Rotating speed isolation and its application to rolling element bearing fault diagnosis under large speed variation conditions

Wang, Yi; Xu, Guizhi; Zhang, Qing; Liú, Dan; Jiang, Kuosheng

doi:10.1016/j.jsv.2015.03.018

Cited by 80 publications

(31 citation statements)

References 31 publications

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“…where f b is the bandwidth parameter and f c is the center frequency. Equations (16) and (17) show that the time-frequency resolution of the wavelet depends on the bandwidth f b and center frequency f c [21]. e bandwidth parameter controls the oscillation attenuation of the Morlet wavelet.…”

Section: Continuous Wavelet Transform Analysismentioning

confidence: 99%

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Estimation of the Defect Width on the Outer Race of a Rolling Element Bearing under Time‐Varying Speed Conditions

Hou

Lee

2019

Shock and Vibration

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Fault diagnosis and failure prognostics for rolling element bearing are helpful for preventing equipment failure and predicting the remaining useful life (RUL) to avoid catastrophic failure. Spall size is an important fault feature for RUL prediction, and most research work has focused on estimating the fault size under constant speed conditions. However, estimation of the defect width under time-varying speed conditions is still a challenge. In this paper, a method is proposed to solve this problem. To enhance the entry and exit events, the edited cepstrum is used to remove the determined components. The preprocessed signal is resampled from the time domain to the angular domain to eliminate the effect of speed variation and measure the defect size of a rolling element bearing on outer race. Next, the transient impulse components are extracted by local mean decomposition. The entry and exit points when the roller passes over the defect width on the outer race were identified by further processing the extracted signal with time-frequency analysis based on the continuous wavelet transform. The defect size can be calculated with the angle duration, which is measured from the identified entry and exit points. The proposed method was validated experimentally.

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Section: Continuous Wavelet Transform Analysismentioning

confidence: 99%

“…Techniques have been proposed to process nonstationary vibration signals [1,[12][13][14][15]. Order tracking (OT) is widely used among those techniques, which can eliminate the effect of speed fluctuation [16].…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Defect Width on the Outer Race of a Rolling Element Bearing under Time‐Varying Speed Conditions

Hou

Lee

2019

Shock and Vibration

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show abstract

“…12(b). It can be seen that the characteristic order of the reference shaft (1 ) and its harmonics (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) are quite abundant. The frequency change of the modulation component with the largest energy is the same as the change of the frequency of the reference shaft, thereby verifying that the fault gear is on the reference shaft.…”

Section: Application To the Gear Fault Detectionmentioning

confidence: 99%

“…Order tracking is a method of great value for vibration signal analysis because of its effectiveness in identifying and separating speed-related vibration [5]. Tacholess order tracking technology has gotten widespread concern, for avoiding installation of tachometers or angle encoders by estimating instantaneous frequency of shaft [4,6] or instantaneous phase of shaft [7,8]. The angular-stationary signal acquired from non-stationary signal by equal angle resampling can represent fault features more intuitively [9].…”

Section: Introductionmentioning

confidence: 99%

Tacholess order tracking method for gearbox based on time-varying filter and energy centrobaric correction method

Jing¹,

Luo²,

Leng³

et al. 2019

J. vibroeng.

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Under the premise that the instantaneous speed can be accurately measured or precise estimated, order tracking is considered to be a classical and effective technique for non-stationary vibration analysis of rotating machinery. The meshing frequency components of the vibration signal measured from complex gearbox system interfere with each other mutually. The energy centrobaric correction method exhibits exact instantaneous frequency estimation (IFE) ability under the interference, but is susceptible to strong noise. Combining with time-varying filter and energy centrobaric correction method, a tacholess order tracking technique for gear fault diagnosis under the rotating frequency multi-linear fluctuation is proposed. The time-varying filter is designed to filter the strong noise signal, and then IFE could be accurately calculated from the filtered signal by energy centrobaric correction method. The instantaneous phase of reference shaft based on the Vold-Kalman filter (VKF) is used for angular resampling of the original vibration signal. The results show that the shaft rotating speed could be accurately identified from extracted gear meshing components. The order spectrum obtained by the proposed method with tacholess is substantially the same as the order spectrum obtained by a tachometer. Simulation analysis and experimental results verify the advantages of proposed tacholess order tracking technology for monitoring and fault diagnosis of gearbox vibration under the condition of strong noise and large speed variation.

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“…A tacholess order tracking method that was based on generalized demodulation was also introduced to extract the tachometer information from the vibration signal [15]. A rotating speed isolation method was proposed, in which the rotating speed component was separated using a low-pass filter and the IFR was extracted via ridge detection [16].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Motor Bearing by Analyzing a Video Clip

Wang

Liu

et al. 2016

Mathematical Problems in Engineering

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Conventional bearing fault diagnosis methods require specialized instruments to acquire signals that can reflect the health condition of the bearing. For instance, an accelerometer is used to acquire vibration signals, whereas an encoder is used to measure motor shaft speed. This study proposes a new method for simplifying the instruments for motor bearing fault diagnosis. Specifically, a video clip recording of a running bearing system is captured using a cellphone that is equipped with a camera and a microphone. The recorded video is subsequently analyzed to obtain the instantaneous frequency of rotation (IFR). The instantaneous fault characteristic frequency (IFCF) of the defective bearing is obtained by analyzing the sound signal that is recorded by the microphone. The fault characteristic order is calculated by dividing IFCF by IFR to identify the fault type of the bearing. The effectiveness and robustness of the proposed method are verified by a series of experiments. This study provides a simple, flexible, and effective solution for motor bearing fault diagnosis. Given that the signals are gathered using an affordable and accessible cellphone, the proposed method is proven suitable for diagnosing the health conditions of bearing systems that are located in remote areas where specialized instruments are unavailable or limited.

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Rotating speed isolation and its application to rolling element bearing fault diagnosis under large speed variation conditions

Cited by 80 publications

References 31 publications

Estimation of the Defect Width on the Outer Race of a Rolling Element Bearing under Time‐Varying Speed Conditions

Estimation of the Defect Width on the Outer Race of a Rolling Element Bearing under Time‐Varying Speed Conditions

Tacholess order tracking method for gearbox based on time-varying filter and energy centrobaric correction method

Fault Diagnosis of Motor Bearing by Analyzing a Video Clip

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