BROWSE
Detail
Journal Article
기계적 모터 고장진단을 위한 머신러닝 기법
Cited - time in 
Share 
Share
- Authors
- 정훈, 김주원
- Issue Date
- 2017-03
- Citation
- 산업경영시스템학회지, v.40, no.1, pp.57-64
- ISSN
- 2005-0461
- Publisher
- 한국산업경영시스템학회
- Language
- Korean
- Type
- Journal Article
- Abstract
- In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.
- KSP Keywords
- Best performance, Data Preprocessing, Data sets, Diagnosis model, Failure diagnosis, Fault diagnosis, Inspection period, Machine Learning Algorithms, Machine Learning technique(MLT), Maintenance cost, Maintenance support