Guest Editorial Special Issue on Artificial Neural Networks and Statistical Pattern Recognition

Jain, Achin; Mao, Jianchang

doi:10.1109/tnn.1997.554186

Cited by 23 publications

(12 citation statements)

References 4 publications

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“…Recently, many benchmark and comparison studies have been published on basic classification performance (e.g. see Ripley, 1994;Jain and Mao, 1997). However, two particular problems distinguish many CMFD applications from generic classifier systems: 1) 'Multiple faults' can occur.…”

Section: Selecting Classifiers For Cmfd Applicationsmentioning

confidence: 99%

“…While some previous studies have sought to compare the performance of these two popular classifiers Mak et al, 1994;Ripley, 1994;Jain and Mao, 1997;Looney, 1997), the present study differs from previously published work in this area in two key respects: 1) the focus of the paper is on aspects of classifier performance which relate to the design of condition monitoring and fault diagnosis (CMFD) applications; 2) the paper is particularly concerned with the design and implementation of embedded CMFD applications.…”

Section: Introductionmentioning

confidence: 99%

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Applying MLP and RBF classifiers in embedded condition monitoring and fault diagnosis systems

Pont

Jones

et al. 2001

Transactions of the Institute of Measurement and Control

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In this paper, results are presented from a comprehensive series of studies aimed at assessing the suitability of multilayered perceptron (MLP) and radial basis function (RBF) networks for use in embedded, microcontroller-based, condition monitoring and fault diagnosis (CMFD) applications. Our assessment criteria include the performance of each classifier on a range of CMFD-related problems, such as situations where there may be multiple faults present simultaneously, or where 'unknown' faults may occur. In addition, the processor and memory requirements of each classifier are compared and discussed. On the basis of the results obtained in these studies, it is argued that each form of classifier has both strengths and weaknesses, and that neither is suitable for use in all CMFD applications. The paper concludes by demonstrating that, where memory and processor limits allow, the best performance may be obtained through use of a fusion classifier containing both MLP and RBF components.

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Section: Selecting Classifiers For Cmfd Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying MLP and RBF classifiers in embedded condition monitoring and fault diagnosis systems

Pont

Jones

et al. 2001

Transactions of the Institute of Measurement and Control

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“…Artificial neural networks (ANN) have potential applications in automated detection and diagnosis of machine conditions [21,22]. Multi-layer perceptions (MLPs) and radial basis functions (RBFs) are the most commonly used ANNs [23,24], though interest in probabilistic neural networks (PNNs) is also increasing recently.…”

Section: Introductionmentioning

confidence: 99%

Gear fault detection and diagnosis under speed-up condition based on order cepstrum and radial basis function neural network

Zhang

Zheng

2009

J Mech Sci Technol

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Varying speed machinery condition detection and fault diagnosis are more difficult due to non-stationary machine dynamics and vibration. Therefore, most conventional signal processing methods based on time invariant carried out in constant time interval are frequently unable to provide meaningful results. In this paper, a study is presented to apply order cepstrum and radial basis function (RBF) artificial neural network (ANN) for gear fault detection during speedup process. This method combines computed order tracking, cepstrum analysis with ANN. First, the vibration signal during speed-up process of the gearbox is sampled at constant time increments and then is re-sampled at constant angle increments. Second, the re-sampled signals are processed by cepstrum analysis. The order cepstrum with normal, wear and crack fault are processed for feature extracting. In the end, the extracted features are used as inputs to RBF for recognition. The RBF is trained with a subset of the experimental data for known machine conditions. The ANN is tested by using the remaining set of data. The procedure is illustrated with the experimental vibration data of a gearbox. The results show the effectiveness of order cepstrum and RBF in detection and diagnosis of the gear condition.

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“…Cepstrum coefficients are derived from the linear predictor coefficients [9], which are extracted from each frame by the auto-correlation method and Durbin's recursive procedure. The ANN has potential applications in automated detection and in the diagnosis of machine conditions [12]. By combining these two techniques, a reliable automatic motor fault diagnosis system employing cepstrum transform to extract features from segmented motor vibration information is proposed.…”

Section: Introductionmentioning

confidence: 99%

Application of cepstrum and neural network to bearing fault detection

2009

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This paper proposes an integrated system for motor bearing diagnosis that combines the cepstrum coefficient method for feature extraction from motor vibration signals and artificial neural network (ANN) models. We divide the motor vibration signal, obtain the corresponding cepstrum coefficients, and classify the motor systems through ANN models. Utilizing the proposed method, one can identify the characteristics hiding inside a vibration signal and classify the signal, as well as diagnose the abnormalities. To evaluate this method, several tests for the normal and abnormal conditions were performed in the laboratory. The results show the effectiveness of cepstrum and ANN in detecting the bearing condition. The proposed method successfully extracted the corresponding feature vectors, distinguished the difference, and classified bearing faults correctly.

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Guest Editorial Special Issue on Artificial Neural Networks and Statistical Pattern Recognition

Cited by 23 publications

References 4 publications

Applying MLP and RBF classifiers in embedded condition monitoring and fault diagnosis systems

Applying MLP and RBF classifiers in embedded condition monitoring and fault diagnosis systems

Gear fault detection and diagnosis under speed-up condition based on order cepstrum and radial basis function neural network

Application of cepstrum and neural network to bearing fault detection

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