An online tacholess order tracking technique based on generalized demodulation for rolling bearing fault detection

Wang, Yi; Xu, Guanghua; Luo, A-Li; Li, Lin; Jiang, Kuosheng

doi:10.1016/j.jsv.2015.12.041

Cited by 93 publications

(21 citation statements)

References 35 publications

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“…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%

“…In Ref. [7], acquired by generalized demodulation and Hilbert transform, instantaneous phase of an adaptively filtered fault bearing signal can provide reference for equal angle resampling of filtered envelopes, which filtered based on Fast-kurtogram. The instantaneous frequency of filtered bearing signal obtained by the short-time Fourier transform (STFT), which filtered by the fast-spectrum kurtosis, then the angular domain signal and the order spectrum were obtained by a new resampling algorithm [18].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Application To the Gear Fault Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Jing¹,

Luo²,

Leng³

et al. 2019

J. vibroeng.

View full text Add to dashboard Cite

show abstract

“…Reference [1] proposed a novel order tracking method based on discrete spectrum correction technique to analyze wind turbine gearbox vibration for the purposes of health monitoring and fault diagnosis. Reference [2] proposed an online tacholess order tracking technique based on generalized demodulation to conduct the bearing fault detection under speed variation conditions without a tachometer.…”

Section: Introductionmentioning

confidence: 99%

Engine fault feature extraction based on order tracking and VMD in transient conditions

Ren¹,

Jin²,

Mei³

et al. 2018

Vib. proced.

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A method based on order tracking and Variational Mode Decomposition was proposed to solve the problem of non-stationarity and background noise in the vibration signal of engine. For the vibration signal of diesel engine crankshaft bearing fault and Connecting rod bearing of gasoline engine, the non-stationary signal in the time domain was converted into a pseudo stationary signal on the angular domain based on order tracking. Then the reconstructed signal was decomposed by several components by VMD, and the component that contains fault information was selected. The three-dimensional spectral array of order, speed and power spectrum was calculated, and the fault feature was extracted. The effectiveness of the proposed method is verified by simulation and experiment.

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“…In recent years, rolling bearing fault diagnosis has become the research focus and received considerable attention from many scholars around the world. At present, the primary means of fault diagnosis is to obtain underlying fault-related information via processing vibration signals [3,4]. However, rolling bearing vibration signals are usually nonstationary and nonlinear with a large amount of background noise.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of rolling bearing based on improved CEEMDAN and distance evaluation technique

Ding¹,

Li²,

Qu³

2017

J. vibroeng.

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In order to accurately identify the fault conditions of rolling bearing, this paper presents a fault diagnosis method based on improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and distance evaluation technique. In this method, to effectively extract potential fault-related information, vibration signals of rolling bearing in different fault conditions are decomposed into a set of intrinsic mode functions (IMFs) through improved CEEMDAN. The first eight IMFs containing most fault information are selected for extracting fault features. The original feature set is obtained including energy values, singular values and envelope sample entropy values. Then distance evaluation technique is implemented for selecting sensitive feature set and discarding irrelevant or redundant features. Subsequently, the sensitive feature set is fed into support vector machine (SVM) for automatically identifying rolling bearing fault conditions. The simulation results demonstrate that improved CEEMDAN is able to solve the problem of mode mixing and achieve a numerically negligible reconstruction error. Meanwhile experimental consequences indicate that the proposed method can acquire higher identification accuracy, as well as reduce the classifier computational burden.

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An online tacholess order tracking technique based on generalized demodulation for rolling bearing fault detection

Cited by 93 publications

References 35 publications

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

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

Engine fault feature extraction based on order tracking and VMD in transient conditions

Fault diagnosis of rolling bearing based on improved CEEMDAN and distance evaluation technique

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