Fault detection and fuzzy rule extraction in AC motors by a neuro-fuzzy ART-based system

Sainz-Palmero, G.I.; Santamarı́a, Javier; Torre, Eduardo Julio Moya de la; González, José Ramón Perán

doi:10.1016/j.engappai.2005.02.005

Cited by 37 publications

(15 citation statements)

References 19 publications

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“…The FasArt system has been used in several previous works (Sainz Palmero et al 2005;Sainz et al 2004) for modeling, fault detection, pattern recognition, etc., with reasonable results when its accuracy as a fuzzy model is involved; but when other aspects, such as rule interpretability, are important, then some problems appear: proliferation of rules, fuzzy sets, etc., so this system is an adequate instance for checking this proposal, taking advantage of the knowledge learnt and stored by FasArt for each problem involved. This aspect is important in analyzing the results and its comparison with other algorithms.…”

Section: Neuro-fuzzy System Fasartmentioning

confidence: 99%

Complexity reduction and interpretability improvement for fuzzy rule systems based on simple interpretability measures and indices by bi-objective evolutionary rule selection

2011

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The aim of this paper is to develop a general post-processing methodology to reduce the complexity of data-driven linguistic fuzzy models, in order to reach simpler fuzzy models preserving enough accuracy and better fuzzy linguistic performance with respect to their initial values. This post-processing approach is based on rule selection via the formulation of a bi-objective problem with one objective focusing on accuracy and the other on interpretability. The latter is defined via the aggregation of several interpretability measures, based on the concepts of similarity and complexity of fuzzy systems and rules. In this way, a measure of the fuzzy model interpretability is given. Two neuro-fuzzy systems for providing initial fuzzy models, Fuzzy Adaptive System ART based and NeuroFuzzy Function Approximation and several case studies, data sets from KEEL Project Repository, are used to check this approach. Both fuzzy and neuro-fuzzy systems generate Mamdani-type fuzzy rule-based systems, each with its own particularities and complexities from the point of view of the fuzzy sets and the rule generation. Based on these systems and data sets, several fuzzy models are generated to check the performance of the proposal under different restrictions of complexity and fuzziness.

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Section: Neuro-fuzzy System Fasartmentioning

confidence: 99%

Complexity reduction and interpretability improvement for fuzzy rule systems based on simple interpretability measures and indices by bi-objective evolutionary rule selection

2011

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“…8 Fault database. Faults 1,3,6,9,12,15,18, and 21 are introduced into the system one at a time at the time interval of 100s. In each case, the fault identified by the proposed FD technique at each time interval is shown in Figs.9∼16.…”

Section: Fault Diagnosismentioning

confidence: 99%

“…This approach allows the operators to include and to extract qualitative knowledge from the network for FD. Another type of NFN, referred to as the fuzzy adaptive system ART-based network (FasART), has also been applied to developing FD techniques, and has been successfully applied for FD of AC motors [9]. An FD scheme based on NFN and with robust optimal decoupling of observers was presented in [10].…”

Section: Introductionmentioning

confidence: 99%

An intelligent online fault diagnostic scheme for nonlinear systems

Mok¹,

Chan

Yang

2008

J. Control Theory Appl.

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An online fault diagnostic scheme for nonlinear systems based on neurofuzzy networks is proposed in this paper. The scheme involves two stages. In the first stage, the nonlinear system is approximated by a neurofuzzy network, which is trained offline from data obtained during the normal operation of the system. In the second stage, residual is generated online from this network and is modelled by another neurofuzzy network trained online. Fuzzy rules are extracted from this network, and are compared with those in the fault database obtained under different faulty operations, from which faults are diagnosed. The performance of the proposed intelligent fault scheme is illustrated using a two-tank water level control system under different faulty conditions.

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“…Globally, the main goals of fault diagnosis systems for CAD [5,10] are: to detect if a fault is in progress as soon as possible, to classify the fault in progress, to be able to suggest suitable remedies (systems able of advising) or to give a reliability rate of the identified fault through a Confidence Index (CI).…”

Section: Fault Diagnosis System Analysismentioning

confidence: 99%

“…In many applications of interest, it is desirable for the system to not only identify the possible causes of the problem, but also to suggest suitable remedies (systems capable of advising) or to give a reliability rate of the identification of possible causes. Recently, several decision support systems and intelligent systems have been developed [2,3] and the diagnosis approaches based on such intelligent systems have been developed for industrial applications [1,4,5], and biomedicine applications [6][7][8][9][10]. Currently, one of the most used approaches to feature identification, classification, and decision-making problems inherent to fault detection and diagnosis, is soft computing implying mainly neural networks and fuzzy logic [1,[3][4][5][6]9,10].…”

Section: Introductionmentioning

confidence: 99%

Hybrid intelligent diagnosis approach based on soft computing from signal and image knowledge representations for a biomedical application

Chohra

Kanaoui

Amarger

et al. 2014

Vietnam J Comput Sci

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Fault diagnosis is a complex and fuzzy cognitive process, and soft computing methods as neural networks and fuzzy logic, have shown great potential in the development of decision support systems. Dealing with expert (human) knowledge consideration, Computer-Aided Diagnosis (CAD) dilemma is one of the most interesting, but also one of the most difficult problems. Among difficulties contributing to challenging nature of this problem, one can mention the need of fine classification and decision-making. In this paper, a brief survey on fault diagnosis systems is given first. Then, from a fault diagnosis system analysis of the classification and decision-making problem, a global diagnosis synopsis is deduced. Afterwards, a hybrid intelligent diagnosis approach, based on soft computing implying modular neural networks for classification and fuzzy logic for decision-making, is suggested from signal and image representations. The suggested approach is developed in biomedicine for a CAD, from Auditory Brainstem Response test, and the prototype design and experimental results are presented. In fact, a double classification is exploited in a primary fuzzy diagnosis, to ensure a satisfactory reliability. Then, this reliability is reinforced using a confidence parameter with the primary diagnosis result, exploited in a final fuzzy diagnosis giving the appropriate diagnosis with a Indeed, experimental results demonstrate the efficiency and reliability of CAD for three classes: two auditory pathologies Retro-cochlear Class (RC) and Endocochlear Class (EC), and Normal auditory Class (NC). The generalization rate of NC is clearly higher for primary fuzzy diagnosis and final fuzzy diagnosis than that of the two classifications. The obtained rates for RC and EC are higher than obtained by image classification but quite similar than those obtained by signal classification. An important contribution of the final fuzzy diagnosis is the fact that a confidence index is associated with each fault diagnosis. Finally, a discussion is given with regard to the reliability and large application field of the suggested approach.

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Fault detection and fuzzy rule extraction in AC motors by a neuro-fuzzy ART-based system

Cited by 37 publications

References 19 publications

Complexity reduction and interpretability improvement for fuzzy rule systems based on simple interpretability measures and indices by bi-objective evolutionary rule selection

Complexity reduction and interpretability improvement for fuzzy rule systems based on simple interpretability measures and indices by bi-objective evolutionary rule selection

An intelligent online fault diagnostic scheme for nonlinear systems

Hybrid intelligent diagnosis approach based on soft computing from signal and image knowledge representations for a biomedical application

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