Process Fault Detection and Diagnosis by Synchronous and Asynchronous Decentralized Kalman Filtering using State-Vector Fusion Technique

This work presents a framework for fault detection and diagnosis of multimode industrial processes. It is shown that the proposed method to generate features for the faults provides a generalization that can allow the fault diagnosis in modes for which no training was performed. The proposed method is applied to a sophisticated simulator of a continuous stirred tank reactor (CSTR). Experimental results suggest the usefulness of this approach for the detection of faults under operating conditions not considered during training phase.

A Real-Time Fuzzy, Deep-knowledge Based Fault-diagnosis System for a CSTR

In the recent era of the Internet of Things, the dominant role of sensors and the Internet provides a solution to a wide variety of real-life problems. Such applications include smart city, smart healthcare systems, smart building, smart transport and smart environment. However, the real-time IoT sensor data include several challenges, such as a deluge of unclean sensor data and a high resource-consumption cost. As such, this paper addresses how to process IoT sensor data, fusion with other data sources, and analyses to produce knowledgeable insight into hidden data patterns for rapid decision-making. This paper addresses the data processing techniques such as data denoising, data outlier detection, missing data imputation and data aggregation. Further, it elaborates on the necessity of data fusion and various data fusion methods such as direct fusion, associated feature extraction, and identity declaration data fusion. This paper also aims to address data analysis integration with emerging technologies, such as cloud computing, fog computing and edge computing, towards various challenges in IoT sensor network and sensor data analysis. In summary, this paper is the first of its kind to present a complete overview of IoT sensor data processing, fusion and analysis techniques.

This chapter addresses the problem of time-varying nonlinear modeling and monitoring of a continuously stirred tank reactor (CSTR) process using state estimation techniques. These techniques include the extended Kalman filter (EKF), particle filter (PF), and the more recently the variational Bayesian filter (VBF). The objectives of this chapter are threefold. The first objective is to use the variational Bayesian filter with better proposal distribution for nonlinear states and parameters estimation. The second objective is to extend the state and parameter estimation techniques to better handle nonlinear and non-Gaussian processes without a priori state information, by utilizing a time-varying assumption of statistical parameters. The third objective is to apply the state estimation techniques EKF, PF and VBF for time-varying nonlinear modeling and monitoring of CSTR process. The estimation performance is evaluated on a synthetic example in terms of estimation accuracy, root mean square error and execution times.

This paper investigates the application of multisensor data fusion (MSDF) technique to enhance the state estimation of a nonlinear plant. The proposed method is based on Kalman filters approach to improve the state estimation obtained by the novel adaptive unscented Kalman filter (AUKF). The common trend for the KF implementation assumes pre-specified fixed distribution matrices for both process and measurement noises. Here, however, the variance matrices for both process and measurement noise signals are assumed unknown a priori and thus incrementally estimated and updated using a sliding time window paradigm within which an estimation of the noise variance is calculated and adaptively updated each time the window is shifted forward. The proposed methodology is tested on a simulated continuous stirred tank reactor (CSTR) problem to estimate 4 states of this nonlinear plant. The simulation results demonstrate the superiority of the suggested method in state estimation compared with a previously reported approach.

Process fault detection and diagnosis using neural networks—I. steady-state processes

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.

