Intelligent Fault Detection and Diagnosis in Condition-Based Maintenance

Dear colleagues,

The impressive progress made in the PHM field over the last decade is remarkable (increasing availability of data from sensors, AI algorithms, computing capabilities, etc.).

This paves the way for new approaches to address maintenance. All recent economic studies agree on the need to minimize Operations and Maintenance (O&M) costs to maintain the competitive level of the industry. The aim of this Special Issue is to present to the community in the field the most recent advances while emphasizing the industrial deployment of these approaches.

These advances now make it possible to obtain an almost instantaneous profile of a set of sensors embedded on the industrial asset. Some even suggest that today, ‘monitoring’ is a problem that has already been solved. However, several fundamental challenges and technological obstacles still exist, including the following: how can we quantify the uncertainty of a diagnosis? How can we transfer learning obtained from one asset to another that is similar but not identified? How can we evolve prognostic processes? How to make an accurate diagnosis from indirect measurements, and what is the inherent uncertainty in this operation? How can we maintain ‘interpretability’ with artificial intelligence algorithms?

This Special Issue seeks original research articles focusing on advances in all facets of diagnosis and prognosis. We welcome papers that offer new directions and research perspectives and especially that pose new challenges for our community. Contributions that demonstrate successful applications of the methods to complex equipment are particularly interesting.

We hope that this Special Issue will be useful and informative for researchers and practitioners in the relevant industrial fields. Research topics of interest in this Special Issue include, but are not limited to, the following:

• Quantification of uncertainties in diagnostics and prognostics;
• Risk management and decision support tools in predictive maintenance;
• Fault monitoring, diagnostics and prognostics by indirect measurements;
• Scalable and transferable diagnostics and prognostics from one type of equipment to another;
• Strategies for the industrial deployment of new solutions;
• Case studies from industrial applications (automotive, aeronautics, manufacturing, food, pharmaceutical, etc.).

Prof. Dr. Antoine Tahan
Guest Editor

Dr. Adaiton Oliveira Filho
Guest Editor Assistant

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• diagnostics
• prognostics
• PHM
• maintenance
• uncertainty
• risk analysis