P. Nolan

National University of Ireland, Ollscoil na Gaillimhe – University of Galway

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About

P. Nolan is a researcher in fault diagnosis and system monitoring, with a focus on developing data-driven methods for detecting incipient faults in complex engineering systems. Their key contributions lie in the induction of fault trees from simulated and real sensor data, enabling automated diagnosis of multiple, early-stage failures before they escalate. Nolan’s seminal 1999 paper, “Monitoring and diagnosis of multiple incipient faults using fault tree induction,” introduces the DE/IFT engine, a novel approach that learns from classified sensor recordings to build diagnostic fault trees—a work that has garnered 20 citations. Earlier foundational research, “Diagnosis using fault trees induced from simulated incipient fault case data” (1994, 12 citations), demonstrated the practical application of the IFT algorithm on a pneumatic robot arm, using nonlinear dynamic simulations to validate the method. Together, these contributions have advanced the field of fault detection by providing a systematic, inductive framework for early diagnosis, with direct implications for reliability engineering and automated monitoring systems. Nolan’s work remains a reference point for researchers developing intelligent diagnostic tools.