Machine Learning and Signal Processing for Diagnostics and Prognostics Applications

Dear Colleagues,

Global industry competition is driving the maximization of the use of resources in terms of equipment, systems, and robots. Methodologies for modeling diagnostics and prognostics have played a crucial role in increasing reliability and durability of systems (e.g., robots, machine tools) based on a robust design and an effective selection of the features that need to be monitored when the equipment is installed in the operating conditions, avoiding breakdowns, failures, or malfunctions. The increase in data availability using machine learning strategies and signal processing has permitted the use of computational methods to extract information from data modeling diagnostics and prognostics and prevent undesired performance. In this way, many challenges and opportunities remain to develop novel models and approaches transferring research results and new knowledge for different applications.

The purpose of this Special Issue is to gather high-quality research and contributions that address recent advances and developments in machine learning strategies and signal processing for diagnostics and prognostics applications, focusing on innovative systems, robots, mechatronic devices, and industrial processes.

Prof. Francesco Aggogeri
Guest Editor

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