Application of the wavelet transform to fault detection in a spur gear

Staszewski, Wiesław J.; Tomlinson, G.R.

doi:10.1006/mssp.1994.1022

Cited by 200 publications

(102 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…By varying the parameters a and b, different daughter wavelets can be obtained that constitute a wavelet family. Wavelet transform is to perform the following operation (Lin and Zuo, 2003;Staszewski and Tomlinson, 1994):…”

Section: Definitionmentioning

confidence: 99%

Robust Prognostics Concept for Gearbox with Artificially Induced Gear Crack Utilizing Acoustic Emission

Abouel-Seoud¹,

Elmorsy²,

Dyab³

2011

EER

View full text Add to dashboard Cite

Prognostic is a rapidly developing field and seeks to build on current diagnostic equipment capabilities for predicting the system state in advance. In machine condition prognostics, the current and past observations are used to predict the upcoming states of the machine. Signal-de-noising and extraction of the weak acoustic signature are crucial to gearbox prognostics since the inherent deficiency of the measuring mechanism often introduces a great amount of noise to the signal. In addition, the signature of a defective gearbox is spread across a wide frequency band and hence can easily become masked by noise and low frequency effects. As a result, robust concepts are needed to provide more evident information for gearbox performance assessment and prognostics. This paper introduces enhanced and robust prognostic concepts for gear tooth based on an optimal wavelet filter method for fault identification and a statistical method for performance degradation assessment. The experimental results demonstrate that the gear tooth defect can be detected and evaluated at an early stage of development when both the optimal wavelet filter and statistical analysis technique are used.

show abstract

Section: Definitionmentioning

confidence: 99%

Robust Prognostics Concept for Gearbox with Artificially Induced Gear Crack Utilizing Acoustic Emission

Abouel-Seoud¹,

Elmorsy²,

Dyab³

2011

EER

View full text Add to dashboard Cite

show abstract

“…Actually, wavelet transform is a kind of Fourier transform with adjustable window, leading to the result that frequency range is decided by sampling frequency, irrelevant to signals. 5 HHT has satisfactory adaptability, applied to monitor status of buildings, 6 analyze vibration signals, 7 and predict natural calamities. 8 Although the previous studies have already proposed many approaches for fault diagnosis, most papers did not focus on the discussion and prediction of degradation trend but the identification of different types of faults.…”

Section: Introductionmentioning

confidence: 99%

The application of Hilbert–Huang transform energy spectrum in brushless direct current motor vibration signals monitoring of unmanned aerial vehicle

Fan

Wang

et al. 2016

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Due to the characteristics of structure, brushless direct current motor has a long lifetime, which makes it more difficult to obtain failure data. Accordingly, valid degeneration characteristics are required in life prediction. In this article, a novel method of condition monitoring and comparing is presented, which contributes to life prediction. Experiments were conducted for hundreds of hours to get degradation data of brushless direct current motor used in rotor of unmanned aerial vehicle. Furthermore, the vibration characteristics were analyzed by Hilbert-Huang transform to get energy spectrum. And, the differences in energy spectrum between two groups of sample collected were quantified by Euclidean distance. According to the scan under microscope, the oxidation was observed on surface of the used bearing, which leads to the changes in energy spectrum. The experimental results demonstrate that Hilbert-Huang transform energy spectrum can be effectively applied to monitor and evaluate the status of brushless direct current motor.

show abstract

“…One of the important considerations in CWT is the proper choice of the mother wavelet function that is determined by the characteristics of the underlying signals. It is well known that the morlet wavelet is suitable for gearbox fault diagnosis [2,4,[11][12][13] because it has a similiar shape to that of the impulse signal. There are several types of morlet wavelet used in the existing literatures however, and the morlet wavelet used in this paper is formulated as [14]:…”

Section: The Continuous Wavelet Transform (Cwt)mentioning

confidence: 99%

“…Moreover, FFT assumes the underlying signal is a stationary signal, whereas the impulse generated by the gear fault is typically a transient signal. In this case, the use of timefrequency analysis has been proved to be able to detect the transient phenomena [3,4], e.g. the impulse response generated from faulty gear.…”

Section: Introductionmentioning

confidence: 99%

Time-Frequency Enhancement Technique for Bevel Gear Fault Diagnosis

et al. 2016

View full text Add to dashboard Cite

Abstract. Gear failure is one of the most common causes of breakdown in rotating machineries. It is well known that vibration signals from machineries can be effectively used to detect certain gear faults. Yet it is still not an easy task to find a symptom that reflects a particular fault from vibration signals. This paper presents an advanced time-frequency signal processing technique for extracting important gear fault information from the vibration signal that is heavily corrupted by measurement noise. Experiments were performed on a bevel gearbox test rig using vibration measurements. The Time Synchronous Average (TSA) was initially utilized to eliminate all asynchronous component of vibration signal obtained from the gear. The Continuous Wavelet Transform (CWT) method was then used to capture the non-stationary behaviour of the impulse signal generated from the broken bevel gear tooth. It was shown that the diagnosis method using the Continuous Wavelet Transform combined with Time Synchronous Averaging outperformed the conventional spectral analysis, capable of identifying the angular location of broken teeth in the gear.

show abstract

Application of the wavelet transform to fault detection in a spur gear

Cited by 200 publications

References 0 publications

Robust Prognostics Concept for Gearbox with Artificially Induced Gear Crack Utilizing Acoustic Emission

Robust Prognostics Concept for Gearbox with Artificially Induced Gear Crack Utilizing Acoustic Emission

The application of Hilbert–Huang transform energy spectrum in brushless direct current motor vibration signals monitoring of unmanned aerial vehicle

Time-Frequency Enhancement Technique for Bevel Gear Fault Diagnosis

Contact Info

Product

Resources

About