Application of acoustic emission to seeded gear fault detection

Toutountzakis, Tim; Tan, Chee Keong; David, Éric

doi:10.1016/j.ndteint.2004.06.008

Cited by 129 publications

(57 citation statements)

References 6 publications

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“…However, as observed by Toutountzakis et al [7] the attenuation value does vary significantly. Figure 4 was taken from Toutountzakis' report [7] which shows the AE response acquired simultaneously from the sensors mounted on the pinion gear and bearing casing; the variation in attenuation values is evident. The reason for this was attributed to the position of the bearing ball/roller elements during rotation.…”

Section: Resultsmentioning

confidence: 57%

“…It should be stated that the average attenuation from the gear face to the bearing casing was measured at approximately 35dB. However, as observed by Toutountzakis et al [7] the attenuation value does vary significantly. Figure 4 was taken from Toutountzakis' report [7] which shows the AE response acquired simultaneously from the sensors mounted on the pinion gear and bearing casing; the variation in attenuation values is evident.…”

Section: Resultsmentioning

confidence: 79%

See 1 more Smart Citation

Diagnostics and prognostics with acoustic emission, vibration and spectrometric oil analysis for spur gears ? a comparative study

Tan¹,

Irving²,

Mba³

2005

Insight - Non-Destructive Testing and Condition Monitoring

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Whilst vibration and spectrometric oil analysis for gear fault diagnosis are well established, the application of AE to this field is still in its infancy. This paper describes an experimental investigation on spur gears in which natural pitting was allowed to occur.Throughout the test period, AE, vibration and spectrometric oil samples were monitored continuously in order to correlate and compare these techniques to natural life degradation of the gears. It was observed that the AE technique was the most sensitive in detecting and monitoring pitting.

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Section: Resultsmentioning

confidence: 57%

Section: Resultsmentioning

confidence: 79%

Diagnostics and prognostics with acoustic emission, vibration and spectrometric oil analysis for spur gears ? a comparative study

Tan¹,

Irving²,

Mba³

2005

Insight - Non-Destructive Testing and Condition Monitoring

Self Cite

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show abstract

“…Traditionally, acoustic and vibration signals are widely used for machine condition monitoring and fault diagnosis due to their easy-to-measure characteristics and their useful information of machine state containing in the features for analysis. Some outstanding works referred in [1]- [6] have been successfully used these signals for machine fault detection and fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Fault Diagnosis of Rotating Machinery Using the Second-Order Statistical Features of Thermal Images and Simplified Fuzzy ARTMAP

Thobiani¹,

Tran²,

Tinga³

2017

ENG

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Thermal image, or thermogram, becomes a new type of signal for machine condition monitoring and fault diagnosis due to the capability to display real-time temperature distribution and possibility to indicate the machine's operating condition through its temperature. In this paper, an investigation of using the second-order statistical features of thermogram in association with minimum redundancy maximum relevance (mRMR) feature selection and simplified fuzzy ARTMAP (SFAM) classification is conducted for rotating machinery fault diagnosis. The thermograms of different machine conditions are firstly preprocessed for improving the image contrast, removing noise, and cropping to obtain the regions of interest (ROIs). Then, an enhanced algorithm based on bi-dimensional empirical mode decomposition is implemented to further increase the quality of ROIs before the second-order statistical features are extracted from their gray-level co-occurrence matrix (GLCM). The highly relevant features to the machine condition are selected from the total feature set by mRMR and are fed into SFAM to accomplish the fault diagnosis. In order to verify this investigation, the thermograms acquired from different conditions of a fault simulator including normal, misalignment, faulty bearing, and mass unbalance are used. This investigation also provides a comparative study of SFAM and other traditional methods such as back-propagation and probabilistic neural networks. The results show that the second-order statistical features used in this framework can provide a plausible accuracy in fault diagnosis of rotating machinery.

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“…Some outstanding works [1][2][3][4][5][6] have successfully employed these signals for fault detection and diagnosis areas. However, it is necessary to increase the fault diagnosis capability and implement suitable signals which intensify the fault detection ability for highly automatic rotating machinery.…”

Section: Introductionmentioning

confidence: 99%

Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis

Tran

Yang

et al. 2013

Mechanical Systems and Signal Processing

112

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In this study, a novel fault diagnosis system for rotating machinery using thermal imaging is proposed. This system consists of bi-dimensional empirical mode decomposition (BEMD) for image enhancement, a generalized discriminant analysis (GDA) for feature reduction, and a relevance vector machine (RVM) for fault classification. Firstly, the thermal image obtained from machine conditions is decomposed into intrinsic mode functions (IMFs) by using BEMD.At each decomposed level, the IMF is expanded and fused with the residue by grey-scale transformation and principal component analysis fusion technique, respectively. The enhanced image is then formed by the improved IMFs in reconstruction process. Subsequently, feature extraction is applied for the enhanced images to obtain histogram features which characterize the thermal image and contain useful information for diagnosis. The high dimensionality of the achieved feature set can be reduced by GDA implementation. Moreover, GDA also assists in the increase of the feature cluster separation. Finally, the diagnostic results are performed by RVM.The proposed system is applied and validated with the thermal images of a fault simulator. A comparative study of the classification results obtained from RVM, support vector machines, and adaptive neuro-fuzzy inference system is also performed to appraise the accuracy of these models. The results show that the proposed diagnosis system is capable of improving the classification accuracy and efficiently assisting in rotating machinery fault diagnosis.

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Application of acoustic emission to seeded gear fault detection

Cited by 129 publications

References 6 publications

Diagnostics and prognostics with acoustic emission, vibration and spectrometric oil analysis for spur gears ? a comparative study

Diagnostics and prognostics with acoustic emission, vibration and spectrometric oil analysis for spur gears ? a comparative study

An Approach to Fault Diagnosis of Rotating Machinery Using the Second-Order Statistical Features of Thermal Images and Simplified Fuzzy ARTMAP

Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis

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