Failures can lead to a series of problems in the complex heating, ventilation and air-conditioning (HVAC) systems in buildings. Fault detection and diagnosis (FDD) is an important technology to solve these problems. Models can represent the behaviors of the HVAC systems, and FDD can be realized with models. Using the model as intermediary, a link between system simulation and FDD can be built. ...

This paper describes a model-based control system that can online determine the optimal control actions and also detect faults quickly in the controlled process and reconfigure the controller accordingly. Thus, such system can perform its function correctly even in the presence of internal faults. A fault detection modelbased (FDMB) controller consists of two main parts, the first is fault dete...

Fault detection and diagnosis (FDD) systems enable high cost savings energy that could have economic environmental impact. This study aims to develop validate a data-driven FDD system for chiller. The uses historical operation data capture quantitative correlations among variables. evaluated the effectiveness robustness of eight classification methods based on experimental chiller (the ASHRAE 1...

Nonintrusive load monitoring (NILM) is accomplished by sampling voltage and current at high rates and reducing the resulting start transients or harmonic contents to concise “signatures.” Changes in these signatures can be used to detect, and in many cases directly diagnose, equipment and component faults associated with rooftop cooling units. Use of the NILM for fault detection and diagnosis (...

In contrast to critical systems, one of the primary consequences of faults in HVAC systems is economic rather than safety-related. Therefore, FDD systems applied to HVAC systems must be assessed based upon economic considerations. However, existing research in this application has mainly focused on technique development and validation. This paper addresses the economics of FDD application to HV...

Nuclear power plants (NPPs) are complex dynamic systems with multiple sensors and actuators. The presence of faults in the actuators can deteriorate system’s performance cause serious safety issues. This calls for development fault detection diagnosis isolation such faults. In this study, (FDD) based on neural networks (NN) K-nearest neighbour (KNN) algorithm is applied to a pressurized water r...

Fault detection and diagnosis (FDD) systems can reduce high costs energy consumption. This paper presents a machine learning-based fault technique for actuators sensors in pressurized water reactor (PWR). In the proposed FDD framework, faults are first detected using shallow neural network. Second, is performed 15 different classifiers provided MATLAB Classification Learner toolbox, including s...

This paper investigates the problem of fault detection and diagnosis (FDD) problem for non-Gaussian singular stochastic distribution control (SDC) systems via the output probability density functions(PDFs). The PDFs can be approximated by using square-root B-spline expansion, via this expansions to represent the dynamics weighting systems between the system input and the weights related to the ...

This paper describes the application of Principal Component Analysis (PCA) for fault detection and diagnosis (FDD) in a real plant. PCA is a linear dimensionality reduction technique. In order to diagnosis the faults, the PCA approach includes one PCA model for each system behavior, i.e., a PCA model for normal operation conditions and a PCA model for each faulty situation. Data set is generate...

Many Fault Detection and Diagnosis (FDD) systems use discrete models for detection and reasoning. To obtain categorical values like ”oil pressure too high”, analog sensor values need to be discretized using a suitable threshold. This task is usually performed by the “wrapper code” of the FDD system. In practice, selecting the right threshold is very difficult, because it heavily influences the ...