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Development of Indicator of Data Sufficiency for Feature-based Early Time Series Classification with Applications of Bearing Fault Diagnosis

Title
Development of Indicator of Data Sufficiency for Feature-based Early Time Series Classification with Applications of Bearing Fault Diagnosis
Author
허선
Keywords
early time series classification; data sufficiency; bearing fault diagnosis; feature-based classification
Issue Date
2020-07
Publisher
MDPI
Citation
PROCESSES, v. 8, Issue. 7, Article no. 790, 14pp
Abstract
Diagnosis of bearing faults is crucial in various industries. Time series classification (TSC) assigns each time series to one of a set of pre-defined classes, such as normal and fault, and has been regarded as an appropriate approach for bearing fault diagnosis. Considering late and inaccurate fault diagnosis may have a significant impact on maintenance costs, it is important to classify bearing signals as early and accurately as possible. TSC, however, has a major limitation, which is that a time series cannot be classified until the entire series is collected, implying that a fault cannot be diagnosed using TSC in advance. Therefore, it is important to classify a partially collected time series for early time series classification (ESTC), which is a TSC that considers both accuracy and earliness. Feature-based TSCs can handle this, but the problem is to determine whether a partially collected time series is enough for a decision that is still unsolved. Motivated by this, we propose an indicator of data sufficiency to determine whether a feature-based fault detection classifier can start classifying partially collected signals in order to diagnose bearing faults as early and accurately as possible. The indicator is trained based on the cosine similarity between signals that were collected fully and partially as input to the classifier. In addition, a parameter setting method for efficiently training the indicator is also proposed. The results of experiments using four benchmark datasets verified that the proposed indicator increased both accuracy and earliness compared with the previous time series classification method and general time series classification.
URI
https://www.mdpi.com/2227-9717/8/7/790https://repository.hanyang.ac.kr/handle/20.500.11754/164860
ISSN
2227-9717
DOI
10.3390/pr8070790
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > INDUSTRIAL AND MANAGEMENT ENGINEERING(산업경영공학과) > Articles
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