Performance Bounds for Neural Network Estimators: Applications in Fault Detection

• URL: http://arxiv.org/abs/2103.12141v1
• Date: Mon, 22 Mar 2021 19:23:08 GMT
• Title: Performance Bounds for Neural Network Estimators: Applications in Fault Detection
• Authors: Navid Hashemi, Mahyar Fazlyab, Justin Ruths
• Abstract summary: We exploit recent results in quantifying the robustness of neural networks to construct and tune a model-based anomaly detector. In tuning, we specifically provide upper bounds on the rate of false alarms expected under normal operation.
• Score: 2.388501293246858
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We exploit recent results in quantifying the robustness of neural networks to input variations to construct and tune a model-based anomaly detector, where the data-driven estimator model is provided by an autoregressive neural network. In tuning, we specifically provide upper bounds on the rate of false alarms expected under normal operation. To accomplish this, we provide a theory extension to allow for the propagation of multiple confidence ellipsoids through a neural network. The ellipsoid that bounds the output of the neural network under the input variation informs the sensitivity - and thus the threshold tuning - of the detector. We demonstrate this approach on a linear and nonlinear dynamical system.

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