Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems

Saxena, Abhinav; Saad, Ashraf

doi:10.1016/j.asoc.2005.10.001

Cited by 171 publications

(69 citation statements)

References 6 publications

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“…Although many studies have been carried out to investigate the use of NNs for automatic diagnosis of machinery condition, most of these methods have been proposed to deal with discrimination of fault types collectively (Saxena et al, 2007) (Samanta et al, 2003) (Li et al, 2006) (Alguindigue et al, 1993) (Samanta et al, 2006). However, the conventional NN cannot adequately reflect the possibility of ambiguous diagnosis problems, and will never converge when the first-layer parameters have the same values in different states (Bishop , 1996).…”

Section: Fuzzy Neural Networkmentioning

confidence: 99%

Intelligent Methods for Condition Diagnosis of Plant Machinery

Wang¹,

Chen²

2011

Intelligent Mechatronics

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Section: Fuzzy Neural Networkmentioning

confidence: 99%

Intelligent Methods for Condition Diagnosis of Plant Machinery

Wang¹,

Chen²

2011

Intelligent Mechatronics

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“…Results obtained from the study showed that SVM yielded higher classification accuracy than ANN without GA-based selection, but is equally accurate when using GA-based selection. Similarly, Saxena and Saad [5] also applied GA as a feature optimizer that determined the optimal number of "good" features for fault diagnosis. These features were then used as inputs to different ANN classifiers.…”

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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“…For example, Sadegh, et al [65], utilized ANN to classify lubrication condition and employed GA to search for an optimal feature space. Saxena, et al [66], used GA to select an optimal feature set, which was used as the input of ANN for mechanical fault classification. GA could successfully determine the desired number of good features in a large search space.…”

Section: Fault Classification Using Easmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems

Cited by 171 publications

References 6 publications

Intelligent Methods for Condition Diagnosis of Plant Machinery

Intelligent Methods for Condition Diagnosis of Plant Machinery

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

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

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