Application of Fuzzy Decision Tree for Signal Classification

Vibroengineering PROCEDIA, 2017

There is a strong need to use different methods for maintaining high product quality and safety production line, given the rapid technological progress. Thus, the condition monitoring is widely used as an efficient method in various industries. A variety of methods have been used so far in order to implement condition monitoring system, the most common one has been seismic waves analysis in the time-frequency domain. The current paper proposes a Fuzzy Inference System for monitoring the status of the compressor based on Daubechies wavelet transform and decision trees. The J48 algorithm was used as a tool for classification design and selection of effective features on troubleshooting. The J48 algorithm output results showed that signal processing technique by Daubechies wavelet mother has the highest accuracy for the implementation of the FIS system. The J48 algorithm output is a decision tree used on production of if-then rules and fuzzy set membership functions of the system. Fina...

IFAC Proceedings Volumes, 2009

Process diagnosis can be defined as the identification of the system's functional states. When a mathematical model is difficult or not possible to obtain (which is generally the case for complex chemical processes), knowledge on the process behaviour can be extracted from historical measurements or complex simulators. This knowledge is then organized as a partition of the data set into classes representing the functional states of the process (normal or faulty operations). Among the data mining techniques, those including fuzzy logic present the advantages to express the data membership degree to several classes. This article presents a methodology that automatically finds an optimal space partition in terms of clusters compactness and separation using only the membership matrix obtained from a fuzzy classification. Test and validation results of this methodology applied to a continuous intensified chemical micro-reactor will be presented.

Annals of Nuclear Energy, 2009

This paper extends a method previously introduced by the authors for building a transparent fault classification algorithm by combining the fuzzy clustering, fuzzy logic and decision trees techniques. The baseline method transforms an opaque, fuzzy clustering-based classification model into a fuzzy logic inference model based on linguistic rules which can be represented by a decision tree formalism. The classification model thereby obtained is transparent in that it allows direct interpretation and inspection of the model. An extension in the procedure for the development of the fuzzy logic inference model is introduced to allow the treatment of more complicated cases, e.g. splitted and overlapping clusters. The corresponding computational tool developed relies on a number of parameters which can be tuned by the user to optimally compromise the level of transparency of the classification process and its efficiency. A numerical application is presented with regards to the fault classification in the Steam Generator of a Pressurized Water Reactor.

MATEC Web of Conferences, 2019

When determining the degree of coincidence of any multi-feature obtained information, received in the form of a fuzzy vector, to a pre-established known pattern, two general steps should be followed. The first step is to eliminate the features that have little or no effect to the final results and to maintain only those that will influence the pattern recognition. This step could be defined as the classification process and is imperative for the simplification of the problem. One example of classification that could considerably reduce system costs is when using sensors distributed along an industrial process to manage information at a central location. Several methods could be used for classification, such as statistical methods, rough sets, fuzzy logic or information theory. The second step is to find out the correlation between the received fuzzy vector and the vector defining the known pattern using the previously selected features. For this part, the use of fuzzy logic is extre...

ARPN journal of engineering and applied sciences, 2016

Vibration fault exhibit a multifaceted and nonlinear behavior generation in rotated machines, for example in a steam turbine (ST). Vibration fault (VF) is collectedin the form of acceleration, velocity, and displacement via the vibration sensor. This fault damages the turbines if it strays into the danger zone. This paper first models the VF in a time domain to transfer the frequency domain via an FFT technique. The signals were applied to the fuzzy system to be used by the VF for classification via sugeno and mamdani Fuzzy Inference System (FIS) to generate the signal that will reflect the VF in the event it is embedded into the protection system. The Membership Function (MF) sets depends on practical work in a power plant, and the ISO is interested in ST vibration zones. The outcomes of the sugeno fuzzy property is the generation of stable and usable signals that can be used within the protection system, mostly owing to its efficiency in detecting vibrational faults. The results f...

WIT Transactions on Information and Communication Technologies, 2004

This paper introduces a fuzzy decision tree to initiate the first population of a genetic algorithm to perform data classification. On large datasets, the evolutive process tends to waste computational resources until some good individual is found. It is expected that the use of a fuzzy decision tree can significantly reduce this feature. The genetic algorithm aims to obtain small fuzzy classifiers by means of optimization of fuzzy rules bases. It is shown how a fuzzy rules base is generated from a numerical database and how its best subset is found by the genetic algorithm. The classifiers are evaluated in terms of accuracy, cardinality and number of features employed. The results obtained are compared with a known study in the literature and with an academic decision tree tool. The method was able to produce small fuzzy classifiers with very good performance.

2004

In this paper it is pointed out that a classification is always made taking into account all the available classes, ie, by means of a classification system. The approach presented in this paper generalizes the classical definition of fuzzy partition as defined by Ruspini, which is now conceived as a quite often desirable objective that can be usually obtained only after a long learning process. In addition, our model allows the evaluation of the resulting classification, according to several indexes related to covering, relevance and overlapping.

Proceedings of the 15th IFAC World Congress, 2002, 2002

The paper focuses on the application of neuro-fuzzy techniques in fault detection and isolation. The objective of this paper is to detect and isolate faults to an industrial gas turbine, with emphasis on faults occurred in the actuator part of the gas turbine. A neuro-fuzzy based learning and adaptation of TSK fuzzy models is used for residual generation, while for residual evaluation a neuro-fuzzy classifier for Mamdani models is used. The paper is concerned on how to obtain an interpretable fault classifier as well as interpretable models for residual generation. Copyright © 2002 IFAC

Journal of Vibroengineering, 2015

Mechanical differentials are widely used in automotive, agricultural machineries and heavy industry applications due to their large transmission ratio, strong load-bearing capacity and high transmission efficiency. The tough operation conditions of heavy duty and intensive impact load may cause damage, hence condition monitoring of these machines is very important. This paper proposes a data driven model-based condition monitoring scheme that is applied to differential. The scheme is based upon a fuzzy inference system (FIS) in combination with decision trees. To achieve this objective, the acoustic signals from a microphone were captured for the following conditions: Health, bearing fault, worn pinion, broken pinion, worn cranwheel and broken cranwheel for tow working levels of differential (1500 and 3000 r/min). Taken signals were in time domain and for extraction more information was converted from time domain to time-frequency domains using wavelet transformation. Subsequently, statistical features were extracted from signals using descriptive statistic parameters, better features were selected by J48 algorithm and used for developing decision trees. In the next stage, fuzzy logic rules were written using the decision tree and fuzzy inference engines were produced. In order to evaluate the proposed J48-FIS model, the data sets obtained from acoustic signals of the differential were used. The total classification accuracy for 1500 and 3000 r/min conditions were 92.5 % and 95 %, respectively, so the work conducted has demonstrated the potential of used method to classify the fault conditions which are represent in differential.

2014

Fuzzy decision tree or Soft Decision Tree (SDT) classifier using interval type-2 fuzzy logic rule based data mining for steam turbine fault analysis of a power system rotatory machine component is used not only for database analyses, but also for machine learning. The classification rules are based on standardized vibration frequency data for steam turbines and field experts’ analyses of turbine vibration problems. The system can identify twenty types of standard steam turbine faults. The system was developed using 1500 simulated data sets. The data mining methods were then used to identify 20 explicit rules for the turbine faults. The results indicate that the fuzzy decision tree classifier using interval type-2 fuzzy logic rule based data mining can be effectively applied to diagnosis of rotating machinery by giving useful rules to interpret the data. The data mining and analysis was implemented between the fault information dimensions table and the relationship rule dimensions ta...