Vol. 10 No. 4 (2019): IJPHM Special Issue on PHM Applications of Deep Learning & Emerging Analytics
The International Journal of Prognostics and Health Management (IJPHM) is the premier journal of multidisciplinary research on Prognostics, Diagnostics, and System Health Management. IJPHM is online, open access, and has no fees whatsoever to publish.
This special issue focuses on theory and application of deep learning and advanced analytics to anomaly detection, condition monitoring, diagnostics, and prognostics. Deep learning has recently achieved significant breakthroughs in many different domains, including computer vision, language processing, genomics, and speech recognition; e.g., AlphaGo and AlphaZero have achieved super-human performance in complex games without human input.
Despite these encouraging results, these techniques have seen little adoption by industry for PHM applications. There are several obstacles that need to be surmounted to enable the broad adoption of deep learning for PHM:
- Limited number of representative training samples, particularly for different types of faulty conditions and representative time-to-failure trajectories
- Appropriate benchmark datasets to compare the progress of newly developed algorithms
- Variability of operating and environmental conditions to appropriately transfer the learnt patterns between different operating conditions
- Heterogeneity of condition monitoring signals, system configurations, and operating conditions
Moreover, a number of emerging technologies – such as quantum computing, distributed ledger, blockchain, edge computing, mixed reality, explainable AI, and smart dust – hold great potential, and will undoubtedly have a profound effect on the research and application of PHM. People already doing work in these areas are truly on the cutting edge of the field.