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

Ayman Elhalwagy, Tatiana Kalganova

TL;DR

The proposed approach, tested on a case study of three-phase current data from two different homogeneous Induction Motor fault datasets on anomaly detection, outperforms conventional training approaches with an Average F1 score of 0.879 and effectively generalises across all datasets.

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.

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

TL;DR

The proposed approach, tested on a case study of three-phase current data from two different homogeneous Induction Motor fault datasets on anomaly detection, outperforms conventional training approaches with an Average F1 score of 0.879 and effectively generalises across all datasets.

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.
Paper Structure (31 sections, 9 equations, 6 figures, 16 tables, 1 algorithm)

This paper contains 31 sections, 9 equations, 6 figures, 16 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. NN based generalisation techniques
  4. Dropout
  5. Pruning
  6. Data-based generalisation techniques
  7. Multi-Dataset training
  8. Dataset Fusion
  9. Computational Complexity
  10. Requirements for application
  11. Homogeneous Datasets
  12. Data periodicity
  13. Time Domain Data
  14. Proposed Benefits of Dataset Fusion
  15. Case Study: Dataset Fusion for 3-phase motor current data
  16. ...and 16 more sections

Figures (6)

  • Figure 1: A summarised illustration of the Dataset Fusion algorithm
  • Figure 2: Time series signal from Dataset A (a), B (b) and fused (c), Probability Distribution Function from Dataset A (d), B (e) and fused (f), and Fourier Transform from Dataset A (g), B (h) and fused (i) healthy files
  • Figure 3: Principal Component Analysis of Dataset A, B and the fused dataset
  • Figure 4: Box and whisker plot comparing the results for Experiment 1 - Training methods comparison
  • Figure 5: Box and whisker plot showcasing the impact of varying data volumes on each training method, grouped by training approach and plotted against average F1 score (higher is better).
  • ...and 1 more figures