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Journal ArticleDOI

Modified self-organising map for automated novelty detection applied to vibration signal monitoring

01 Apr 2006-Mechanical Systems and Signal Processing (Elsevier)-Vol. 20, Iss: 3, pp 593-610
TL;DR: This novel MCM method is based on Kohonen's self-organising map and adopts a multidimensional dissimilarity measure for dual class classification and designed to be highly modular and scale well for a multi-sensor condition monitoring environment.
About: This article is published in Mechanical Systems and Signal Processing.The article was published on 2006-04-01. It has received 102 citations till now. The article focuses on the topics: Novelty detection & Condition monitoring.
Citations
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Journal ArticleDOI
TL;DR: A review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective is presented.

1,173 citations

Journal ArticleDOI
TL;DR: A novel multi-sensor health diagnosis method using deep belief network (DBN) that is compared with four existing diagnosis techniques to demonstrate the efficacy of the proposed approach.

527 citations


Cites methods from "Modified self-organising map for au..."

  • ...The SOM is trained using the architecture of 10 10 neurons....

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  • ...As also indicated by the diagnosis results in this case study, the SOM algorithm generally performs less accurately compared to all other algorithms, mainly due to its inefficiency in learning the non-linearly separable health conditions based on the complex sensory signals [53]....

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  • ...Significant advances have been achieved in applying classification techniques based on machine learning [27–34] or statistical inferences [35–37], which resulted in a number of state-of-the-art health state classification methods, such as back-propagation neural network (BNN) [27–30], self-organizing maps (SOM) [31], support vector machine (SVM) [28,32–34], and Mahalanobis distance (MD) [32,35]....

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  • ...Health diagnosis using DBN based health state classification technique is compared with four existing diagnosis techniques: SVM, BNN, SOM, and MD classifier....

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  • ...The 10 10 architecture of neurons is used to develop SOM models for all datasets....

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Journal ArticleDOI
TL;DR: The proposed method for fault diagnosis based on empirical mode decomposition (EMD), an improved distance evaluation technique and the combination of multiple adaptive neuro-fuzzy inference systems (ANFISs) show that the multiple ANFIS combination can reliably recognise different fault categories and severities.

406 citations


Cites methods from "Modified self-organising map for au..."

  • ...One of the principal tools for diagnosing rotating machinery problems is the vibration analysis [1–4]....

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  • ...Therefore, there is a demand for techniques that can make decision on the running health of the machine automatically and reliably [4–6]....

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Journal ArticleDOI
TL;DR: The proposed approach to intelligent fault diagnosis based on statistics analysis, an improved distance evaluation technique and adaptive neuro-fuzzy inference system (ANFIS) can reliably recognise different fault categories and severities.
Abstract: This paper presents a new approach to intelligent fault diagnosis based on statistics analysis, an improved distance evaluation technique and adaptive neuro-fuzzy inference system (ANFIS). The approach consists of three stages. First, different features, including time-domain statistical characteristics, frequency-domain statistical characteristics and empirical mode decomposition (EMD) energy entropies, are extracted to acquire more fault characteristic information. Second, an improved distance evaluation technique is proposed, and with it, the most superior features are selected from the original feature set. Finally, the most superior features are fed into ANFIS to identify different abnormal cases. The proposed approach is applied to fault diagnosis of rolling element bearings, and testing results show that the proposed approach can reliably recognise different fault categories and severities. Moreover, the effectiveness of the proposed feature selection method is also demonstrated by the testing results.

350 citations

Journal ArticleDOI
TL;DR: The diagnosis results show that the proposed method enables the identification of the single faults in the bearings and at the same time the recognition of the fault severities and the compound faults.
Abstract: Research highlights? EEMD and WNN are combined to propose an automated fault diagnosis method. ? Features are extracted from the sensitive IMF of EEMD in this method. ? The features are fed into WNN to identify the bearing health conditions. ? The method can identify the fault severities and the compound faults. The ensemble empirical mode decomposition (EEMD) can overcome the mode mixing problem of the empirical mode decomposition (EMD) and therefore provide more precise decomposition results. Wavelet neural network (WNN) possesses the advantages of both wavelet transform and artificial neural networks. This paper combines the merits of EEMD and WNN to propose an automated and effective fault diagnosis method of locomotive roller bearings. First, the vibration signals captured from the locomotive roller bearings are preprocessed by EEMD method and intrinsic mode functions (IMFs) are produced. Second, a kurtosis based method is presented and used to select the sensitive IMF. Third, time- and frequency-domain features are extracted from the sensitive IMF, its frequency spectrum and its envelope spectrum. Finally, these features are fed into WNN to identify the bearing health conditions. The diagnosis results show that the proposed method enables the identification of the single faults in the bearings and at the same time the recognition of the fault severities and the compound faults.

269 citations

References
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Book
01 Jan 1995
TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
Abstract: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981. Its theory and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it. The most important practical applications are in exploratory data analysis, pattern recognition, speech analysis, robotics, industrial and medical diagnostics, instrumentation, and control, and literally hundreds of other tasks. In this monograph the mathematical preliminaries, background, basic ideas, and implications are expounded in a manner which is accessible without prior expert knowledge.

12,920 citations

Journal ArticleDOI
01 Mar 1999
TL;DR: An overview and categorization of both old and new methods for the visualization of SOM is presented to give an idea of what kind of information can be acquired from different presentations and how the SOM can best be utilized in exploratory data visualization.
Abstract: The self-organizing map SOM is an efficient tool for visualization of multidimensional numerical data. In this paper, an overview and categorization of both old and new methods for the visualization of SOM is presented. The purpose is to give an idea of what kind of information can be acquired from different presentations and how the SOM can best be utilized in exploratory data visualization. Most of the presented methods can also be applied in the more general case of first making a vector quantization e.g. k-means and then a vector projection e.g. Sammon's mapping.

836 citations

Journal ArticleDOI
TL;DR: The performance of both types of classifiers in two-class fault/no-fault recognition examples are examined and the attempts to improve the overall generalisationperformance of both techniques through the use of genetic algorithm based feature selection process are examined.

363 citations

Journal ArticleDOI
TL;DR: It is shown that the SOM visualizes the similarity of genes in a more trustworthy way than two alternative methods, multidimensional scaling and hierarchical clustering.

178 citations

Book ChapterDOI
15 Sep 1999
TL;DR: It is demonstrated that the number of output units used in a self-organizing map (SOM) influences its applicability for either clustering or visualization, and it is shown that this flexibility comes with a price in terms of impaired performance.
Abstract: We show that the number of output units used in a self-organizing map (SOM) influences its applicability for either clustering or visualization. By reviewing the appropriate literature and theory and own empirical results, we demonstrate that SOMs can be used for clustering or visualization separately, for simultaneous clustering and visualization, and even for clustering via visualization. For all these different kinds of application, SOM is compared to other statistical approaches. This will show SOM to be a flexible tool which can be used for various forms of explorative data analysis but it will also be made obvious that this flexibility comes with a price in terms of impaired performance. The usage of SOM in the data mining community is covered by discussing its application in the data mining tools CLEMENTINE and WEBSOM.

157 citations