Input design and data-driven approaches based on convex optimization for fault diagnosis in linear systems

The complexity of automated systems has grown considerably during the past decades. This convolutes the observation of possible faults in these systems. If not being revealed timely, such faults can lead to catastrophic failures. As a result, there is a continuous interest in sophisticated fault diagnosis techniques. Since it is generally...

A Bayesian Approach for Active Fault Isolation with an Application to Leakage Localization in Water Distribution Networks

This article proposes an active fault isolation method for application to water distribution networks (WDNs) to localize leaks. The method relies on the classification of observed outputs to a discrete set of hypothetical faults. Due to parametric uncertainties, the outputs are random vectors that follow unknown probability distribution...

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...

Faults in consumer products are difficult to diagnose, and design is to blame: A user observation study

The process of fault diagnosis is an essential first step when repairing a product: it determines the condition of the parts and identifies the origin of failure. We report on how product users go through the process of fault diagnosis in consumer products and the influence of design features on this process. Two groups of 12 participants were...

Fault diagnosis of the 10MW Floating Offshore Wind Turbine benchmark: A mixed model and signal-based approach

Floating Offshore Wind Turbines (FOWTs) operate in the harsh marine environment with limited accessibility and maintainability. Not only failures are more likely to occur than in land-based turbines, but also corrective maintenance is more expensive. In the present study, a mixed model and signal-based Fault Diagnosis (FD) architecture is...

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...

Practices of fault diagnosis in household appliances: Insights for design

Fault diagnosis is the process of identifying and characterising a fault when a failure occurs. It is, therefore, an essential step to take before product-repair. In this study, we ask how conventional users diagnose faults in household appliances and how the design of these appliances facilitates or hampers the process of fault diagnosis.To...

Scalable distributed sensor fault diagnosis for smart buildings

The enormous energy use of the building sector and the requirements for indoor living quality that aim to improve occupants'productivity and health, prioritize Smart Buildings as an emerging technology. The Heating, Ventilation and Air-Conditioning ( HVAC ) system is considered one of the most critical and essential parts in buildings since...

Planetary gear fault diagnosis via feature image extraction based on multi central frequencies and vibration signal frequency spectrum

Poor working environment leads to frequent failures of planetary gear trains. However, complex structure and variable transmission make the vibration signal strongly non-linear and non-stationary, which brings big problems to fault diagnosis. A method of planetary gear fault diagnosis via feature image extraction based on multi central...

Automatic Fault Diagnosis and Prediction of Wind Turbines

A Morlet wavelet-based compensated algorithm was proposed to calculate the accurate amplitudes of faulty signals. The specific way is to compute the time range and frequency values of the faulty signals at first, and then to compensate the amplitudes calculated for above faulty signals according to the center frequency values of Morlet...

Bayesian and Dempster–Shafer reasoning for knowledge-based fault diagnosis: A comparative study

Even though various frameworks exist for reasoning under uncertainty, a realistic fault diagnosis task does not fit into any of them in a straightforward way. For each framework, only part of the available data and knowledge is in the desired format. Moreover, additional criteria, like clarity of inference and computational efficiency,...

Railway track circuit fault diagnosis using recurrent neural networks

Timely detection and identification of faults in railway track circuits are crucial for the safety and availability of railway networks. In this paper, the use of the long-short-term memory (LSTM) recurrent neural network is proposed to accomplish these tasks based on the commonly available measurement signals. By considering the signals from...

Combining knowledge and historical data for system-level fault diagnosis of HVAC systems

Interdependencies among system components and the existence of multiple operating modes present a challenge for fault diagnosis of Heating, Ventilation, and Air Conditioning (HVAC) systems. Reliable and timely diagnosis can only be ensured when it is performed in all operating modes, and at the system level, rather than at the level of the...

Fault diagnosis and maintenance optimization for interconnected systems: With applications to railway and climate control systems

For many systems, like medical devices, nuclear reactors, and transportation systems, an adequate maintenance optimization approach is essential to ensure high levels of reliability and safety while keeping operational costs low. A promising approach towards this goal is condition-based maintenance, which plans maintenance only when the system...

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...