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Enhancing Systems Health Monitoring by Embedding Failure Mode and Effect Analysis

Enhancing Systems Health Monitoring by Embedding Failure Mode and Effect Analysis

Presented at the 10th Annual BayesiaLab Conference on Tuesday, October 25, 2022.

Abstract

Aeronautics research is at the forefront with the advent of electric vertical takeoff and landing (eVOTL) vehicles as a mode of transport. These vehicles are expected to travel in low-altitude airspace, from small drones for package delivery to larger, on-demand, urban air mobility (UAM) vehicles. The foreseeable high traffic density suggests that many of these electric propulsion systems will enter the airspace and that they will also operate at high frequency. The reliability of such critical systems is, therefore, key to ensuring high safety standards in low-altitude airspace, especially when moving in dense urban environments. Diagnostic systems, which aim at identifying incipient faults, can mitigate unexpected failures by performing early fault detection in critical systems through monitoring. The proposed approach leverages a combination of failure mode and effect analysis (FMEA) integrated with Bayesian networks, thus introducing dependability structures into a diagnostic framework.

Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded within FMEAs. A key element of fault diagnosis is fault detection and isolation (FDI), which increases in complexity with the complexity of the system itself, namely the number of subsystems and components, interactions among sub-systems, and sensor availability. The developed framework enables the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor data. This is demonstrated through a case study applied to the electric powertrain system of a small, rotary-wing unmanned aerial vehicle (UAV). The proposed work integrates the early design phase of an electric propulsion system with diagnostic tools, often developed later in the product lifecycle. Failure mode and effect analysis (FMEA) derived for the system in the design phase is embedded within a Bayesian network (BN).

Presentation Video

About the Presenter

Chetan S. Kulkarni is a staff researcher at the Prognostics Center of Excellence and the Diagnostics and Prognostics Group in the Intelligent Systems Division at NASA Ames Research Center. His current research interests are in Systems Diagnostics, Prognostics, and Health Management. Specifically focused on developing physics-based models, prognostics of electronic systems, energy systems, exploration ground systems, and hybrid systems.

He completed his MS ('09) and Ph.D. ('13) from Vanderbilt University, TN where he was a Graduate Research Assistant with the Institute for Software Integrated Systems and the Department of Electrical Engineering and Computer Science. He completed his BE (02) from the University of Pune, India. Before joining Vanderbilt, he was a Research Fellow at the Department of Electrical Engineering, IIT-Bombay, where his research work focused on developing low-cost substation automation system monitoring and control devices and partial discharge of high voltage transformers. Earlier he was a member of the technical team of the Power Automation group at Honeywell, India, where he was involved in turnkey power automation projects and product development in substation automation.

He is KBR Technical Fellow and AIAA Associate Fellow. Associate Editor for IEEE, SAE, and IJPHM Journals on topics related to Prognostics and Systems Health Management. He has been the Technical Program Committee co-chair at PHME18, PHM20-22. And co-chairs the Professional Development and Education Outreach subcommittee in the AIAA Intelligent Systems Technical Committee.


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