Fuzzy Multivariable Gaussian Evolving Approach for Fault Detection and Diagnosis

The aim of this paper is to discuss the methodology of Fault Detection and Diagnosis (FDD) in dynamic systems. Fault diagnosis is characterized as a control system that controls the ability to adapt system component faults automatically. Fault detection is an implementation of using the error signals, where when error signal is zero or approximately zero. A coupled water tank system was used as a study case model for implementing and testing the proposed methodology. The developed system should generate a set of signals to notify the process operator about the faults that are occurring, enabling changes in control strategy or control parameters. Due to the damage risks involved with sensors, actuators and structural faults of the real plant, the data set of the faults are computationally generated and the results will be collected from numerical simulations of the process model. A Recurrent Neural Networks (RNN) is employed in this paper for modeling the used system. This paper shows how to determine the structure and how to estimate the results using the gradient-based algorithm which it is allowed to obtain a Neural Network with relatively small modeling uncertainly. It describes how to increase the stability of dynamic systems using FDD based on RNN. Faults in the study case are faults in the sensors, faults in the actuators and the structural faults. The faults which are presented in the controlled system are reduced to 98% from the default value.

Fault detection and diagnosis in process data using one-class support vector machines

There is an increasing demand for dynamic systems to become safer, more reliable and more economical in operation. This requirement extends beyond the normally accepted safety-critical systems e.g., nuclear reactors, aircraft and many chemical processes, to systems such as autonomous vehicles and some process control systems where the system availability is vital. The field of fault diagnosis for dynamic systems (including fault detection and isolation) has become an important topic of research. Many applications of qualitative and quantitative modelling, statistical processing and neural networks are now being planned and developed in complex engineering systems. Issues of Fault Diagnosis for Dynamic Systems has been prepared by experts in fault detection and isolation (FDI) and fault diagnosis with wide ranging experience.Subjects featured include: - Real plant application studies; - Non-linear observer methods; - Robust approaches to FDI; - The use of parity equations; - Statistical process monitoring; - Qualitative modelling for diagnosis; - Parameter estimation approaches to FDI; - Fault diagnosis for descriptor systems; - FDI in inertial navigation; - Stuctured approaches to FDI; - Change detection methods; - Bio-medical studies. Researchers and industrial experts will appreciate the combination of practical issues and mathematical theory with many examples. Control engineers will profit from the application studies.

Large non-residential buildings can contain complex and often inefficient water distribution systems. As requirements for water increase due to water scarcity and industrialization, it has become increasingly important to effectively detect and diagnose faults in water distribution systems in large buildings. In many cases, if water supply is not impacted, faults in water distribution systems can go unnoticed. This can lead to unnecessary increases in water usage and associated energy due to pumping, treating, and heating water. The majority of fault detection and diagnosis studies in the water sector are limited to municipal water supply and leakage detection. The application of detection and diagnosis for faults in building water networks remains largely unexplored and the ability to identify and distinguish between routine and non-routine water usage at this scale remains a challenge. This study using case-study data, presents the application of principal component analysis and a multi-class support vector machine to detect and classify faults for non-residential building water networks. In the absence of a process model (which is typical for such water distribution systems), principal component analysis is proposed as a data-driven fault detection technique for building water distribution systems for the first time herein. Hotelling T2-statistics and Q-statistics were employed to detect abnormality within incoming data, and a multi-class support vector machine was trained for fault classification. Despite the relatively limited training data available from the case-study (which would reflect the situation in many buildings), meaningful faults were detected, and the technique proved successful in discriminating between various types of faults in the water distribution system. The effectiveness of the proposed approach is compared to a univariate threshold technique by comparison of their respective performance in the detection of faults that occurred in the case-study site. The results demonstrate the promising capabilities of the proposed fault detection and diagnosis approach. Such a strategy could provide a robust methodology that can be applied to buildings to reduce inefficient water use, reducing their life-cycle carbon footprint.

Angular velocity sensor detection and diagnosis become increasingly essential for the improvement of reliability, safety, and efficiency of the control system on aircraft. The classical methods for fault detection and diagnosis are limit or trend checking of some measurable output variables. Due to they do not give a deeper insight and usually do not allow a fault diagnosis, model-based methods of fault detection and diagnosis were developed by using input and output signals and applying dynamic process models. These approaches are based on parameter estimation, parity equations, or state observers. This paper presents an improvement method to build algorithm fault diagnosis for angular velocity sensors on aircraft. Based on proposed method, results of paper can be used in designed intelligent systems that can automatically fault detection on aircraft.

This paper presents a fault detection and diagnosis approach based on Mahalanobis distance (MD) for monitoring continuous processes. The Mahalanobis distance is first used to detect sensor faults, and its control limit is determined by the empirical method via specification a significance level. The main idea behind Mahalanobis distance is using the whole data information instead of dimensionality techniques such as partial least squares, canonical variate analysis, and principal component analysis. The data reflected by the detection index, MD, is analysed by a fault diagnosis method, i.e, contributions plots. An integrated approach for fault detection and diagnosis is presented and tested by two simulation studies, Monte Carlo simulation data and continuous stirred tank reactor chemical process(CSTR). The results illustrate the reliability of the proposed approach in detecting and diagnosing sensor faults.

