SPIKE-Dx A Low-Power High-Throughput Fault Diagnostics Tool using Spiking Neural Networks for Constrained Systems

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Published Nov 5, 2024
Chetan Kulkarni Johann Schumann Anupa Bajwa

Abstract

Diagnostic systems are important for many aerospace systems, which are severly limited in available power, like cubesats or UAVs. Therefore, traditional diagnostics systems cannot be used due to their substantial footprint and constraints. In this paper, we present our very low power diagnostic tool SPIKE-Dx. to monitor critical systems with constrained computational and energy resources. This is made possible through spiking neural networks (SNNs), which are executable within optimized simulation environments and further implemented on on cutting-edge neuromorphic hardware.

Based upon FMEA (Failure Mode and Effect Analysis) framework, Diagnostic Bayesian Networks (DBNs) can be constructed that provide powerful means for diagnostic reasoning. In this paper, we describe such DBNs and a method to automatically translate the DBN into highly structured networks of spiking neurons for execution in SPIKE-Dx.

How to Cite

Kulkarni, C., Schumann, J., & Bajwa, A. (2024). SPIKE-Dx: A Low-Power High-Throughput Fault Diagnostics Tool using Spiking Neural Networks for Constrained Systems. Annual Conference of the PHM Society, 16(1). https://doi.org/10.36001/phmconf.2024.v16i1.4154
Abstract 94 | PDF Downloads 67

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Keywords

Spiking Neural Networks, Diagnosis, FMEA, Diagnostic Bayesian Networks

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Section
Technical Research Papers

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