Fault identification in rotating machinery using artificial neural networks

Nahvi, Hassan; Esfahanian, Mohsen

doi:10.1243/095440605x8469

Cited by 18 publications

(18 citation statements)

References 8 publications

(6 reference statements)

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“…Artificial neural networks (ANN) have been beneficially applied in mechanical systems due their suitability for complex sensor data processing problems [1]. Nahvi and Esfahanian [2] have evidenced the capability of ANNs of dealing with a vast number of features and providing acceptable results for the fault detection, however the 100% of accuracy is not achieved at any of their conducted experiments. Bearing faults have been also studied by this method [3,4], demonstrating an ability for damage detection at an early stage.…”

Section: Introductionmentioning

confidence: 99%

Comparison of machine learning models based on time domain and frequency domain features for faults diagnosis in rotating machines

Sepulveda

Sinha

2018

MATEC Web Conf.

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The development of technologies for the maintenance industry has taken an important role to meet the demanding challenges. One of the important challenges is to predict the defects, if any, in machines as early as possible to manage the machines downtime. The vibration-based condition monitoring (VCM) is well-known for this purpose but requires the human experience and expertise. The machine learning models using the intelligent systems and pattern recognition seem to be the future avenue for machine fault detection without the human expertise. Several such studies are published in the literature. This paper is also on the machine learning model for the different machine faults classification and detection. Here the time domain and frequency domain features derived from the measured machine vibration data are used separated in the development of the machine learning models using the artificial neutral network method. The effectiveness of both the time and frequency domain features based models are compared when they are applied to an experimental rig. The paper presents the proposed machine learning models and their performance in terms of the observations and results.

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Section: Introductionmentioning

confidence: 99%

Comparison of machine learning models based on time domain and frequency domain features for faults diagnosis in rotating machines

Sepulveda

Sinha

2018

MATEC Web Conf.

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“…It is an important step in designing intelligent fault diagnosis systems. For multi-class fault diagnosis problem, feature selection methods used in most previous studies usually select a single shared feature subset to discriminate all pairs of classes [2][3][4][5][6][7][8][9]. One popular method is the distance evaluation technique, which has been applied in many recent papers for its simplicity and reliability [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, various intelligent classification algorithms have been successfully applied to automatic diagnosis of machine conditions, such as artificial neural networks (ANN) [2][3][4][5][6][7], support vector machines (SVM) [8][9][10][11][12], relevance vector machines (RVM) [13][14][15], and so on. ANN is a kind of multiinput multi-output model which can only use the SFS method.…”

Section: Introductionmentioning

confidence: 99%

Intelligent fault diagnosis of rotating machinery based on multiple relevance vector machines with variance radial basis function kernel

Liu

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part C:

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Automatic and accurate fault diagnosis is very important for condition-based maintenance. In this study, an intelligent fault diagnosis method based on relevance vector machines (RVM) is proposed for automatic fault diagnosis of rotating machinery. First, the global optimal features from all node energies of full wavelet packet tree are obtained by combining wavelet packet transform with an improved Fisher feature selection method. Individual salient feature subsets are selected for each pair of classes separately. Then, RVM method is adopted to train the intelligent fault diagnosis model. The multi-class RVM classifier is constructed by combining several RVM binary classifiers using 'max-probability-win' strategy. Moreover, improved from Gaussian radial basis function, a new kernel function denoted variance radial basis function is developed and used for RVM to adaptively balance the difference between the scales of different features. The proposed method was carried out to develop a multi-class bearing fault diagnosis model under varying load conditions, resulting in high accuracy around 99.58 per cent. Experimental results demonstrate that the proposed method is promising for intelligent fault diagnosis of rotating machinery.

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“…Moreover, the limited human resource of expert personnel restricts a widespread application of the fault diagnosis techniques. As such, in order to help inexperienced operators to quickly make an objective and accurate decision on machinery running conditions, a wide variety of automated classification paradigms have been introduced for fault classification such as expert systems (ESs), fuzzylogic inference [25,26], NNs [6,[27][28][29][30][31][32][33][34][35], and SVMs [36][37][38][39][40]. ESs may be the earliest attempt made to automate fault diagnosis, which documents the expertise of human experts into a computer system and emulates human reasoning process.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis based on optimized node entropy using lifting wavelet packet transform and genetic algorithms

Zhang

Xiong

Liu

et al. 2010

Proceedings of the Institution of Mechanical Engineers, Part I:

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The current paper presents a novel scheme for bearing fault diagnosis based on lifting wavelet packet transform (LWPT), sample entropy (SampEn), support vector machines (SVMs), and genetic algorithms (GAs). In the proposed scheme, bearing vibration signals were first decomposed into different frequency sub-bands through a four-level LWPT, resulting in a total of 31 node signal components throughout all layers of the LWPT decomposition tree. The SampEns of all 31 components were then calculated as an original feature pool to characterize the complexity of the bearing vibration signals within the corresponding frequency bands. For selecting the most informative features thus reducing the number of features, a GA was applied to simultaneously pick out the salient features and optimize the parameters of SVMs so as to avoid the curse of dimensionality, alleviate computational burden, and improve the subsequent classification. Experiments were conducted on an induction motor with respect to various bearing faults and a range of fault severities. As an example here, three salient features were selected from the original 31 features, with the dimension of features reduced dramatically. The selected three features were then presented into the optimized SVM to identify various bearing conditions. The scheme was compared with the widely used WPT-Energy method and with a neural network classifier with respect to feature extractions and fault classifications, respectively. The results are in favour of the proposed scheme in terms of the feature dimension, number of support vectors, robustness to the data partition, and classification rate.

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Fault identification in rotating machinery using artificial neural networks

Cited by 18 publications

References 8 publications

Comparison of machine learning models based on time domain and frequency domain features for faults diagnosis in rotating machines

Comparison of machine learning models based on time domain and frequency domain features for faults diagnosis in rotating machines

Intelligent fault diagnosis of rotating machinery based on multiple relevance vector machines with variance radial basis function kernel

Fault diagnosis based on optimized node entropy using lifting wavelet packet transform and genetic algorithms

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