Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine

Saimurugan, M.; Ramachandran, K. I.; Sugumaran, V.; Sakthivel, N. R.

doi:10.1016/j.eswa.2010.09.042

Cited by 167 publications

(58 citation statements)

References 18 publications

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“…In addition to the aforementioned machine learning-based algorithms, statistical inference-based algorithms can also be used to classify system HSs based on statistical distances such as Mahalanobis distance [15], k-nearest neighbor method [16] and k-mean clustering [17]. Significant advancements in diagnostics area have been achieved by applying classification techniques based on machine learning or statistical inferences, resulting in a number of classification methods, such as back-propagation neural networks [18][19][20][21], deep belief networks [22,23], support vector machines [24][25][26][27][28], self-organizing maps [29], and Mahalanobis distance (MD) [15]. Some researchers combined two or more existing techniques to form hybrid models to achieve better diagnostic performance.…”

Section: Introductionmentioning

confidence: 99%

Reliability-based robust design of smart sensing systems for failure diagnostics using piezoelectric materials

Wang

Youn

et al. 2015

Computers & Structures

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a b s t r a c tThis paper presents a reliability-based robust design approach to develop piezoelectric materials based structural sensing systems for failure diagnostics and prognostics. A detectability measure is defined to evaluate the performance of any given sensing system, and the sensing system design problem can be formulated to maximize detectability for different failure modes by optimally allocating piezoelectric materials into a target structure. This formulation can be conveniently solved within a reliability-based robust design framework to ensure design robustness while considering the uncertainties such as those from structure properties and operation conditions. Two case studies, that design sensor networks for an aircraft wing panel and a power transformer structure, are employed to demonstrate the effectiveness of the proposed methodology in developing multifunctional material sensing systems.

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Section: Introductionmentioning

confidence: 99%

Reliability-based robust design of smart sensing systems for failure diagnostics using piezoelectric materials

Wang

Youn

et al. 2015

Computers & Structures

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“…SVM is a supervised, kernel-based nonparametric statistical learning algorithm, in which the learning machine is given a set of features (or inputs) with the associated labels (or output) [14]. Based on the Vapnik-Chervonenkis (VC) theory (or VC dimension) and structure risk minimization principle [15,16], SVM algorithm shows great advantages that projecting available data into a larger-dimensional space where a linear classification becomes possible or summarizing the available data with fewer "the support vectors" [17,18].…”

Section: Introductionmentioning

confidence: 99%

Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines

Zhang

Shen

et al. 2015

Int J CARS

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“…Saimurugan et al [7] demonstrated work on detecting multi-component faults in rotating machinery considering both shaft and bearing. In this work, a decision tree (DT) was used to select the best features, which were then classified by four different SVM kernel functions.…”

Section: Existing Workmentioning

confidence: 99%

Fault diagnosis of rotating machinery using wavelet-based feature extraction and support vector machine classifier

Tran¹,

Noori²,

Altabey³

et al. 2017

High Speed Machining

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Modern machine tools with high speed machining capabilities could place rotating shafts, gears, and bearings under extreme thermal, static, and impact stresses, potentially increasing their failure rates. In this research, a gearbox damage detection strategy based on discrete wavelet transform (DWT), wavelet packet transform (WPT), support vector machine (SVM), and artificial neural networks (ANN) is presented. Three case studies are conducted to compare the classification performance of SVM kernel functions and ANN. First, a fault detection analysis based on DWT and WPT is carried out to extract the damage information from the gearbox's raw vibration signal. In this step, wavelet coefficients obtained from DWT are characterized using statistical calculations. Energy characteristics of the gearbox signal are acquired using WPT and their statistical characteristics are also computed. These three sets of information extracted from wavelet transforms are utilized as the input to SVM and ANN classifiers. Secondly, the improved distance evaluation technique (IDE) is implemented to select the sensitive input features for SVM and ANN. The penalty parameter C and kernel parameter in SVM are also optimized using the grid-search method. Finally, the optimized features and parameters are input into SVM and ANN algorithms to detect gearbox damage. The result shows that gearbox damage detection using energy characteristics ex- tracted from WPT (Case 2) or their statistical values as input features (Case 3) to the learning algorithms produces higher classification accuracies than using statistical values of the DWT coefficients as inputs (Case 1). In addition, RBF-SVM has the best classification performance in Case 2 and 3 while Linear-SVM has the best classification accuracy rate in Case 1 in damage detection average.

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Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine

Cited by 167 publications

References 18 publications

Reliability-based robust design of smart sensing systems for failure diagnostics using piezoelectric materials

Reliability-based robust design of smart sensing systems for failure diagnostics using piezoelectric materials

Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines

Fault diagnosis of rotating machinery using wavelet-based feature extraction and support vector machine classifier

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