From real data to remaining useful life estimation : an approach combining neuro-fuzzy predictions and evidential Markovian classifications.

International audienceThis paper deals with the proposition of a prognostic approach that enables to face up to the problem of lack of information and missing prior knowledge. Developments rely on the assumption that real data can be gathered from the system (online). The approach consists in three phases. An information theory-based criterion is first used to isolate the most useful observations with regards to the functioning modes of the system (feature selection step). An evolving neuro-fuzzy system is then used for online prediction of observations at any horizons (prediction step). The predicted observations are classified into the possible functioning modes using an evidential Markovian classifier based on Dempster-Shafer theory (classification step). The whole is illustrated on a problem concerning the prediction of an engine health. The approach appears to be very efficient since it enables to early but accurately estimate the failure instant, even with few learning data

Proceedings. 26. Workshop Computational Intelligence, Dortmund, 24. - 25. November 2016

Dieser Tagungsband enthält die Beiträge des 26. Workshops Computational Intelligence. Die Schwerpunkte sind Methoden, Anwendungen und Tools für Fuzzy-Systeme, Künstliche Neuronale Netze, Evolutionäre Algorithmen und Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen und Benchmark-Problemen

Remaining Useful Life Estimation by Classification of Predictions Based on a Neuro-Fuzzy System and Theory of Belief Functions.

International audienceVarious approaches for prognostics have been developed, and data-driven methods are increasingly applied. The training step of these methods generally requires huge datasets to build a model of the degradation signal, and estimate the limit under which the degradation signal should stay. Applicability and accuracy of these methods are thereby closely related to the amount of available data, and even sometimes requires the user to make assumptions on the dynamics of health states evolution. Following that, the aim of this paper is to propose a method for prognostics and remaining useful life estimation that starts from scratch, without any prior knowledge. Assuming that remaining useful life can be seen as the time between the current time and the instant where the degradation is above an acceptable limit, the proposition is based on a classification of prediction strategy (CPS) that relies on two factors. First, it relies on the use of an evolving real-time neuro-fuzzy system that forecasts observations in time. Secondly, it relies on the use of an evidential Markovian classifier based on Dempster-Shafer theory that enables classifying observations into the possible functioning modes. This approach has the advantage to cope with a lack of data using an evolving system, and theory of belief functions. Also, one of the main assets is the possibility to train the prognostic system without setting any threshold. The whole proposition is illustrated and assessed by using the CMAPPS turbofan dataset. RUL estimates are shown to be very close to actual values, and the approach appears to accurately estimate the failure instants, even with few learning data

Towards an Online Fuzzy Modeling for Human Internal States Detection

International audienceIn human-robot interaction, a social intelligent robot should be capable of understanding the emotional internal state of the interacting human so as to behave in a proper manner. The main problem towards this approach is that human internal states can't be totally trained on, so the robot should be able to learn and classify emotional states online. This research paper focuses on developing a novel online incremental learning of human emotional states using Takagi-Sugeno (TS) fuzzy model. When new data is present, a decisive criterion decides if the new elements constitute a new cluster or if they confirm one of the previously existing clusters. If the new data is attributed to an existing cluster, the evolving fuzzy rules of the TS model may be updated whether by adding a new rule or by modifying existing rules according to the descriptive potential of the new data elements with respect to the entire existing cluster centers. However, if a new cluster is formed, a corresponding new TS fuzzy model is created and then updated when new data elements get attributed to it. The subtractive clustering algorithm is used to calculate the cluster centers that present the rules of the TS models. Experimental results show the effectiveness of the proposed method