An LSTM-Based Predictive Monitoring Method for Data with Time-varying Variability
This work addresses anomaly detection for sensor data in industrial monitoring, but it is incremental as it adapts existing LSTM techniques to a specific problem.
The paper tackled anomaly detection in data with time-varying variability by proposing an LSTM-based predictive monitoring method, which outperformed other neural network-based methods in mean shift detection as shown in simulation studies.
The recurrent neural network and its variants have shown great success in processing sequences in recent years. However, this deep neural network has not aroused much attention in anomaly detection through predictively process monitoring. Furthermore, the traditional statistic models work on assumptions and hypothesis tests, while neural network (NN) models do not need that many assumptions. This flexibility enables NN models to work efficiently on data with time-varying variability, a common inherent aspect of data in practice. This paper explores the ability of the recurrent neural network structure to monitor processes and proposes a control chart based on long short-term memory (LSTM) prediction intervals for data with time-varying variability. The simulation studies provide empirical evidence that the proposed model outperforms other NN-based predictive monitoring methods for mean shift detection. The proposed method is also applied to time series sensor data, which confirms that the proposed method is an effective technique for detecting abnormalities.