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FEATURE EXTRACTION AND FAULT DETECTION IN A NON-STATIONARY PROCESS THROUGH UNSUPERVISED MACHINE LEARNING
FEATURE EXTRACTION AND FAULT DETECTION IN A NON-STATIONARY PROCESS THROUGH UNSUPERVISED MACHINE LEARNING
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机译:不受监督的机器学习在非平稳过程中的特征提取和故障检测
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摘要
An apparatus, method, and non-transitory machine-readable medium provide for improved feature extraction and fault detection in a non-stationary process through unsupervised machine learning. The apparatus includes a memory and a processor operably connected to the memory. The processor receives training data regarding a field device in an industrial process control and automation system; extracts a meaningful feature from the training data; performs an unsupervised classification to determine a health index for the meaningful feature; identifies a faulty condition of real-time data using the health index of the meaningful feature; and performs a rectifying operation in the industrial process control and automation system for correcting the faulty condition of the field device.
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