The development of schemes for effectively examining unmeasured disturbances and plant-model mismatch in nonlinear model predictive control (NMPC) is an important area, which has attracted considerable attention over the past decade. In this work, we propose NMPC formulations for improving regulatory performance using nonlinear state space models identified from input-output data. The NARX-type MISO and MIMO nonlinear models identified in Srinivasarao et al.(17) are used to develop the state estimators and the predictors in the proposed NMPC formulations. We show that these models provide information about the covariances of the fast changing disturbances that are necessary for developing an extended Kalman filter (EKF) used in an NMPC scheme (EKF-NMPC). Rather than formulating an EKF to reject these disturbances, which we show has certain drawbacks, we proceed to propose an alternate prediction scheme based on the direct use of the identified noise model for future trajectory predictions. We show that the resulting NMPC formulation (DC-NMPC) is relatively easy to tune to achieve disturbance rejection as well as offset removal. The efficacy of the proposed control schemes is demonstrated using simulation studies on two benchmark control problems: (a) regulatory control of a continuously stirred tank reactor (CSTR) exhibiting input multiplicity at a singular point, and (b) servo control of a CSTR exhibiting output multiplicities. The analysis of simulation results reveals that the proposed EKF-NMPC and DC-NMPC schemes consistently generate significantly better regulatory performances, when compared to the performance of a conventional NDMC-type formulation, even in the presence of significant plant-model mismatch. In particular, the DC-NMPC performs significantly better than the EKF-NMPC formulation and can be tuned to achieve excellent regulatory performance without causing excessive input variability using parameters of a single disturbance filter. The conclusions reached through simulation studies are validated by conducting servo and regulatory control experiments on a laboratory-scale heater-mixer setup.

Switch Open-circuit Fault Diagnosis and Fault-Tolerant Control for Boost DC-DC Converter

Fault diagnosis and tolerance are crucial for monitoring system health and ensuring stability in industrial processes. Challenges arise in designing fault diagnostic solutions for real-time industrial processes with inherent nonlinear dynamic behaviors, particularly when dealing with multiple operating regions characterized by varying dynamics. This article addresses this challenge and proposes a fault diagnostic and tolerant control scheme for industrial systems. The proposed approach integrates a fuzzy-based realization technique with a subspace-aided methodology to effectively handle the nonlinear dynamic behavior observed across different operational scenarios. A practical solution is presented, significantly reducing the computational burden associated with online diagnostics, as the parity vectors are computed offline using available input–output data for different operating regions. During online diagnostics, only computed parity spaces are used with fuzzy realizations for residual generation, leading to a significant reduction in online computation. Numerical examples demonstrate the effectiveness of the proposed method, achieving a high precision rate in fault diagnostics. Furthermore, the diagnostic methodology is integrated with fault-tolerant control for practical applications, as demonstrated in the application of a continuous stirred tank reactor. This integration enables the system to effectively tolerate faults and ensure sub-optimal operation of the industrial process.

Detection and isolation of faults is a crucial step in the physical realization of quantum circuits. Even though quantum gates and circuits compute reversible functions, the standard techniques of automatic test pattern generation (ATPG) for classical reversible circuits are not directly applicable to quantum circuits. For faulty quantum circuits under the widely accepted single fault assumption, we show that their behavior can be fully characterized by the (single) faulty gate and the corresponding fault model. This allows us to efficiently determine test input states as well as measurement strategy for fault detection and diagnosis. Building on top of these, we design randomized algorithms which are able to detect every nontrivial single-gate fault with minimal probability of error. We also describe similar algorithms for fault diagnosis. We evaluate our algorithms by the number of output samples that needs to be collected and the probability of error. Both of these can be related to the eigenvalues of the operators corresponding to the circuit gates. We experimentally compare all our strategies with the state-of-the-art ATPG techniques for quantum circuits under the “single missing faulty gate” model and demonstrate that significant improvement is possible if we can exploit the quantum nature of circuits.

Multiscale PLS-based GLRT for fault detection of chemical processes

The following paper presents an improved self-tuned Model-based Predictive Controller (MPC) with constraints through an Extended Kalman Filter (EKF). The algorithm is tested in a Continuous Stirred Tank Reactor (CSTR) process for temperature and molar concentration regulations. The proposed method was developed from a previous work [1], where the MPC was tuned from error and control weights of equal value. A posterior analysis demonstrated that the MPC tuning was not robust. Sometimes it generated negative weight values due to the EKF convergence and the algorithm cannot always assure a global minimum value at performance index. Therefore, this work considers this problem and always generates positive weights which assures a minimum global value in the cost function of the MPC.

