Fault detection for LTI systems using data-driven dissipativity analysis

Motivated by the physical exchange of energy and its dissipation in electro-mechanical systems, we propose a new fault detection method based on data-driven dissipativity analysis. We first identify a dissipativity inequality using one or multiple shots of data obtained from a linear time-invariant system. This dissipativity inequality's...

Comprehensive Study on Transformer Fault Detection via Frequency Response Analysis

The sudden outage of a transformer due to a fault can cause irreparable damage to the electricity industry. Hence, by conducting momentarily inspections of the transformer's condition, faults can be promptly detected, and the transformer can be disconnected from the power grid to prevent subsequent failures in this equipment. Detecting faults...

A sliding mode observer approach to oscillatory fault detection in commercial aircraft

The Flight Control System (FCS) is one of the most important systems in all modern aircraft. For such systems it is required to have robust Fault Detection Isolation and Reconfiguration (FDIR) functionalities with high detection performance. In this work we specifically consider the Oscillatory Failure Cases (OFC), which, if not mitigated,...

Non-Homogeneous Sampling Rate Wide Area Backup Protection using Synchrophasors and IED Data

Fault currents may result in cascading failures and even system collapse if not detected and cleared on time. To account for the possibility of failure of primary protection under stressed system conditions, an extra layer of protection is commonly employed, referred to as backup protection. This paper introduces an effective formulation for...

Real-time Fault Estimation for a Class of Discrete-Time Linear Parameter-Varying Systems

Estimating and detecting faults is crucial in ensuring safe and efficient automated systems. In the presence of disturbances, noise, or varying system dynamics, such estimation is even more challenging. To address this challenge, this letter proposes a novel filter to estimate multiple fault signals for a class of discrete-time linear...

Fault diagnosis in low voltage smart distribution grids using gradient boosting trees

In this paper, a gradient boosting tree model is proposed to detect, identify and localize single-phase-to-ground and three-phase faults in low voltage (LV) smart distribution grids. The proposed method is based on gradient boosting trees and considers branch-independent input features to be generalizable and applicable to different grid...

A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids

Power outages in electrical grids can have very negative economic and societal impacts rendering fault diagnosis paramount to their secure and reliable operation. In this paper, deep neural networks are proposed for fault detection and location in low-voltage smart distribution grids. Due to its key properties, the proposed method solves some...

Fault isolation for large scale discrete-time systems based on implicit set representation

To detect faults in a system we can adopt an observer, designed for the healthy system, and monitor the discrepancy between actual and expected behaviour of the residual (difference between the system output and its estimate). To isolate faults, we can compute the invariant sets associated with each fault, and their projection in the residual...

Detection and isolation of replay attacks through sensor watermarking

This paper addresses the detection and isolation of replay attacks on sensor measurements. As opposed to previously proposed additive watermarking, we propose a multiplicative watermarking scheme, where each sensor's output is separately watermarked by being fed to a SISO watermark generator. Additionally, a set of equalizing filters is...

A message passing algorithm for automatic synthesis of probabilistic fault detectors from building automation ontologies

Model-based fault diagnosis has been indicated as a fundamental method in enabling optimal maintenance of buildings, thus leading to important energy savings. However, accurate models of buildings and their technical equipment are seldom available, and this lack of knowledge applies even more to descriptions of modeling and measurement...

Evaluation of physical damage associated with action selection strategies in reinforcement learning

Reinforcement learning techniques enable robots to deal with their own dynamics and with unknown environments without using explicit models or preprogrammed behaviors. However, reinforcement learning relies on intrinsically risky exploration, which is often damaging for physical systems. In the case of the bipedal walking robot Leo, which is...

Fault diagnosis using spatial and temporal information with application to railway track circuits

Adequate fault diagnosis requires actual system data to discriminate between healthy behavior and various types of faulty behavior. Especially in large networks, it is often impracticable to monitor a large number of variables for each subsystem. This results in a need for fault diagnosis methods that can work with a limited set of monitoring...