accuracy of the RUL prediction and know timely the fault condition of bearing.…”

“…In recent years, many efforts have been made to detect rapidly and accurately the IFT [11]. The backtracking strategy was used to determine the IFT quickly by Li et al [9], Babiker et al [8] and Meng et al [10]. The kurtosis value beyond threshold 3.5 was used to identify the IFT by Howard [12].…”

“…Signal processing is the first procedure to confirm the existence of a fault accurately [16]. Because the initial fault is weak and masked easily with the environmental noise under bearing running, many signal processing methods for extracting initial fault signatures or denoising based on vibration signal were investigated [9], for instance, Empirical Mode Decomposition (EMD) [17], Variational Mode Decomposition (VMD) [18], Singular Value Decomposition (SVD) [19], Multi-resolution SVD (MRSVD) [20], Local Mean Decomposition (LMD) [21], Wavelet Packet Decomposition (WPD) [22], TQWT [15,23] and improved methods [9,10,22]. Although the above methods have a good effect on detecting the early fault of bearing since the solid theoretical foundation, strong robustness and the features of the time-frequency domain are considered [24], in order to overcome their disadvantage and obtain better effectiveness or computational efficiency, a lot of optimal methods are investigated, their convenience is even ignored under the optimal studies in practice.…”

“…Meanwhile, an ideal health indicator will be helpful to reflect quickly and accurately the IFT of bearing. Although many health indicators can highlight the IFT, most of them are constructed based on the sampling data in full life or the selection of performance features of bearing [9,10,13]. Furthermore, the convenience and mechanism of the selection methods are different, for example, signal-to-ratio [25], locality preserving projection [26] and principal component analysis [27,28].…”

“…Furthermore, the convenience and mechanism of the selection methods are different, for example, signal-to-ratio [25], locality preserving projection [26] and principal component analysis [27,28]. Hence, these indicators are not convenient for directly monitoring the IFT, such as the HI proposed by Li et al [9], the Growth Rate of Real-time MD with Cumulative Sum (GRRMD-CUMSUM) proposed by Meng et al [10] and MD employed Yu et al [13]. Some health indicators are able to present the IFT visually, but there is not a definite feature or quantified criteria for determining the IFT, for instance, RMS employed by Jiang et al [14,29], FEEMD-EHNR and EEACF proposed by Chegini [11].…”