Anomaly Detection on Gas Turbine Time-series’ Data Using Deep LSTM-Autoencoder

Abstract: Anomaly detection with the aim of identifying outliers plays a very important role in various applications (e.g., online spam, manufacturing, finance etc.). An automatic and reliable anomaly detection tool with accurate prediction is essential in many domains. This thesis proposes an anomaly detection method by applying deep LSTM (long short-term memory) especially on time-series data. By validating on real-worlddata at Siemens Industrial Turbomachinery (SIT), the proposed methods hows promising performance, and can be employed in different data domains like device logs of turbine machines to provide useful information on abnormal behaviors. In detail, our proposed method applies an auto encoder to have feature selection by keeping vital features, and learn the time series’s encoded representation. This approach reduces the extensive input data by pulling out the auto encoder’s latent layer output. For prediction, we then train a deep LSTM model with three hidden layers based on the encoder’s latent layer output. Afterwards, given the output from the prediction model, we detect the anomaly sensors related to the specific gas turbine by using a threshold approach. Our experimental results show that our proposed methods perform well on noisy and real-world data set in order to detect anomalies. Moreover, it confirmed that making predictions based on encoding representation, which is under reduction, is more accurate. We could say applying autoencoder can improve both anomaly detection and prediction tasks. Additionally, the performance of deep neural networks would be significantly improved for data with high complexity.