The problem of robust model-based diagnosis of process faults is addressed by means of artificial neural networks. Different structures and learning methods are investigated for both approaches to function approximation and pattern recognition. Main emphasis is placed upon static and dynamic neural nets that are used as predictors of nonlinear models for symptom generation. Dynamic neural networks are properly integrated into a generalized observer scheme. The goal is to achieve an adequate approximation of process outputs for each known class of system behavior. Symptoms are then evaluated by means of pattern classification. Application to a laboratory process is presented. A diagnosing subsystem is designed to detect incipient faults in the components of a three-tank system. It is implemented in real-time by using the SIMULINL/MATLAB programming environment. Experimental results regarding the diagnosis of single and multiple faults are included in a comparative study. It demonstrates the effectiveness of the suggested approach.

Data fusion has been a trend in the field of imaging and signal/image analysis. Although multisensor data fusion is still not regarded as a formal professional discipline, tremendous progress has been made since the publication of the first edition of this book in 1992. With this second edition, the authors have been successful in updating us with state-of-the-art methods and techniques in multisensor data fusion. The book involves both algorithms and software tools, and also covers contemporary subjects like smart agents, cognitive aides, and so on. This book is written according to the Joint Directors of Laboratories (JDL) data fusion group model. There are five levels in the JDL model. The first level deals with association, correlation, estimation, and identification in the data domain. The second and third levels perform knowledge-based processing and utilize expert systems. The fourth level is focused on process monitoring and optimization. The fifth level is devoted to human computer interaction. Chapter 1 serves as an overview. Then, Chapter 2 introduces the JDL model and associated algorithms. Chapters 3–6 cover processing techniques at level one. Chapter 7 gives methods at levels two and three. Chapter 8 targets the control of sensor and information resources at level four. Chapter 9 is for data fusion at level five. Chapter 10 discusses implementation of fusion systems. Chapters 11 and 12 describe emerging applications and information management. The book contains 100 equations, 75 illustrations and key references. The typesetting quality is generally excellent but it would be better if some labels in the figures have been put in larger size. Note that the additons in this book include materials on fusion system control, DARPA's TRIP model, and applications in data warehousing, medical equipment, and defense systems. Hall (associate dean for research, Pennsylvania State University School of Information Sciences and Technology) and McMullen (captain, US Air Force) are well known experts in the field, and should be congratulated for accomplishing such an excellent job in summarizing the up-to-date essential ideas and results on multisensor fusion. Overall, the book is very informative and not difficult to read for electrical and computer engineers as well as technical managers. These types of practitioners can gain solid advice from the book regarding selection of data fusion methods, balance of trade-offs among commercial off-the-shelf tools, development of multisensor data fusion systems and their applications to solve real-world problems. However, to build a sophisticated data fusion system one may need other references for more comprehensive mathematical theories, more rigorous statistical treatments, and more technical details. The cited literature in the book represents appropriate pointers for that purpose. A good example for further reading is the Handbook [1]. As an engineer in biomedical imaging, I would like to underline the relevance of this book to research on systems biomedicine. Given the completion of the Human Genome Project [2] and the momentum of the Physiome Project [3], in 2003 the NIH defined its Roadmap to guide our efforts towards most important biomedical research, including studies on biological building blocks, pathways, and networks using molecular libraries, imaging, bioinformatics and computational biology. Consequently, multi-modality signal sensing and imaging plays a critical role, which often necessarily generates heterogeous sparse/huge datasets that are multi-dimensional, multi-spectral, dynamic, noisy and/or incomplete. The data fusion techniques discussed in this book are closely related to that for biomedical image registration/fusion. Major challenges and new opportunities are enormous ahead of us. In that context, I hope this book will have a significant impact on the biomedical imaging areas, and recommend it to biomedical imaging researchers in particular and other interested engineers in general.