Thermal analysis MLP neural network based fault diagnosis on worm gears

Waqar, Tayyab; Demetgül, Mustafa

doi:10.1016/j.measurement.2016.02.024

Cited by 64 publications

(28 citation statements)

References 22 publications

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“…Gear condition can be monitored through process parameter measurement [27], vibratory [28,29], thermal [30], acoustic [31] and oil-based analysis [32]. Out of these, the use of vibrating signals in gear fault diagnosis has seen a surge in popularity due to its direct mensuration and rich systemcritical content [33].…”

Section: Vibration In Rotating Machinery With Gearmentioning

confidence: 99%

Efficient gear fault feature selection based on moth-flame optimisation in discrete wavelet packet analysis domain

Ong

Tieh

Lai

et al. 2019

J Braz. Soc. Mech. Sci. Eng.

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Rotating machinery-a crucial component in modern industry, requires vigilant monitoring such that any potential malfunction of its electromechanical systems can be detected prior to a fatal breakdown. However, identifying faulty signals from a defective rotating machinery is challenging due to complex dynamical behaviour. Therefore, the search for features which best describe the characteristic of different fault conditions is often crucial for condition monitoring of rotating machinery. For this purpose, this study used the intensification and diversification properties of the recently proposed moth-flame optimisation (MFO) algorithm and utilised the algorithm in the proposed feature selection scheme. The proposed method consisted of three parts. First, the vibration signals of gear with different fault conditions were decomposed by a fourth-level discrete wavelet packet transform, and the statistical features at all constructed nodes were derived. Second, the MFO algorithm was utilised to select the optimal discriminative features. Lastly, the MFO-selected features were used as the input for a support vector machine (SVM) diagnostic model to identify fault patterns. To further demonstrate the superiority of the proposed method, other feature selection approaches were applied, including randomly selected features and complete features, and other diagnostic models, namely the multilayer perceptron neural network and k-nearest neighbour. Comparative experiments demonstrated that SVM with the MFO-selected features outperformed the others, with the classification accuracy of 99.60%, thus validating its effectiveness.

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Section: Vibration In Rotating Machinery With Gearmentioning

confidence: 99%

Efficient gear fault feature selection based on moth-flame optimisation in discrete wavelet packet analysis domain

Ong

Tieh

Lai

et al. 2019

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…In the literature, few papers are reported that use infrared imaging for monitoring and diagnosis of gearbox failures. A thermal analysis was proposed based on MLP neural networks for the diagnosis of failures in helical gears [28]. Further, there was a review published discussing different studies with infrared imaging to detect failures in gearboxes [29], and infrared imaging was used as a complement to other techniques such as vibration signals and acoustic signals for the diagnosis of failures in a gearbox [30].…”

Section: Introductionmentioning

confidence: 99%

Novel Methodology for Condition Monitoring of Gear Wear Using Supervised Learning and Infrared Thermography

et al. 2020

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In gearboxes, the occurrence of unexpected failures such as wear in the gears may occur, causing unwanted downtime with significant financial losses and human efforts. Nowadays, noninvasive sensing represents a suitable tool for carrying out the condition monitoring and fault assessment of industrial equipment in continuous operating conditions. Infrared thermography has the characteristic of being installed outside the machinery or the industrial process under assessment. Also, the amount of information that sensors can provide has become a challenge for data processing. Additionally, with the development of condition monitoring strategies based on supervised learning and artificial intelligence, the processing of signals with significant improvements during the classification of information has been facilitated. Thus, this paper proposes a novel noninvasive methodology for the diagnosis and classification of different levels of uniform wear in gears through thermal analysis with infrared imaging. The novelty of the proposed method includes the calculation of statistical time-domain features from infrared imaging, the consideration of a dimensionality reduction stage by means of Linear Discriminant Analysis, and automatic fault diagnosis performed by an artificial neural network. The proposed method is evaluated under an experimental laboratory data set, which is composed of the following conditions: healthy, and three severity degrees of uniform wear in gears, namely, 25%, 50%, and 75% of uniform wear. Finally, the obtained results are compared with classical condition monitoring approaches based on vibration analysis.

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“…e related features selected by the proposed method can be used directly to analyze bearing signals for fault classification and defect severity identification avoiding the impact of irrelevant features on the premise of keeping the original feature state. e proposed method outperforms the traditional principal component analysis (PCA) [29], kernel principal component analysis (KPCA) [30], and locally linear embedding (LLE) [31] dimension reduction methods [32,33]. e intellectual merits of this paper rest on two folds.…”

Section: Introductionmentioning

confidence: 99%

Association Rule-Based Feature Mining for Automated Fault Diagnosis of Rolling Bearing

Wang

Duan

et al. 2019

Shock and Vibration

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Effective and efficient diagnosis methods are highly demanded to improve system reliability. Comparing with conventional fault diagnosis methods taking a forward approach (e.g., feature extraction, feature selection, and fusion, and then fault diagnosis), this paper presents a new association rule mining method which provides an inverse approach unearthing the underlying relation between labeled defects and extracted features for bearing fault analysis. Instead of evenly dividing methods used in traditional association rule mining, a new association rule mining approach based on the equal probability discretization method is presented in this study. First, a series of extracted features of signal data are discretized following the guideline of equalized probability distribution of the data in order to avoid excessive concentration or decentralized data. Next, the data matrix composed of arrays of discretized features and defect labels is exploited to generate the association rules representing the relation between the features and fault types. Experimental study on a bearing test reveals that the proposed method can generate a series of underlying association rules for bearing fault diagnosis, and the related features selected by the proposed method can be used directly to analyze bearing signals for fault classification and defect severity identification. As a new feature selection method, it possesses prominent superiority compared to traditional PCA, KPCA, and LLE dimension reduction methods.

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Thermal analysis MLP neural network based fault diagnosis on worm gears

Cited by 64 publications

References 22 publications

Efficient gear fault feature selection based on moth-flame optimisation in discrete wavelet packet analysis domain

Efficient gear fault feature selection based on moth-flame optimisation in discrete wavelet packet analysis domain

Novel Methodology for Condition Monitoring of Gear Wear Using Supervised Learning and Infrared Thermography

Association Rule-Based Feature Mining for Automated Fault Diagnosis of Rolling Bearing

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