GFRBS-PHM: A Genetic Fuzzy Rule-Based System for PHM with improved interpretability

Ishibashi, Rogério; Nascimento, Cairo Lúcio

doi:10.1109/icphm.2013.6621419

Cited by 19 publications

(12 citation statements)

References 10 publications

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“…Therefore, during the last few years many researchers focused on obtaining a compromise between accuracy and interpretability of fuzzy systems (see, e.g., Zhou and Gan, 2008;Casillas et al, 2003;Di Nuovo and Ascia, 2013;Ishibashi and Lucio Nascimento, Jr., 2013;Shukla and Tripathi, 2013;Juang and Chen, 2013;Lughofer, 2013;Johansen et al, 2000).…”

Section: 2mentioning

confidence: 99%

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A new approach to nonlinear modelling of dynamic systems based on fuzzy rules

Bartczuk

Przybył

Cpałka

2016

International Journal of Applied Mathematics and Computer Science

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For many practical weakly nonlinear systems we have their approximated linear model. Its parameters are known or can be determined by one of typical identification procedures. The model obtained using these methods well describes the main features of the system's dynamics. However, usually it has a low accuracy, which can be a result of the omission of many secondary phenomena in its description. In this paper we propose a new approach to the modelling of weakly nonlinear dynamic systems. In this approach we assume that the model of the weakly nonlinear system is composed of two parts: a linear term and a separate nonlinear correction term. The elements of the correction term are described by fuzzy rules which are designed in such a way as to minimize the inaccuracy resulting from the use of an approximate linear model. This gives us very rich possibilities for exploring and interpreting the operation of the modelled system. An important advantage of the proposed approach is a set of new interpretability criteria of the knowledge represented by fuzzy rules. Taking them into account in the process of automatic model selection allows us to reach a compromise between the accuracy of modelling and the readability of fuzzy rules.

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Section: 2mentioning

confidence: 99%

“…Interpretability of fuzzy models can be provided in many ways, but restrictions on the learning process are imposed most commonly (see, e.g., Lughofer, 2013;Cpałka et al, 2014;Shukla and Tripathi, 2013;Ishibashi and Lucio Nascimento, Jr., 2013).…”

Section: 2mentioning

confidence: 99%

A new approach to nonlinear modelling of dynamic systems based on fuzzy rules

Bartczuk

Przybył

Cpałka

2016

International Journal of Applied Mathematics and Computer Science

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“…A GFRBS (Genetic Fuzzy Rule Based System) assisted by a Decision Tree was presented by the authors in two previous works in [11] and [12]. In both works, a GFRBS with a single objective optimization was used with the following three steps:…”

Section: Mogft-imentioning

confidence: 99%

“…6. The main difference from the proposed approach by authors in [11] and [12] is that the output is now composed by a set of solutions forming the Pareto-optimal front. A tradeoff analysis can then be performed between any two solutions in the resulting Pareto-optimal front in order to select a solution that best fits with the user purposes.…”

Section: Mogft-imentioning

confidence: 99%

MoGFT-I: A Multi-objective Optimization approach for the Cart and Pole control problem

Ishibashi

Nascimento

2015

2015 Annual IEEE Systems Conference (SysCon) Proceedings

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In this article a set of well-known computational intelligence techniques such as Decision Trees, Fuzzy Logic, and Multi-objective Optimization Genetic Algorithm are combined to generate a novel hybrid method which is called MoGFT-I: Multi-objective Genetic Fuzzy Rule Based System supported by a Decision Tree with Improved Interpretability. The output of the proposed supervised learning method is a set of Mamdani-type fuzzy systems which are optimized and distributed along a Pareto curve by considering two conflicting attributes: accuracy and interpretability. The MoGFT-I method is then applied to the Cart and Pole control problem such that a set of feedback controller are designed to control this unstable nonlinear dynamical system.

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“…Stetter, et al [103], used fuzzy logic to monitor the health status of the pump system in an engine. Ishibashi, et al [104], combined decision tree, fuzzy logic, and GA for RUL prediction of aeroengine, and acquired the historical data from the sensor on the aeroengine. Tian, et al [105], proposed a fuzzy-adaptive unscented Kalman filter to improve the prediction accuracy of nonlinear processes.…”

Section: Fuzzy Logicmentioning

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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GFRBS-PHM: A Genetic Fuzzy Rule-Based System for PHM with improved interpretability

Cited by 19 publications

References 10 publications

A new approach to nonlinear modelling of dynamic systems based on fuzzy rules

A new approach to nonlinear modelling of dynamic systems based on fuzzy rules

MoGFT-I: A Multi-objective Optimization approach for the Cart and Pole control problem

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

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