An Online System of Detecting Anomalies and Estimating Cycle Times for Production Lines
An Online System of Detecting Anomalies and Estimating Cycle Times for Production Lines
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DOI：
10.1109/iecon49645.2022.9969061
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发表时间：
2022-10
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影响因子：
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通讯作者：
Tsuyoshi Ishizone;T. Higuchi;Kosuke Okusa;K. Nakamura
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文献类型：
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作者：
Tsuyoshi Ishizone;T. Higuchi;Kosuke Okusa;K. Nakamura
生产机器的能耗数据往往表现出准周期性，当检测到偏离准周期性时，就会发现异常。对于这些数据，快速估计每个时间点的单个周期并检测异常是至关重要的。在本研究中，我们提出了一个满足这些要求的系统。该系统训练了一个具有注意机制的神经网络，并将该机制中的权向量应用于两个任务。实验结果表明，对于模拟生产线功耗数据的传感器数据，该方法优于基准方法。
Energy consumption data of production machines often exhibit quasi-periodicity, and anomalies are observed when deviations from the quasi-periodicity are detected. For such data, it is crucial to quickly estimate the individual cycles at each time point and detect abnormalities. In this study, we propose a system that satisfies these requirements. The proposed system trains a neural network with an attention mechanism and applies the weight vectors in the mechanism to the two tasks. Experimental results demonstrate that the proposed method outperforms benchmark methods for sensor data that mimic power consumption data of production lines.