A deep learning unsupervised approach for fault diagnosis of household appliances

Various faults occurred in the continuous bulk materials weighing equipment (CBMWE) usually lead to more economic loss and waste of human resources inevitably. A new approach based on the improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering and Bayesian regularization neural network (BRNN) is proposed for online fault detection and diagnosis of CBMWE--electronic belt weigher (BW). Firstly, in view of the fault data varying with equipment flow, an improved DBSCAN clustering algorithm is developed to realize the online fault detection by extracting the fault data with the clustering analysis of the real-time data. Secondly, BRNN is proposed as a classifier to identify the fault pattern with the extracted fault data. Both the models of online fault detection and diagnosis are realized using MATLAB. Finally, the test result shows that the proposed online fault detection and diagnosis model is able to cope with the online fault detection and diagnosis of BW and also yields great diagnostic accuracy. In general, this approach for online fault detection and diagnosis of BW has a great significance to bulk weighing equipment.DOI: http://dx.doi.org/10.5755/j01.mech.21.1.8560

Accurate fault detection and diagnosis are critical components of any fault-tolerant control system, especially for unmanned aerial vehicles (UAVs) where reliability is paramount. Traditionally, both model-based and data-driven approaches have been applied for fault diagnosis. However, the increasing complexity of high-dimensional UAV systems has shifted focus toward data-driven methods, which leverage advanced classification algorithms to enhance fault identification and isolation. This study builds on this evolution by developing a sophisticated condition-based monitoring (CBM) system specifically designed for multirotor UAVs. In contrast to earlier studies that primarily relied on raw data for classifier training, this work introduces advanced preprocessing techniques and multidomain feature extraction, significantly improving the robustness and accuracy of fault detection. A comparative analysis is performed between feature-selection methods, including recursive feature elimination with cross-validation (RFECV) and variational autoencoder (VAE), to extract critical insights into UAV operational behavior. Through testing and evaluating various classification models on data from a hexarotor UAV under diverse actuator fault conditions, this research identifies optimal approaches for real-time fault detection and diagnosis. Results demonstrate notable improvements across all evaluation metrics, establishing this approach as a substantial advancement in UAV fault tolerance.

Fault diagnosis is an important task for the safe and optimal operation of chemical processes. Hence, this area has attracted considerable attention from researchers in the past few years. A variety of approaches have been proposed for solving this problem. All approaches for fault detection and diagnosis in some sense involve the comparison of the observed behavior of the process to a reference model. The process behavior is inferred using sensors measuring the important variables in the process. Hence, the efficiency of the diagnostic approach depends critically on the location of sensors monitoring the process variables. The emphasis of most of the work on fault diagnosis has been more on procedures to perform diagnosis given a set of sensors and less on the actual location of sensors for efficient identification of faults. A digraph‐based approach is proposed for the problem of sensor location for identification of faults. Various graph algorithms that use the developed digraph in deciding the location of sensors based on the concepts of observability and resolution are discussed. Simple examples are provided to explain the algorithms, and a complex FCCU case study is also discussed to underscore the utility of the algorithm for large flow sheets. The significance and scope of the proposed algorithms are highlighted.

In this paper, a high-gain nonlinear observer based fault detection and diagnosis (FDD) approach is proposed for a general class of nonlinear uncertain systems with measured output probability density functions (PDFs). The objective of the presented FDD algorithm is to use the measured output probability density functions (PDFs) and the input of the system to construct a exponential observer-based residual generator such that the fault can be detected and diagnosed. The main result is given in a constructive manner by developing a novel nonlinear observer, without resort to any linearization. By a coordinates transformation, the design of the proposed observer does not necessitate the resolution of kind of linear matrix inequalities (LMIs) and its expression is explicitly given. The exponential convergence of the errors in the presence of parameters uncertainties is proved to guarantee the fastness of the proposed fault diagnosis scheme. Furthermore, the bound of the estimation errors in the presence the faults is minimized by appropriately choosing the parameters of the presented observer. Finally a simulation example is given to illustrate the efficiency of the proposed fault detection and diagnosis method.

Large margin-learning methodology from time-frequency maps and its physically interpretable weights for simultaneous machine health monitoring and fault diagnosis

A new fault detection and diagnosis approach is developed in this paper for a class of singular nonlinear systems via the use of adaptive updating rules. Both detection and diagnostic observers are established, where Lyapunov stability theory is used to obtain the required adaptive tuning rules for the estimation of the process faults. This has led to stable observation error systems for both fault detection and diagnosis. A simulated numerical example is included to demonstrate the use of the proposed approach and encouraging results have been obtained.

This paper presents an efficient approach for automated fault detection and diagnosis of the distillation column by analyzing the gamma intensity variations. Gamma-ray scanning is one of the most common techniques that is used in industrial radioisotopes applications. It is used to get signals that reflect the inner details of a distillation column. These signals are gamma intensity variation that comes from material density variation in the column. In the proposed approach, the signals of each distillation column are divided into frames. Each frame contains the signal of just one column tray. Therefore, the position of the defected tray can be determined exactly in the column. Discrete wavelet transform, power density spectrum, higher order statistics (HOS), area under the curve (AUC) and correlation between signals are features that are extracted to be used in artificial neural network. The proposed results prove that the selection of HOS with correlation and AUC effectively achieves the fault detection and diagnosis approach in different high noise environments.

To online monitor process, a combined approach of fault detection and diagnosis based on Lifting Wavelets and Moving Window PCA (LW-MWPCA) was presented. Firstly the data were pre-processed to remove noise and spikes through lifting scheme wavelets, and then MWPCA was used to diagnose faults. To validate the performance and effectiveness of the proposed scheme, LW-MWPCA was applied to diagnose the faults in TE Process. The results were given to show the effectiveness of these improvements for fault diagnosis performance in terms of low computational cost and high fault diagnosis rate.