Improved Random Forest Algorithm Based on Decision Paths for Fault Diagnosis of Chemical Process with Incomplete Data.

Hybrid modeling based double-granularity fault detection and diagnosis for quadrotor helicopter

With growing realization of the benefits brought by fault detection and diagnosis(FDD), a large body of research pertinent to FDD methods for centrifugal chillers has been conducted during the past two decades. However, at the present time, relatively few FDD methods can handle multiple simultaneous faults on centrifugal chillers. This article describes the development of some decoupling features, which are the basis of a decoupling-based FDD methodology that can handle multiple simultaneous faults on centrifugal chillers. The performance of the decoupling features were validated and evaluated by the laboratory data from a 90-ton centrifugal chiller tested in a laboratory environment.

Fault detection and diagnosis based on transfer learning for multimode chemical processes

Adaptive fault detection and diagnosis using parsimonious Gaussian mixture models trained with distributed computing techniques

Fault detection and diagnosis for non-linear processes empowered by dynamic neural networks

Electric Vehicles (EVs) are emerging as the preferred mode of transportation owing to various advantages and to reach the Sustainable Development Goals (SDGs). However, the commercial usage of EVs is in its initial stage, due to multiple factors, among which faults caused by the operational environment and component failures are one. Furthermore, early diagnosis of such faults is necessary to protect from severe damage and catastrophic failures. This paper presents various fault modes in EV components, their impact on EV performance, and Fault Detection and Diagnosis (FDD) practices. Later sections of the paper focus on the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques in FDD. Also, it provides a comparative analysis of model-based, signal-based, data-driven, and hybrid FDD approaches. Additionally, the paper explores component-specific FDD techniques, modelling of drive faults, current research trends, and suggestions on the selection of feature extraction techniques. Furthermore, the paper highlights the need to develop a hybrid FDD method to enhance the accuracy, reliability, and speed of FDD in EVs. The future scope of EV FDD techniques, considering various aspects, is also presented.

This report summarizes a comprehensive literature review for the selected sensor suites used in building controls and fault detection and diagnostics (FDD), which is a Q2 deliverable of the Sensor Impact Evaluation and Verification project. The project’s overarching goal is to develop a framework for investigating the impact of sensor deployment and configuration for building energy optimization, fault detection and diagnosis (FDD), occupants’ thermal comfort, and potential grid efficiency. While the first phase of the literature review performed in Q1 reviewed the current methods of various sensor selection and placements and their impact, this report focuses on high impactful potential use cases for the selected sensor suites in building control and FDD. The use cases identified in this literature review will be further investigated and be included in the final sensor impact evaluation framework. For the review, the multi-laboratory team reviewed 241 research papers, technical reports, and books to identify existing technologies, the potential impact on energy savings and thermal comfort, and future research opportunities for the selected use cases in building controls and FDD. Along with the literature review, the team conducted a series of interviews with experts in sensors and controls area to identify the state-of-art sensor technologies, and the most impactful use cases in controls and FDD areas. The interview results will also be incorporated with the findings from the literature review in the next quarter, and the final use cases to be evaluated will be selected.

Modular Multilevel Cascade Converters (MMCCs) are considered a promising power electronics topology in industry. Their scalability allows to reach (ultra/very) high voltage levels with low harmonic content and high efficiency and makes MMCCs an ideal solution for high-power applications; such as electrical drives, solid-state transformers and high-voltage direct-current (HVDC) transmission systems. However, the high levels of thermal, electrical and mechanical stress on the power electronics devices and the large number of components (e.g. capacitors or semiconductors) make MMCCs prone to faults. Fault detection and diagnosis (FDD) in combination with fault isolation and system reconfiguration techniques, based on cell redundancy, can increase the reliability, availability and safety of MMCCs, which is crucial for their utilization in critical energy applications. This second part of the paper comprehensively surveys: (i) fault tolerance and fault detection & diagnosis (FDD; e.g. expert system, model- or hardware and data-based FDD methods) and (ii) system reconfiguration strategies (e.g. cold- or hot-redundant) for MMCCs. Finally, the state-of-the-art, challenges and future research trends and opportunities towards reliable MMCC-based systems are revealed.

This paper presents a new scheme for fault detection and diagnosis in spacecraft Attitude Determination (AD) sensors. An integrated attitude determination system, which includes measurements of rate and angular position using rate gyros and vector sensors, is developed. Measurement data from all sensors are fused by a linearized Kalman filter, which is designed based on the system kinematics, to provide attitude estimation and the values of the gyro bias. Using this information the erroneous sensor measurements are corrected, and unbounded sensor measurement errors are avoided. The resulting bias-free data are used in the Fault Detection and Diagnosis (FDD) scheme. The FDD algorithm uses model-based state estimation, combining the information from the rotational dynamics and kinematics of a spacecraft with the sensor measurements to predict the future sensor outputs. Fault isolation is performed through Extended Kalman Filters (EKFs). The innovation sequences of EKFs are monitored by several statistical tests to detect the presence of a failure and to localize the failures in all AD sensors. The isolation procedure is developed in two phases. In the first phase, two EKFs are designed, which use subsets of measurements to provide state estimates and form residuals, which are used to verify the source of the fault. In the second phase of isolation, testing of multiple hypotheses is performed. The generalized likelihood ratio test is utilized to identify the faulty components. In the scheme developed in this paper a relatively small number of hypotheses is used, which results in faster isolation and highly distinguishable fault signatures. An important feature of the developed FDD scheme is that it can provide attitude estimations in presence of fault in one type of sensors.

