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.

Published: 2019-12-02

Technical Papers

Anomaly Detection on Time Series with Wasserstein GAN applied to PHM

Mélanie Ducoffe, Ilyass Haloui, Jayant Sen Gupta
Abstract 4714 | PDF Downloads 1098 | DOI https://doi.org/10.36001/ijphm.2019.v10i4.2610

Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models

Vishnu TV, Diksha Chavan, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff
Abstract 384 | PDF Downloads 237 | DOI https://doi.org/10.36001/ijphm.2019.v10i4.2612

Simulation-driven Deep Classification of Bearing Faults from Raw Vibration Data

Martin Hemmer, Andreas Klausen, Huynh van Khang, Kjell G. Robbersmyr, Tor I. Waag
Abstract 268 | PDF Downloads 315 | DOI https://doi.org/10.36001/ijphm.2019.v10i4.2615

Deep Detector Health Management under Adversarial Campaigns

Javier Echauz, Keith Kenemer, Sarfaraz Hussein, Jay Dhaliwal, Saurabh Shintre, Slawomir Grzonkowski, Andrew Gardner
Abstract 145 | PDF Downloads 109 | DOI https://doi.org/10.36001/ijphm.2019.v10i4.2617

Attention and Long Short-Term Memory Network for Remaining Useful Lifetime Predictions of Turbofan Engine Degradation

Paulo Roberto de Oliveira da Costa, Alp Akcay, Yingqian Zhang, Uzay Kaymak
Abstract 774 | PDF Downloads 632 | DOI https://doi.org/10.36001/ijphm.2019.v10i4.2623