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The ability to detect when a system undergoes an incipient fault is of paramount importance in preventing a critical failure. Classic methods for fault detection (including model-based and data-driven approaches) rely on thresholding error…

Accurate fault location is essential for operational reliability and fast restoration in wind farm collector networks. However, the growing integration of inverter-based resources changes the current and voltage behavior during faults,…

This paper addresses the problem of domain shifts in electric motor vibration data created by new operating conditions in testing scenarios, focusing on bearing fault detection and diagnosis (FDD). The proposed method combines the Harmonic…

In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with machine learning (ML) and data mining techniques. While conventional ML methods, including artificial neural network (ANN), principal…

This paper investigates runtime monitoring of perception systems. Perception is a critical component of high-integrity applications of robotics and autonomous systems, such as self-driving cars. In these applications, failure of perception…

The detection and characterization of partial discharge (PD) are crucial for the insulation diagnosis of overhead lines with covered conductors. With the release of a large dataset containing thousands of naturally obtained high-frequency…

Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing…

The electric power transformer is a critical component in electrical distribution networks, and the diagnosis of faults in transformers is an important research area. Frequency Response Analysis (FRA) methods are widely used for analyzing…

This paper focuses on sensor fault detection and compensation for robotic manipulators. The proposed method features a new adaptive observer and a new terminal sliding mode control law established on a second-order integral sliding surface.…

Rotating machines like engines, pumps, or turbines are ubiquitous in modern day societies. Their mechanical parts such as electrical engines, rotors, or bearings are the major components and any failure in them may result in their total…

The increased presence of advanced sensors on the production floors has led to the collection of datasets that can provide significant insights into machine health. An important and reliable indicator of machine health, vibration signal…

Today, interest in automotive applications notably Hybrid Electric Vehicles (HEV) has risen due to environmental concerns and the modern society's energetic dependence. Consequently, it is necessary to study and implement in these vehicle…

Most wind turbines are remotely monitored 24/7 to allow for an early detection of operation problems and developing damage. We present a new fault detection method for vibration-monitored drivetrains that does not require any feature…

Reliability is necessary in safety-critical applications spanning numerous domains. Conventional hardware-based fault tolerance techniques, such as component redundancy, ensure reliability, typically at the expense of significantly…

This paper addresses the detection of periodic transients in vibration signals for detecting faults in rotating machines. For this purpose, we present a method to estimate periodic-group-sparse signals in noise. The method is based on the…

This paper presents a novel method to identify and insert redundant logic into a combinational circuit to improve its fault tolerance without having to replicate the entire circuit as is the case with conventional redundancy techniques. In…

This paper employs a supervised machine learning (ML) algorithm to propose an integrated fault detection and diagnosis (FDD) and fault-tolerant control (FTC) strategy to detect, diagnose, and classify the grid faults and correct the input…

This paper proposes a novel graph-based framework for robust and interpretable multiclass fault diagnosis in rotating machinery. The method integrates entropy-optimized signal segmentation, time-frequency feature extraction, and…

Dimensionality Reduction plays a pivotal role in improving feature learning accuracy and reducing training time by eliminating redundant features, noise, and irrelevant data. Nonnegative Matrix Factorization (NMF) has emerged as a popular…

This paper proposes a Decision Tree (DT) based detection and classification of internal faults in a power transformer. The faults are simulated in Power System Computer Aided Design (PSCAD)/ Electromagnetic Transients including DC (EMTDC)…