Anomaly detection refers to detecting data points, events, or behaviour that do not comply with expected or normal behaviour. For example, a typical problem related to anomaly detection on an industrial level is having little labelled data and a few run-to-failure examples, making it challenging to develop reliable and accurate prognostics and health management systems for fault detection and identification. Certain machine learning approaches for anomaly detection require normal data to train, which reduces the need for historical data with fault labels, where the main task is to differentiate between normal and anomalous behaviour. Several reconstruction-based deep learning approaches are explored in this work and compared towards detecting anomalies in air compressors. Anomalies in such systems are not point-anomalies, but instead, an increasing deviation from the normal condition as the system components start to degrade. In this paper, a descriptive range of the deviation based on the reconstruction-based techniques is proposed. Most anomaly detection approaches are considered black box models, predicting whether an event should be considered an anomaly or not. This paper proposes a method for increasing the transparency and explainability of reconstruction-based anomaly detection to indicate which parts of a system contribute to the deviation from expected behaviour. The results show that the proposed methods detect abnormal behaviour in air compressors accurately and reliably and indicate why it deviates. The proposed approach is capable of detecting faults without the need for historical examples of similar faults. The proposed method for explainable anomaly detection is crucial to any prognostics and health management (PHM) system due to its purpose of detecting deviations and identifying causes.

中文翻译：

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使用基于重构的深度学习对空气压缩机进行健康监测，以提高透明度进行异常检测

异常检测是指检测不符合预期或正常行为的数据点、事件或行为。例如，与工业级别的异常检测相关的典型问题是很少有标记数据和一些运行到故障的示例，这使得开发用于故障检测和识别的可靠且准确的预测和健康管理系统具有挑战性。某些用于异常检测的机器学习方法需要训练正常数据，这减少了对带有故障标签的历史数据的需求，其中主要任务是区分正常和异常行为。在这项工作中探索了几种基于重建的深度学习方法，并将其与检测空气压缩机中的异常进行比较。这种系统中的异常不是点异常，而是，随着系统组件开始退化，与正常条件的偏差越来越大。在本文中，提出了基于重建技术的偏差描述范围。大多数异常检测方法都被认为是黑盒模型，用于预测事件是否应被视为异常。本文提出了一种提高基于重构的异常检测的透明度和可解释性的方法，以指示系统的哪些部分导致了与预期行为的偏差。结果表明，所提出的方法可以准确可靠地检测空气压缩机中的异常行为，并指出其偏差的原因。所提出的方法能够在不需要类似故障的历史示例的情况下检测故障。