Adaptive event-triggered anomaly detection in compressed vibration data
Adaptive event-triggered anomaly detection in compressed vibration data
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DOI：
10.1016/j.ymssp.2018.12.039
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发表时间：
2019-05
影响因子：
8.4
通讯作者：
Yang Zhang;P. Hutchinson;N. Lieven;J. Núñez-Yáñez
中科院分区：
文献类型：
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作者：
Yang Zhang;P. Hutchinson;N. Lieven;J. Núñez-Yáñez
异常检测是机械故障预测与状态监测中的一项重要任务。在现代远程PCM系统中，经常使用数据压缩技术来减少对带宽和存储的需求。在这些系统中，压缩（失真）振动数据如何影响状态监测算法的挑战随之而来。本文介绍了一种新的算法，可以自适应地建立正常范围的操作，从连续的噪声振动轮廓与压缩的振动数据。该方法包括特征提取、特征融合、极值振动建模和自适应阈值异常检测四个模块。所提出的方法已被验证与实验使用三个时间序列数据集。实验结果表明，该算法能够有效地进行旋转机械故障检测，没有错误的参考数据。此外，所提出的方法是能够产生准确的预警和报警指示从原始和压缩（失真）的数据集具有相同的准确性。
Anomaly detection is a crucial task in Prognostics and Condition Monitoring (PCM) of machinery. In modern remote PCM systems, data compression techniques are regularly used to reduce the need for bandwidth and storage. In these systems the challenge arises of how the compressed (distorted) vibration data affects the condition monitoring algorithms. This paper introduces a novel algorithm that can adaptively establish normal bounds of operation from continuous noisy vibration profiles working with compressed vibration data. The proposed technique is based on four modules, including feature extraction, feature fusion, extreme value vibration modeling and adaptive thresholding for anomaly detection. The proposed method has been validated with experiments using three time-series datasets. The experimental results indicate that the proposed algorithm is able to perform detection of malfunctions in rotating machines effectively without faulty reference data. Moreover, the proposed method is able to produce accurate early warning and alarm indications from both the raw and compressed (distorted) datasets with equal veracity.