A Dataset Fusion Algorithm for Generalised Anomaly Detection in Homogeneous Periodic Time Series Datasets

• URL: http://arxiv.org/abs/2305.08197v1
• Date: Sun, 14 May 2023 16:24:09 GMT
• Title: A Dataset Fusion Algorithm for Generalised Anomaly Detection in Homogeneous Periodic Time Series Datasets
• Authors: Ayman Elhalwagy and Tatiana Kalganova
• Abstract summary: "Dataset Fusion" is an algorithm for fusing periodic signals from multiple homogeneous datasets into a single dataset. The proposed approach significantly outperforms conventional training approaches with an Average F1 score of 0.879. Results show that using only 6.25% of the training data, translating to a 93.7% reduction in computational power, results in a mere 4.04% decrease in performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The generalisation of Neural Networks (NN) to multiple datasets is often overlooked in literature due to NNs typically being optimised for specific data sources. This becomes especially challenging in time-series-based multi-dataset models due to difficulties in fusing sequential data from different sensors and collection specifications. In a commercial environment, however, generalisation can effectively utilise available data and computational power, which is essential in the context of Green AI, the sustainable development of AI models. This paper introduces "Dataset Fusion," a novel dataset composition algorithm for fusing periodic signals from multiple homogeneous datasets into a single dataset while retaining unique features for generalised anomaly detection. The proposed approach, tested on a case study of 3-phase current data from 2 different homogeneous Induction Motor (IM) fault datasets using an unsupervised LSTMCaps NN, significantly outperforms conventional training approaches with an Average F1 score of 0.879 and effectively generalises across all datasets. The proposed approach was also tested with varying percentages of the training data, in line with the principles of Green AI. Results show that using only 6.25\% of the training data, translating to a 93.7\% reduction in computational power, results in a mere 4.04\% decrease in performance, demonstrating the advantages of the proposed approach in terms of both performance and computational efficiency. Moreover, the algorithm's effectiveness under non-ideal conditions highlights its potential for practical use in real-world applications.

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