Application of Long Short-Term Memory (LSTM) Autoencoder with Density-Based Spatial Clustering of Applications with Noise(DBSCAN) on Anomaly Detection

Abstract: Early fault diagnosis of equipment based on the current condition assessment is one of the commonly used methods of CBM (condition-based maintenance). It refers to mining the impending fault characteristics from a large amount of production data in long-term operation (Luo et al., 2019). However, these data are huge multivariate data causing a difficulty in extracting features manually. In manufacturing scenario, the majority of machines are in a normal state and the abnormalities are relatively rare which makes the collected data occurring data imbalancing.This study explores the use of LSTM (Long Short-Term Memory) autoencoder combined with DBSCAN (Density-based spatial clustering of applications with noise) under the condition of data imbalance. The reconstruction error of the model after training is used as an evaluation index where the errors of each time point between the reconstruction sequence and the actual sequence are calculated and inputted for classification in the DBSCAN model.In this study, a water distribution system dataset from the SKAB (Skoltech Anomaly Benchmark) was used to verify the anomaly detection of our proposed model. Our model shows the F1-score of 0.8025 which is better than the four models proposed by Moon et al. in 2023. With a LSTM autoencoder, the proposed DBSCAN classification model can avoid the difficulty of setting a threshold value in classification.

Keywords: Anomaly Detection, Condition-Based Maintenance, Lstm Autoencoder, Dbscan

DOI: 10.54941/ahfe1004654

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