Supervisory generalised predictive control and fault detection for multivariable anaesthesia

The development and evaluation of generalised predictive control (GPC) for online administration of muscle relaxant drugs in operating theatre is described. The necessary software was transferred into the operating theatre at two sites where successful trials were performed. Excellent regulation has been achieved, and the GPC algorithm displayed good robustness properties against disturbances such as surgical diathermy. The work was extended later to the multivariable case involving simultaneous control of muscle relaxation, and unconsciousness using blood pressure monitoring. While preliminary results are very encouraging, it is hoped to conduct more clinical trials using this system to establish its robustness especially over a wide spectrum of patients whose dynamics vary on a 1:4 ratio.

This particular study concerns the application of the generalised predictive control (GPC) algorithm using the concept of prespecified set-points in clinical anaesthesia. The study reveals that the SISO GPC version behaves very well under these circumstances and displays good robustness properties when mismatch conditions between the actual and assumed set-point profiles are considered. Moreover, when the scheme is extended to enhance a multivariable structure involving simultaneous control of muscle relaxation together with unconsciousness using blood pressure measurements, the levels of interaction from one channel to the other one can be considerably reduced without great compromise on other characteristics such as transient response and speed making therefore this whole concept of set-point prespecification an attractive candidate for future clinical trials.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Design of an active fault tolerant control system for a simulated industrial steam turbine

Beta Residuals: Improving Fault-Tolerant Control for Sensory Faults via Bayesian Inference and Precision Learning

Reliability evaluation of Fault Tolerant Control Systems with a semi-Markov FDI model

Fault detection, classification, and location estimation are paramount in transmission lines to ensure uninterrupted power supply, enhance reliability, and safety. Faster and more accurate fault localization is critical for prompt repair and efficient power restoration, thus minimizing economic losses. This paper presents a novel Fault Detection Isolation and Localization (FDIL) scheme, a machine learning (ML)-based framework, to increase the accuracy of fault location (FL) prediction. The proposed framework utilizes the phasor voltage, current, and frequency data obtained from optimally placed Phasor Measurement Units (PMUs) for fault detection, isolation, and localization. This paper also proposes a hybrid deep learning-based approach using Bidirectional Long Short-Term Memory (Bi-LSTM) networks and an attention mechanism for FL prediction. Extensive simulations using the IEEE 9-bus system, with different types and locations of faults, are performed on the OPAL-RT simulator-based testbed to generate and train the ML models. The performance of the proposed scheme is evaluated by calculating the Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) metric. The results demonstrate that the proposed approach achieves high accuracy in FL prediction, with an MAE of 0.0228 km, an MSE of 0.001, and an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.9894. Compared to the best-performing technique reported in the literature (CNN-LSTM, MAE = 0.211km), the proposed approach achieves an 89.2% reduction in localization error. The proposed scheme and model outperform the contemporary model, as reported in various literature, making it an effective solution for real-time fault monitoring in transmission systems.

Recently, a bond-graph-based fault detection and isolation (FDI) framework has been developed with a new concept of global analytical redundancy relations (GARRs) (Low, Wang, Arogeti, and Luo, 2009, 2010; Low, Wang, Arogeti, and Zhang, 2010). This new concept allows the fault diagnosis for hybrid systems which consist of both continuous dynamics and discrete modes. A failure of a safety critical system such as the steering system of an automated guided vehicle may cause severe damage. Such failure can be avoided by an early detection and estimation of faults. In this paper, the newly developed FDI method is studied in details using an electrohydraulic steering system of an electric vehicle. The steering system and faults are modeled as a hybrid dynamic system by the hybrid bond graph (HBG) modeling technique. GARRs are then derived systematically from the HBG model with a specific causality assignment. Fault detection, isolation, and estimation are applied, experimental setup is described, and results are discussed.

Fault detection isolation and diagnosis of multi-axle speed sensors for high-speed trains

The satellite ground station environment is one in which the availability of the equipment is normally paramount. Satellite contacts may be scheduled at any time and the data being down-linked may be irreplaceable if the equipment in the ground station is not available to receive it. The satellite may not be able to carry out its mission if necessary data is not up-linked when needed because of equipment outage. Availability is a function of mean time between failures (MTBF) and mean time to restore (MTTR). In many cases, the MTBF is addressed by installing equipment built with highly reliable parts, while MTTR is minimized by the inclusion of redundant equipment in the architecture. If a failure does occur, the redundant equipment can be brought into action only after the failure has been detected, isolated, and reported. In some cases, this is not straight forward. The interaction of multiple pieces of equipment may make a simple diagnosis impossible without further investigation. This paper presents an approach to fault detection and fault isolation (FDFI) which can increase the availability of the equipment by reducing the delay in the discovery and isolation of a fault. The approach can be implemented, in full or in part, based upon a cost benefit trade. If the data is not time critical, or if the procurement budget is constrained, a subset of the techniques and the features discussed may be implemented. Program unique analysis shows the best combination for each application.

Intelligent Magnetic Detection Microsystems (IMDM) are complex microsystems, which are used to obtain the information of motor flux and speed in single-driveway traffic system; the microsystems have to fulfill the task in a harsh environment of field, which would infect the reliability of system. An effective method to ensure maintenance cost and reliability is to integrate efficient built-in-test and monitoring function into IMDM, so testability analysis is introduced in the design of Microsystems. The Microsystems testing-points and diagnostic strategy are reasonably identified, which are beneficial to implement the rapid detection and fault isolation. This paper presents an approach to construct a fault message matrix based on the functional block diagram and signal flow chart of the microsystems. By analyzing the contribution of the testing-points to fault detection and fault isolation, the matrix is resolved step by step, the most necessary testing-points are identified and optimal diagnostic strategy is determined. Thus, the purpose of fault detection and fault isolation is achieved by the use of the fewest testing-points and short time.

ACTIVE ACTUATOR FAULT TOLERANT CONTROL DESIGN FOR POLYTOPIC LPV SYSTEMS

Using Ontological Classification to Design Fault Detection and Isolation Architecture

Fault simulation is the critical approach for many applications such as fault detection & diagnostics, test set quality measurement, generation of test vectors, circuit testability, and many others along with the help of fault injection technique. The fault simulation approach is divided into many types. The most straightforward approach among them is a serial fault simulation. In the simulation process, the circuit under test is faulted, and a faulty copy is achieved by either using a simulator command technique or instrumentation technique. A fault simulator must examine the behaviour of specified target fault in design and classified as detected or undetected by the applied test patterns. To modify the original code is a very challenging and time-consuming task. Therefore, the RASP-FIT tool is developed, which alters the fault-free FPGA design, which is under investigation, at the Verilog HDL code level. It produces the copies of faulty design along with the top design file for several fault simulation methods. Using this tool, a serial fault simulation environment can easily be created with no much effort. In this work, a serial fault simulation method is verified and validated using the RASP-FIT tool for an ISCAS’85 benchmark design as an example.

A VHDL based Fault simulation procedure for test bench and test hardware evaluation has been developed. This work is aimed to utilize features of VHDL for more efficient fault simulation. Information about fault detection can be obtained in this environment using fault simulation method and guidelines presented in this report. This environment consists of automated steps, which will lead to fault simulation. Information such as fault coverage, efficiency of test patterns and capability of test hardware to detect faults, can be extracted. Using this environment, one can evaluate test benches and order test vectors or configure BIST (Built-In Self Test) architectures.

Matchmaking and the Future of Hypertension Management.