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Deep Convolutional Clustering-Based Time Series Anomaly Detection
| Content Provider | MDPI |
|---|---|
| Author | Chadha, Gavneet Singh Islam, Intekhab Schwung, Andreas Ding, Steven X. |
| Copyright Year | 2021 |
| Description | This paper presents a novel approach for anomaly detection in industrial processes. The system solely relies on unlabeled data and employs a 1D-convolutional neural network-based deep autoencoder architecture. As a core novelty, we split the autoencoder latent space in discriminative and reconstructive latent features and introduce an auxiliary loss based on k-means clustering for the discriminatory latent variables. We employ a Top-K clustering objective for separating the latent space, selecting the most discriminative features from the latent space. We use the approach to the benchmark Tennessee Eastman data set to prove its applicability. We provide different ablation studies and analyze the method concerning various downstream tasks, including anomaly detection, binary and multi-class classification. The obtained results show the potential of the approach to improve downstream tasks compared to standard autoencoder architectures. |
| Starting Page | 5488 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s21165488 |
| Journal | Sensors |
| Issue Number | 16 |
| Volume Number | 21 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-08-15 |
| Access Restriction | Open |
| Subject Keyword | Sensors Industrial Engineering Unsupervised Learning Deep Convolutional Autoencoder Top-k K-means Clustering Anomaly Detection |
| Content Type | Text |
| Resource Type | Article |
