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Adaptive event-triggered anomaly detection in compressed vibration data

机译：压缩振动数据中的自适应事件触发异常检测

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摘要

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. (C) 2018 Elsevier Ltd. All rights reserved.
机译：异常检测是机器的预测和状态监视（PCM）中的关键任务。在现代远程PCM系统中，通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，挑战在于压缩（失真）的振动数据如何影响状态监测算法。本文介绍了一种新颖的算法，该算法可以根据连续的噪声振动曲线（使用压缩的振动数据）自适应地建立正常的操作范围。所提出的技术基于四个模块，包括特征提取，特征融合，极值振动建模和用于异常检测的自适应阈值。使用三个时间序列数据集的实验验证了该方法的有效性。实验结果表明，所提出的算法能够有效地检测出旋转机械中的故障，而没有错误的参考数据。此外，所提出的方法能够从原始数据集和压缩数据集（失真的数据集）中以相同的准确性生成准确的预警和警报指示。 （C）2018 Elsevier Ltd.保留所有权利。

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