A Dataset Fusion Algorithm for Generalised Anomaly Detection in
  Homogeneous Periodic Time Series Datasets
Ayman Elhalwagy Tatiana Kalganova
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.