During the past few years industrial predictive maintenance has benefited from new developments in hardware and software systems. A key conclusion is that to maximize results, these systems need to be smarter with learning capabilities. Moreover, wireless sensor networks have led to a new revolution in the field of e-maintenance, offering new possibilities in measurement collection, aiming to empower monitoring with more advanced features. In what way can wireless sensor networks be applied to industrial maintenance? How can novelty detection be implemented on these systems? How can such systems scale up to offer distributed intelligence? This paper presents the WelCOM research program’s approach on the aforementioned matters answering many questions that relate to intelligent sensor systems in the field of e-maintenance and proposing flexible architectures for the implementation of these systems.
How to Cite
machine learning, signal processing, E-Maintenance, WSN, Novelty Detection
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