Complex Systems Reliability and Maintenance Optimal Management Using the PHM Approach and Artificial Intelligence

Dear Colleagues,

This Special Issue focuses on the theory and application of prognostic and health management (PHM) methodologies in industrial contexts. PHM finds application in several domains, including aerospace, automotive, transportation, and manufacturing. Due to the potential advantages of a predictive maintenance policy, in terms of cost savings and asset management, PHM is receiving a broad consensus among industries too. 

Despite encouraging results achieved by the several methods proposed in the literature, PHM has seen little adoption by industries because of practical issues that need to be addressed. The most relevant ones can be summarized as follows: 

1. The number of labelled training data is limited, given the difficulties in getting data in all possible operating and faulty conditions;
2. Both operating and environmental conditions continuously change over time, making it hard to apply a pre-built model to a similar component/system in a different working environment;
3. The data are often collected from different sources, at different frequencies, and stored in several devices, making a time-consuming pre-processing necessary;
4. The equipment generates raw data that must be analyzed to extract relevant information. The transfer and storage of a high amount of raw data is a time-consuming activity for companies.

On the other hand, enabling technologies, such as the Industrial Internet of Things and Edge-Cloud Computing, hold great potential for the implementation of predictive maintenance in industries and should be exploited to deal with the abovementioned issues.

The focus of this Special Issue is to provide a forum for PHM researchers and practitioners to discuss the applicability and challenges of semi-supervised, incremental, and transfer learning for industrial PHM applications. Papers describing both novel applications and related theory are encouraged, with a specific focus on streaming analysis that provides real-time feedback on the health condition of the assets. 

Prof. Alberto Regattieri
Prof. Matthias Klumpp
Prof. Miguel Delgado-Prieto
Guest Editors

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