Autoencoder and Incremental Clustering-Enabled Anomaly Detection
Autoencoder and Incremental Clustering-Enabled Anomaly Detection
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DOI：
10.3390/electronics12091970
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发表时间：
2023-04
期刊：
影响因子：
2.9
通讯作者：
Andrew Charles Connelly;Syed Ali Raza Zaidi;D. McLernon
中科院分区：
文献类型：
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作者：
Andrew Charles Connelly;Syed Ali Raza Zaidi;D. McLernon
许多支持机器学习的异常检测方法都依赖于大量训练数据的可用性。我们的数据是由家用电器（如洗碗机或洗衣机）的周期功率读数形成的，并且不包含任何已知的异常行为示例。此外，我们仅限于机器的电压，安培数和电流读数，从60秒样本的改装电源插座中提取。没有丰富的传感器数据或以前的见解可作为训练基础，限制了我们利用现有工作的能力。我们设计了一个系统来监控电器的行为。该系统需要特别考虑，因为来自同一机器的不同功率循环可能表现出不同的行为，并且它通过将看不见的循环模式聚类到孤立的训练数据集和相应的学习参数中来解释这一点。然后，它们被实时传递到自动编码器集合，用于基于重建的异常检测，使用重建中的错误作为及时标记异常点的手段。该系统在注入了随机、成比例异常的真实机器数据集上正确识别和训练数据流的适当循环集群。
Many machine-learning-enabled approaches towards anomaly detection depend on the availability of vast training data. Our data are formed from power readings of cycles from domestic appliances, such as dishwashers or washing machines, and contain no known examples of anomalous behaviour. Moreover, we are limited to the machine’s voltage, amperage, and current readings, drawn from a retrofitted power outlet in 60-s samples. No rich sensor data or previous insights are available as a training basis, limiting our ability to leverage the existing work. We design a system to monitor the behaviour of electrical appliances. This system requires special consideration as different power cycles from the same machine can exhibit different behaviours, and it accounts for this by clustering unseen cycle patterns into siloed training datasets and corresponding learned parameters. They are then passed in real-time to an autoencoder ensemble for reconstruction-based anomaly detection, using the error in reconstruction as a means to flag anomalous points in time. The system correctly identifies and trains appropriate cycle clusters of data streams on a real-world machine dataset injected with stochastic, proportionate anomalies.