This study presents a novel framework for self-supervised motif discovery in long temporal data, an approach focusing on enhancing explainability and identifying intrinsic differences between motifs. Our research fills a critical gap in motif analysis by introducing a three-pronged methodology: extracting motifs, comparing their signatures, identifying them, and interpreting their differences. The proposed self-supervised technique is designed to ascertain the expected number of motifs in a dataset and to extract them effectively. This approach is reinforced by a robust algorithm that identifies unique motifs and their signatures and a proper distance metric for comparing partially similar motifs. This metric is crucial in determining the similarity between motifs of varying lengths or those containing noise. The application of our framework to ECG data demonstrates its effectiveness in distinguishing between normal and irregular heartbeat patterns without supervision. This capability is a significant advancement in motif analysis, offering a scalable and noise-resistant solution that simplifies the process of segmenting, identifying, and understanding complex temporal patterns. Our results of over 99.9% accuracy underscore our approach's potential in a wide range of applications, particularly in healthcare diagnostics, where accurate and explainable motif analysis is paramount.

This work summarizes the state-of-the-art data-driven methods for prediction of the Remaining Useful Life (RUL). It discusses challenges and open problems faced in PdM. This study presents a discussion on the new problems that need to be considered towards the Industry 4.0 goals. We propose the future direction for each challenge discussed in this article.