In this paper a fault detection analysis through a neural networks ensembling approach and statistical pattern recognition techniques is presented. Abnormal consumption or faults are detected by analyzing the residual values, which are the difference between the expected and the real operating data. The residuals are more sensitive to faults and insensitive to noise. In this study, first, the e...

Selecting the model is an important and essential step in model based fault detection and diagnosis (FDD). Factors that are considered in evaluating a model include accuracy, training data requirements, calibration effort, generality, and computational requirements. The objective of this study was to evaluate different modeling approaches for their applicability to model based FDD of vapor comp...

Process disturbances always spread along the connected equipment in a plant and are detected in many places. In order to identify the root disturbance, many data-based fault detection and diagnosis (FDD) methods have been developed in recent years. However, most of these methods can generate spurious solutions. Several authors have observed that FDD methods are enhanced if topology information ...

In this paper, ‎the multi-sensor fault diagnosis in the exhaust temperature sensors of a V94.2 heavy duty gas turbine is presented‎. ‎A Laguerre network-based fuzzy modeling approach is presented to predict the output temperature of the gas turbine for sensor fault diagnosis‎. Due to the nonlinear dynamics of the gas turbine, in these models the Laguerre filter parts are related to the linear d...

In this work, an effective Fault Detection and Diagnosis (FDD) strategy designed to increase the performance accuracy of fault diagnosis in grid-connected photovoltaic (GCPV) systems is developed. The evolved approach threefold: first, a pre-processing training dataset applied using multiscale scheme that decomposes data at multiple scales high-pass/low-pass filters separate noise from informat...

Motor fault detection and diagnosis involves processing a large amount of information of the motor system. With the combined synergy of fuzzy logic and neural networks, a better understanding of the heuristics underlying the motor fault detection/diagnosis process and successful fault detection/diagnosis schemes can be achieved. This paper presents two neural fuzzy (NN/FZ) inference systems, na...

Kim, Woohyun. Ph.D., Purdue University, December 2013. Fault Detection and Diagnosis for Air Conditioners and Heat Pumps based on Virtual Sensors. Major Professor: James E. Braun, School of Mechanical Engineering. The primary goal of this research is to develop and demonstrate an integrated, on-line performance monitoring and diagnostic system with low cost sensors for air conditioning and heat...

This paper addresses the design of an observer-based fault diagnosis scheme, which is applied to some of the sensors and actuators of a wind turbine benchmark model. The methodology is based on a modified sliding mode observer (SMO) that allows accurate reconstruction of multiple sensor or actuator faults occurring simultaneously. The faults are reconstructed using the equivalent output error i...