马氏距离
分类器(UML)
数据挖掘
工程类
故障检测与隔离
计算机科学
模式识别(心理学)
人工智能
预言
变压器
可靠性工程
执行机构
电气工程
电压
作者
Haiyue Wu,Matthew J. Triebe,John W. Sutherland
标识
DOI:10.1016/j.jmsy.2023.02.018
摘要
Owing to the rapid development of Industry 4.0, new sensing and communication technologies have made vast amounts of untapped process data available. In order to transform such data assets into strong insights and knowledge that support manufacturing decisions, condition-based maintenance (CBM) and fault detection and diagnosis (FDD) have become effective ways to enhance equipment reliability and reduce costs. A successful data-driven FDD method must not only be capable of identifying the types of known faults, but also in detecting unseen or uncharacterized events during manufacturing system operation. To this end, this paper presents a Transformer-based classifier that can efficiently identify different known types and severity levels of fault conditions, in addition to novel fault detection. In this method, time-frequency spectrograms transformed from raw vibration signals are input to the classifier for known fault classification. Utilizing the advanced feature extracting performance of the classifier, a simple yet effective technique based on Mahalanobis distance is adopted to detect whether the fault comes from a previously unseen fault condition. When a novel condition is detected, the model is subsequently retrained using the novel data in an incremental learning manner. The proposed method is verified by an experimental case study with data collected from a testbed that has many features representative of common manufacturing equipment. The results demonstrated that the proposed method has superior performance in both fault diagnosis and novelty identification when compared with the baseline models and a cutting-edge model.
科研通智能强力驱动
Strongly Powered by AbleSci AI