Monitoring varying speed machinery vibrations—II recursive filters and angle domain

Lopatinskaia, E.; Zhu, Jichao; Mathew, Joseph

doi:10.1006/mssp.1995.0048

Cited by 29 publications

(25 citation statements)

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“…The vibration signal sampled from the rotating machinery is a nonstationary signal, whose amplitudes and frequencies both vary with time. Fixed time sampling cannot cope with the varying rotational frequency of the machine, resulting in increasing leakage error and spectral smearing [1,2]. Therefore, most of the conventional methods for signal processing become inappropriate when monitoring the vibrations of varying speed machinery [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…In the last decade vibration analysis and condition monitoring techniques for varying speed machinery have attracted the attention of scientists and engineers. Lopatinskaia et al [1,2] presented the application of recursive filtering and angle domain analysis to non-stationary vibration analysis. The approach is implemented and validated through computer simulation and experiments.…”

Section: Introductionmentioning

confidence: 99%

“…Fixed time sampling cannot cope with the varying rotational frequency of the machine, resulting in increasing leakage error and spectral smearing [1,2]. Therefore, most of the conventional methods for signal processing become inappropriate when monitoring the vibrations of varying speed machinery [1,2]. Some progress has been made in the theoretical analysis [7,8], the signal processing methodology [9,10], measurements and practical applications of varying speed machinery monitoring [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Gear fault detection and diagnosis under speed-up condition based on order cepstrum and radial basis function neural network

Zhang

Zheng

2009

J Mech Sci Technol

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Varying speed machinery condition detection and fault diagnosis are more difficult due to non-stationary machine dynamics and vibration. Therefore, most conventional signal processing methods based on time invariant carried out in constant time interval are frequently unable to provide meaningful results. In this paper, a study is presented to apply order cepstrum and radial basis function (RBF) artificial neural network (ANN) for gear fault detection during speedup process. This method combines computed order tracking, cepstrum analysis with ANN. First, the vibration signal during speed-up process of the gearbox is sampled at constant time increments and then is re-sampled at constant angle increments. Second, the re-sampled signals are processed by cepstrum analysis. The order cepstrum with normal, wear and crack fault are processed for feature extracting. In the end, the extracted features are used as inputs to RBF for recognition. The RBF is trained with a subset of the experimental data for known machine conditions. The ANN is tested by using the remaining set of data. The procedure is illustrated with the experimental vibration data of a gearbox. The results show the effectiveness of order cepstrum and RBF in detection and diagnosis of the gear condition.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gear fault detection and diagnosis under speed-up condition based on order cepstrum and radial basis function neural network

Zhang

Zheng

2009

J Mech Sci Technol

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“…Fixed time sampling cannot cope with the varying rotational frequency of the machine, resulting in increasing leakage error and spectral smearing. Therefore, most of the conventional methods for signal processing become inappropriate when monitoring the vibrations of varying speed machinery [13,14]. The reason why we stress the run-up or run-down process is that non-stationary vibrations signals from varying speed machinery may include more abundant information about its condition.…”

Section: Introductionmentioning

confidence: 99%

“…Some progresses have been made in the theoretical analysis, the signal processing methodology, measurements and practical applications of varying speed machinery monitoring. Lopatinskaia et al [13,14] presented the application of recursive filtering and angle domain analysis to non-stationary vibration analysis. The approach is implemented and validated through computer simulation and experiments.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault detection and diagnosis based on order tracking and Teager-Huang transform

Zhang

Zheng

2010

J Mech Sci Technol

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The vibration signal of the run-up or run-down process is more complex than that of the stationary process. A novel approach to fault diagnosis of roller bearing under run-up condition based on order tracking and Teager-Huang transform (THT) is presented. This method is based on order tracking, empirical mode decomposition (EMD) and Teager Kaiser energy operator (TKEO) technique. The nonstationary vibration signals are transformed from the time domain transient signal to angle domain stationary one using order tracking. EMD can adaptively decompose the vibration signal into a series of zero mean amplitude modulation-frequency modulation (AM-FM) intrinsic mode functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency of the AM-FM component at any instant. Experimental examples are conducted to evaluate the effectiveness of the proposed approach. The experimental results provide strong evidence that the performance of the Teager-Huang transform approach is better to that of the Hilbert-Huang transform approach for bearing fault detection and diagnosis. The Teager-Huang transform has better resolution than that of Hilbert-Huang transform. Teager-Huang transform can effectively diagnose the faults of the bearing, thus providing a viable processing tool for gearbox defect monitoring.

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Effective Vibration Analysis of Ic Engines Using Cyclostationarity. Part I-a Methodology for Condition Monitoring

Antoni¹,

Danière²,

Guillet³

2002

Journal of Sound and Vibration

153

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Monitoring varying speed machinery vibrations—II recursive filters and angle domain

Cited by 29 publications

References 0 publications

Gear fault detection and diagnosis under speed-up condition based on order cepstrum and radial basis function neural network

Gear fault detection and diagnosis under speed-up condition based on order cepstrum and radial basis function neural network

Bearing fault detection and diagnosis based on order tracking and Teager-Huang transform

Effective Vibration Analysis of Ic Engines Using Cyclostationarity. Part I-a Methodology for Condition Monitoring

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