Construction of bearing health indicator under time-varying operating conditions based on Isolation Forest

Sim, Jinwoo; Min, Jinhong; Kim, Seokgoo; Lee, Seok Woo; Choi, Joo-Ho

doi:10.1016/j.engappai.2023.107058

Cited by 7 publications

(1 citation statement)

References 28 publications

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“…Four Rexnord ZA-2115 double row bearing run-to-failure experiments under the rotating speed 2000 rpm and radial load 6000 lbs were performed to acquire the bearing failure signals by using PCB 353B33 accelerometer and a national instruments DAQCard-6062E data acquisition card [48][49][50]. The bearing parameters are listed as below: the ball number 16, the pitch diameter 2.815 inches, the contact angle 15.17 degrees and rolling element diameter 0.331 inches.…”

Section: Case Ii: Outer Race Fault Feature Extractionmentioning

confidence: 99%

Fourth-stable stochastic resonance and its application to weak fault signal detection of rotating machinery

Yun,

Zhang,

Shang

et al. 2024

Phys. Scr.

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Weak characteristic extraction is vital for weak fault signal detection of machinery. Stochastic resonance (SR) is able to transfer noise energy into weak fault characteristic frequency excited by a defect of machines. However, the potential function in SR is vital to enhance weak fault characteristic frequency and determines the capability of SR to improve the signal-to-noise ratio (SNR) of a noisy signal. Now, common potential functions include monostable, bistable and even tri-stable potentials but fourth-stable SR has not been studied and applied to detect early fault characteristic frequency. In this paper, thus, we would investigate the behaviors of SR with a fourth-stable potential subject to additive noise, in which the approximate theoretical expression of the power done by SR is derived to demonstrate the fourth-stable SR. Then, a SR method with the fourth-stable potential is proposed to enhance weak fault characteristic frequency, in which these system parameters are adjusted by using SNR as the objective function and using genetic algorithms adaptively. In this paper, thus, Finally, the proposed method is verified by using a simulated signal with noise and two early fault experiment of rolling element bearings with different levels of defects on the outer and inner races. Moreover, the proposed method is compared with wavelet denoising and fast kurtogram methods. The comparisons indicate that the proposed method has the better performance for enhancing weak fault characteristic frequency or weak useful signals than other two methods and is available to weak fault signal detection of machinery.

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Section: Case Ii: Outer Race Fault Feature Extractionmentioning

confidence: 99%