Fault diagnosis in power transformers using multi-class logical analysis of data

This paper presents the implementation of a novel multi-class diagnostic technique for the detection and identification of faults based on an approach called logical analysis of data (LAD). LAD is a data mining, artificial intelligence approach that is based on pattern recognition. In the context of...

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Published in:	Journal of intelligent manufacturing Vol. 25; no. 6; pp. 1429 - 1439
Main Authors:	Mortada, Mohamad-Ali, Yacout, Soumaya, Lakis, Aouni
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.12.2014 Springer Nature B.V
Subjects:	Algorithms Artificial intelligence Boolean Business and Management Control Data mining Decision making Diagnostic systems Dissolution Fault diagnosis Faults Hypothesis testing Integer programming Linear programming Machine learning Machines Manufacturing Mechatronics Neural networks Pattern recognition Processes Production Repair & maintenance Robotics Statistical analysis Support vector machines Transformers Fault diagnosis Mixed 0–1 integer and linear programming Multi-class decision model Logical analysis of data Condition based maintenance
ISSN:	0956-5515, 1572-8145
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Abstract	This paper presents the implementation of a novel multi-class diagnostic technique for the detection and identification of faults based on an approach called logical analysis of data (LAD). LAD is a data mining, artificial intelligence approach that is based on pattern recognition. In the context of condition based maintenance (CBM), historical data containing condition indices and the state of the machine are the inputs to LAD. After training and testing phases, LAD generates patterns that characterize the faulty states according to the type of fault, and differentiate between these states and the normal state. These patterns are found by solving a mixed 0–1 integer linear programming problem. They are then used to detect and to identify a future unknown state of equipment. The diagnostic technique has already been tested on several known machine learning datasets. The results proved that the performance of this technique is comparable to other conventional approaches, such as neural network and support vector machine, with the added advantage of the clear interpretability of the generated patterns, which are rules characterizing the faults’ types. To demonstrate its merit in fault diagnosis, the technique is used in the detection and identification of faults in power transformers using dissolved gas analysis data. The paper reaches the conclusion that the multi-class LAD based fault detection and identification is a promising diagnostic approach in CBM.
AbstractList	This paper presents the implementation of a novel multi-class diagnostic technique for the detection and identification of faults based on an approach called logical analysis of data (LAD). LAD is a data mining, artificial intelligence approach that is based on pattern recognition. In the context of condition based maintenance (CBM), historical data containing condition indices and the state of the machine are the inputs to LAD. After training and testing phases, LAD generates patterns that characterize the faulty states according to the type of fault, and differentiate between these states and the normal state. These patterns are found by solving a mixed 0–1 integer linear programming problem. They are then used to detect and to identify a future unknown state of equipment. The diagnostic technique has already been tested on several known machine learning datasets. The results proved that the performance of this technique is comparable to other conventional approaches, such as neural network and support vector machine, with the added advantage of the clear interpretability of the generated patterns, which are rules characterizing the faults’ types. To demonstrate its merit in fault diagnosis, the technique is used in the detection and identification of faults in power transformers using dissolved gas analysis data. The paper reaches the conclusion that the multi-class LAD based fault detection and identification is a promising diagnostic approach in CBM. This paper presents the implementation of a novel multi-class diagnostic technique for the detection and identification of faults based on an approach called logical analysis of data (LAD). LAD is a data mining, artificial intelligence approach that is based on pattern recognition. In the context of condition based maintenance (CBM), historical data containing condition indices and the state of the machine are the inputs to LAD. After training and testing phases, LAD generates patterns that characterize the faulty states according to the type of fault, and differentiate between these states and the normal state. These patterns are found by solving a mixed 0-1 integer linear programming problem. They are then used to detect and to identify a future unknown state of equipment. The diagnostic technique has already been tested on several known machine learning datasets. The results proved that the performance of this technique is comparable to other conventional approaches, such as neural network and support vector machine, with the added advantage of the clear interpretability of the generated patterns, which are rules characterizing the faults' types. To demonstrate its merit in fault diagnosis, the technique is used in the detection and identification of faults in power transformers using dissolved gas analysis data. The paper reaches the conclusion that the multi-class LAD based fault detection and identification is a promising diagnostic approach in CBM.[PUBLICATION ABSTRACT]
Author	Yacout, Soumaya Mortada, Mohamad-Ali Lakis, Aouni
Author_xml	– sequence: 1 givenname: Mohamad-Ali surname: Mortada fullname: Mortada, Mohamad-Ali organization: Department of Industrial Engineering and Department of Mechanical Engineering, École Polytechnique de Montréal – sequence: 2 givenname: Soumaya surname: Yacout fullname: Yacout, Soumaya email: soumaya.yacout@polymtl.ca organization: Department of Industrial Engineering and Department of Mechanical Engineering, École Polytechnique de Montréal – sequence: 3 givenname: Aouni surname: Lakis fullname: Lakis, Aouni organization: Department of Industrial Engineering and Department of Mechanical Engineering, École Polytechnique de Montréal
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SubjectTerms	Algorithms Artificial intelligence Boolean Business and Management Control Data mining Decision making Diagnostic systems Dissolution Fault diagnosis Faults Hypothesis testing Integer programming Linear programming Machine learning Machines Manufacturing Mechatronics Neural networks Pattern recognition Processes Production Repair & maintenance Robotics Statistical analysis Support vector machines Transformers
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Title	Fault diagnosis in power transformers using multi-class logical analysis of data
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