Energy Out-of-distribution Based Fault Detection of Multivariate Time-series Data

离群值 Softmax函数 异常检测 能量(信号处理) 计算机科学 人工神经网络 航程(航空) 人工智能 模式识别(心理学) 断层(地质) 自编码 多元统计 功能(生物学) 数据挖掘 机器学习 统计 数学 工程类 航空航天工程 地质学 地震学 生物 进化生物学
作者
Umang Goswami,Jyoti Rani,Deepak Kumar,Hariprasad Kodamana,Manojkumar Ramteke
出处
期刊:Computer-aided chemical engineering 卷期号:: 1885-1890 被引量:4
标识
DOI:10.1016/b978-0-443-15274-0.50299-7
摘要

A major challenge faced by the chemical process industry is carrying out operations safely and safely. The proposed work entails a fault detection approach for a multivariate time series dataset by utilizing the energy scores instead of the traditional approach. This work proposes a loss function which utilizes the concept of in-distribution and out of the distribution of data. Energy scores are more theoretically aligned with the probability density of the inputs and can be used as a scoring function. For a pre-trained neural network, energy can be utilized as a scoring function and can also be used as a trainable cost function. The concept of out-of-distribution is similar to that of any outlier identification method. Similarly, for energy out of distribution, an energy value which falls below a certain threshold can be considered an outlier and is addressed as out-of-distribution. The values within the range are in-distribution. Higher energy values imply a lower likelihood of occurrence and vice versa. The proposed approach is compared with different deep learning approaches like Auto-encoders (AEs), LSTMs and LSTM-AEs that are traditionally used for anomaly detection and utilize the softmax scores. The Proposed methodology is also compared with some state-of-the-art fault detection methods, such as the PCA and DPCA and returns encouraging results. Energy based out of distribution is coupled with various deep learning methods to identify faulty and normal points. When teamed with the Auto-encoder network, energy-based scoring proved to be of significant dominance compared to other methods. The study was validated for the benchmark Tennessee Eastman data for fault detection.
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