  In recent times, Large Language Models (LLMs) have captured a global
spotlight and revolutionized the field of Natural Language Processing. One of
the factors attributed to the effectiveness of LLMs is the model architecture
used for training, transformers. Transformer models excel at capturing
contextual features in sequential data since time series data are sequential,
transformer models can be leveraged for more efficient time series data
prediction. The field of prognostics is vital to system health management and
proper maintenance planning. A reliable estimation of the remaining useful life
(RUL) of machines holds the potential for substantial cost savings. This
includes avoiding abrupt machine failures, maximizing equipment usage, and
serving as a decision support system (DSS). This work proposed an
encoder-transformer architecture-based framework for multivariate time series
prediction for a prognostics use case. We validated the effectiveness of the
proposed framework on all four sets of the C-MAPPS benchmark dataset for the
remaining useful life prediction task. To effectively transfer the knowledge
and application of transformers from the natural language domain to time
series, three model-specific experiments were conducted. Also, to enable the
model awareness of the initial stages of the machine life and its degradation
path, a novel expanding window method was proposed for the first time in this
work, it was compared with the sliding window method, and it led to a large
improvement in the performance of the encoder transformer model. Finally, the
performance of the proposed encoder-transformer model was evaluated on the test
dataset and compared with the results from 13 other state-of-the-art (SOTA)
models in the literature and it outperformed them all with an average
performance increase of 137.65% over the next best model across all the
datasets.
