Chemical Engineering Transactions Volume 33 deals with Prognostics and Health Management (PHM), a field of research and application which aims at making use of past, present and future information on the environmental, operational and usage conditions of an equipment in order to detect its degradation, diagnose its faults, predict and proactively manage its failures. PHM provides solutions for effective condition-based and predictive maintenance strategies which reduce the risk of failure while supporting low maintenance costs. For these reasons, many efforts are being devoted to the development of techniques for health monitoring, fault detection, diagnosis and prognosis with the intent of improving the safety and economic performances of existing and future structures, systems and components.
The content of this Volume includes 194 papers revised and finally accepted by the 93 Reviewers. These papers range on a variety of methodological developments dealing with health monitoring, fault detection, diagnostics and prognostics, data-driven, model-based and hybrid PHM methods, uncertainty treatment in PHM, system-level PHM, design and integration of PHM systems, cost analysis of PHM, verification, validation, and maturation of PHM systems, standards for PHM, advanced sensors, data and signal processing, vibration analysis, condition-based and predictive maintenance, maintenance decision support systems, maintenance and human and organizational factors, physics of failure, reliability prediction, simulation and optimization.
Also practical PHM applications in the following industrial areas are published: Aeronautics, Aerospace Automotive, Chemical and Process Industry, Electronics, Energy, Information Technology and Telecommunications, Manufacturing, Maritime Industry, Nuclear Industry, Oil & Gas, Structural Engineering, Train & Railway Industry.
The Reviewers have put a significant effort in keeping the quality of the Volume with respect to the technical aspects. We recognize this with satisfaction and wish to express our deepest appreciation for it.
Enrico Zio, Piero baraldi (Guest Editors)
