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http://dx.doi.org/10.22156/CS4SMB.2022.12.01.031

Anomaly Detection In Real Power Plant Vibration Data by MSCRED Base Model Improved By Subset Sampling Validation  

Hong, Su-Woong (Department of Computer-Engineering, Inha University)
Kwon, Jang-Woo (Department of Computer-Engineering, Inha University)
Publication Information
Journal of Convergence for Information Technology / v.12, no.1, 2022 , pp. 31-38 More about this Journal
Abstract
This paper applies an expert independent unsupervised neural network learning-based multivariate time series data analysis model, MSCRED(Multi-Scale Convolutional Recurrent Encoder-Decoder), and to overcome the limitation, because the MCRED is based on Auto-encoder model, that train data must not to be contaminated, by using learning data sampling technique, called Subset Sampling Validation. By using the vibration data of power plant equipment that has been labeled, the classification performance of MSCRED is evaluated with the Anomaly Score in many cases, 1) the abnormal data is mixed with the training data 2) when the abnormal data is removed from the training data in case 1. Through this, this paper presents an expert-independent anomaly diagnosis framework that is strong against error data, and presents a concise and accurate solution in various fields of multivariate time series data.
Keywords
Expert independent feature extraction; Error data detection method; Multivariate time series data anomaly detection; Vibration anomaly diagnosis; Auto-encoder model;
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Times Cited By KSCI : 1  (Citation Analysis)
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