Unknown fault detection for EGT multi-temperature signals based on self-supervised feature learning and unary classification  

作　　者：Xilian YANG Kanru CHENG Qunfei ZHAO Yuzhang WANG 

机构地区：[1]School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai,200240,China [2]School of Mechanical Engineering,Shanghai Jiao Tong University,Shanghai,200240,China

出　　处：《Frontiers in Energy》2023年第4期527-544,共18页能源前沿（英文版）

基　　金：supported by the National Science and Technology Major Project of China(Grant No.2017-V-0011-0063).

摘　　要：Intelligent tpower systems scanimprove operational efficiency by installing a large number of sensors.Data-based methods of supervised learning have gained popularity because of available Big Data and computing resources.However,the common paradigm of the loss function in supervised learning requires large amounts of labeled data and cannot process unlabeled data.The scarcity of fault data and a large amount of normal data in practical use pose great challenges to fault detection algorithms.Moreover,sensor data faults in power systems are dynamically changing and pose another challenge.Therefore,a fault detection method based on self-supervised feature learning was proposed to address the above two challenges.First,self-supervised learning was employed to extract features under various working conditions only using large amounts of normal data.The self-supervised representation learning uses a sequence-based Triplet Loss.The extracted features of large amounts of normal data are then fed into a unary classifier.The proposed method is validated on exhaust gas temperatures(EGTs)of a real-world 9F gas turbine with sudden,progressive,and hybrid faults.A comprehensive comparison study was also conducted with various feature extractors and unary classifiers.The results show that the proposed method can achieve a relatively high recall for all kinds of typical faults.The model can detect progressive faults very quickly and achieve improved results for comparison without feature extractors in terms ofF1 score.

关 键 词：fault detection unary classification self-supervised representation learning multivariate nonlinear time series 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]