A framework to automate fault detection and diagnosis based on moving window principal component analysis and Bayesian network

The malfunctioning of the heating, ventilating, and air conditioning (HVAC) system is considered to be one of the main challenges in modern buildings. Due to the complexity of the building management system (BMS) with operational data input from a large number of sensors used in HVAC system, the faults can be very difficult to detect in the early stage. While numerous fault detection and diagnosis (FDD) methods with the use of statistical modeling and machine learning have revealed prominent results in recent years, early detection remains a challenging task since many current approaches are unfeasible for diagnosing some HVAC faults and have accuracy performance issues. In view of this, this study presents a novel hybrid FDD approach by combining random forest (RF) and support vector machine (SVM) classifiers for the application of FDD for the HVAC system. Experimental results demonstrate that our proposed hybrid random forest–support vector machine (HRF–SVM) outperforms other methods with higher prediction accuracy (98%), despite that the fault symptoms were insignificant. Furthermore, the proposed framework can reduce the significant number of sensors required and work well with the small number of faulty training data samples available in real-world applications.

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.

Physics theory integrated machine learning models enhance the interpretability and performance of artificial intelligence (AI) techniques to real-world industrial applications, such as the fault detection and diagnosis (FDD) of air handling units (AHU). Traditional machine learning-based automated FDD model demonstrates a high classification accuracy with sufficient training data samples, however, suffers from physical interpretation of the machine learning models. In this article, a physical model integrated Wasserstain generative adversarial network (WGAN) model is presented for AHU FDD with a scenario of insufficient training data samples. The proposed solution tackles the real-world problem of AHU FDD and enhances the model interpretability significantly. A transformer-WGAN model is designed to further improve the proposed FDD framework. Experimental results show that the proposed method outperforms existing AHU FDD methods with imbalanced real-world training data samples.

The problem of fault detection and diagnosis (FDD) in dynamic systems has received considerable attention in last decades due to the growing complexity of modern engineering systems and ever increasing demand for fault tolerance, cost efficiency, and reliability (Willsky, 1976; Basseville, 1988). Existing FDD approaches can be roughly divided into two major categories including model-based and knowledge-based approaches (Venkatasubramanian et al., 2003a; Venkatasubramanian et al., 2003b). Model-based approaches make use of the quantitative analytical model of a physical system. Knowledgebased approaches do not need full analytical modeling and allow one to use qualitative models based on the available information and knowledge of a physical system. Whenever the mathematical models describing the system are available, analytical model-based methods are preferred because they are more amenable to performance analysis. Generally, there are two steps in the procedure of model-based FDD. First, on the basis of the available observations and a mathematical model of the system, the state variable x and test statistics are required to be obtained. Then, based on the generated test statistics, it is required to decide on the potential occurrence of a fault. For linear and Gaussian systems, the Kalman filter (KF) is known to be optimal and employed for state estimation. The innovations from the KF are used as the test statistics, based on which hypothesis tests can be carried out for fault detection (Belcastro & Weinstein, 2002). In reality, however, the models representing the evolution of the system and the noise in observations typically exhibit complex nonlinearity and non-Gaussian distributions, thus precluding analytical solution. One popular strategy for estimating the state of such a system as a set of observations becomes available online is to use sequential Monte-Carlo (SMC) methods, also known as particle filters (PFs) (Doucet et al., 2001). These methods allow for a complete representation of the posterior probability distribution function (PDF) of the states by particles (Guo & Wang, 2004; Li & Kadirkamanathan, 2001). The aforementioned FDD strategies are single-model-based. However, a single-model-based FDD approach is not adequate to handle complex failure scenarios. One way to treat this

In this paper, a fault detection and diagnosis (FDD) scheme is presented for general stochastic dynamic systems with time delays. Originated from some practical processes, it is supposed that the measured information is the probability density function (PDF) of the system output rather than its actual value, which is different from the classical FDD problem. A B-spline expansion technique is applied so that the output PDF can be formulated in terms of the dynamic weights of the B-spline expansion. This leads to a dynamic model between the input and the weights where nonlinearities, uncertainties and time delays are included. As a result, the concerned FDD problem is transformed into a classic nonlinear FDD problem subject to an uncertain nonlinear system with time delays. Feasible criteria to detect the system fault are obtained and an adaptive fault diagnosis method is further presented to produce an estimate of the size of fault. Simple simulations are given to demonstrate the efficiency of the proposed approach.