Design Of Fault Detection System Research Articles

Adaptive process monitoring based on parity space methods

Motivated by data-driven design of fault detection system, a recursive algorithm is developed for updating the parity relation within the framework of subspace identification methods (SIM). The presented recursive algorithm benefits from the signal processing methods for noise subspace tracking to reduce the computationally burdensome updating of singular value decomposition (SVD), which is a crucial step in SIM. Based on the recursive updated parity relation, the residual generator for the fault detection purpose is constructed and the issues of residual evaluation and threshold computation are discussed further. The adaptive process monitoring scheme that integrates the aforementioned issues, is proposed and tested on the laboratory three-tank-system.

Decentralized Fault Detection System Design For Large-scale Interconnected System

In this paper, a decentralized fault detection (FD) system design approach is proposed for discrete-time large-scale interconnected systems. Such an FD system consists of two parts for each subsystem: a residual generator and a residual evaluator. The residual generator producing weighted output estimation errors is designed to match a proper reference residual model, such that it is robust against system disturbances and sensitive to system faults. The solution to a (sub)-optimal residual generator is given by solving a convex optimization problem with the help of linear matrix inequalities (LMIs). Then norm based evaluation functions are selected for each subsystem and the corresponding thresholds are presented for the residual evaluator design. The computation of thresholds are formulated also as an optimization problem, which can be solved by using LMIs. Finally, a numerical example is given to illustrate the results.

Detection and identification of ore grindability in a semiautogenous grinding circuit model using wavelet transform variances of measured variables

A reduced dimension dynamic model subject to random disturbances for a semiautogenous grinding (SAG) circuit is developed that is able to handle changes in the characteristics of the new feed ore. This dynamic model, which is adjusted using data from a large mineral processing plant, has been developed with the objective of being useful for design and testing of fault identification and detection (FDI) systems, soft-sensors, automatic control systems, etc. The reduced dimension is a requirement in order to be able to obtain in a reasonable time statistical results to evaluate such systems. In this paper this SAG circuit dynamic model is used to test a method for detecting changes and identifying the grindability of the ore being processed by the SAG circuit. The method used — based on the variance of the continuous wavelet transform of measured circuit variables — incorporates improvements of a previous method. Results show that a step change of new feed ore is detected in about 30 to 80 min depending on the grindability change. This result may be considered to be adequate when taking into account that the response time for the mill hold-up to attain a new equilibrium value after the ensuing transient is of about 3 h. Identification of grindability in stationary operation gives near 100% of correct classification under the analysed conditions. The sensitivity of the FDI method to changes in circuit characteristics is also assessed and acceptable results are obtained.

Fault diagnosis system for a robot manipulator through neuro fuzzy approach

The adoption of an efficient online Fault Detection and Isolation (FDI) tool is becoming utmost importance for robots, especially for those operating in remote or hazardous environments, where a high degree of safety and self-diagnostics capabilities are required. This saves time and cost in repairing the robot. A number of researchers have proposed Fault Diagnosis (FD) architectures for robotic manipulators using the model-based analytical and redundancy approach. One of the main issues in the design of fault detection system is to model the rigid link robotic manipulators with modelling uncertainties. In this paper, a new approach neuro fuzzy-based FDI for robot manipulators is discussed. A learning architecture, with neural network as online approximates the off-nominal system behaviour, which is used for monitoring the robotic system for the faults. This generates the residual by comparing the actual output from robot. Fuzzy inference system is applied to identify and isolate the faults which provide the adaptive threshold under the varying conditions. The concepts discussed were validated through simulation study using a Scorbot-ER 5 plus manipulator to illustrate the ability of the neuro fuzzy-based FD scheme to detect and isolate fault. Although a robotic manipulator is used to illustrate the effectiveness of this approach, it can also be applied to the non-linear systems.

Fault Detection System Design for Networked Control System with Stochastically Varying Transmission Delays

This paper studies the observer based fault detection problem in networked control systems. It is assumed that the transmission delays caused by the communication network are varying and their probability distribution is unknown but knowledge about its stochastic characteristics is known a priori . First the dynamic modelling of a proposed networked control system under different sampling frequencies using the packet-oriented data transmission method is derived. Based on this developed system model, an approach for fault detection system is presented. Main attention is paid to the residual evaluation task in order to reduce false alarm rate caused by the uncertainties due to the effect of the communication network. A study based on simulation platform to verify the results is given.

불확실한 비선형시스템을 위한 고장검출 시스템 설계

This paper deals with a fault detection system design for nonlinear systems with uncertain time varying parameters modelled as a T-S fuzzy system. A coprime factorization for T-S fuzzy systems is defined and a residual generator is designed using a left coprime factor. A fault detection criteria derived from the residual generator is also suggested. In order to demonstrate the efficacy of the suggested method, the fault defection method is applied to an inverted pendulum system and computer simulations are performed.

A hybrid fuzzy logic artificial neural network algorithm-based fault detection and isolation for industrial robot manipulators

The adoption of an efficient online Fault Detection and Isolation (FDI) tool is becoming of utmost importance for robots, especially for those operating in remote or hazardous environments, where a high degree of safety and self-diagnostics capabilities are required. This saves time and cost in repairing the robot. A number of researchers have proposed fault diagnosis architectures for robotic manipulators using the model-based analytical and redundancy approach. One of the main issues in the design of fault detection systems is how to model the rigid-link robotic manipulators with modelling uncertainties. In this paper, a new approach ? hybrid intelligence-based fault detection and isolation for robot manipulators ? is discussed. A learning architecture with neural network approximates the off-nominal system behaviour, which is used for monitoring the robotic system for the faults. This generates the residual by comparing the actual output from the robot. The fuzzy inference system is applied to identify and isolate the faults which provide the adoptive threshold under varying conditions. The concepts discussed are validated through a simulation study using a Scorbot-ER 5Plus manipulator to illustrate the ability of the neuro fuzzy-based fault-diagnosis scheme to detect and isolate faults.

AN ACTUATOR FAULT TOLERANT APPROACH FOR UNDERWATER REMOTELY OPERATED VEHICLES

This paper presents a preliminary set of results on the design of Actuator Fault Detection Systems (AFDSs) for an underwater remotely operated vehicle (ROV) to prevent actuator faults and hence to reduce the risk of safety hazards. The approach consists of two phases. First a model-based approach is applied to the nonlinear model of the ROV. This solution permits to discover the redundancy of the system, and thus using it for fault detection purposes. Successively, by means of a Sliding Mode Control based approach, the fault is isolated and the control law is reconfigurated using the remaining working actuators. In both the phases the plant is assumed to be affected by “moderate” parametric variations. Moreover, it is supposed to undergo unknown faults, which cause the plant input components to be stuck at some uncertain but bounded time functions.

Robust Design of Fault Detection and Isolation Systems

AbstractInspired by the rationale of Robust Design, a novel methodology is presented for the design of diagnostic systems for fault detection and isolation (FDI). Detection/isolation capability and robustness, i.e. sensitivity to faults and insensitivity to noise, are addressed in an integrated way within a fully stochastic framework. Although FDI is a classical problem in control engineering, this new approach improves the current state of the art both in terms of general applicability and optimality of the design solution. It demonstrates the potential of robust design in fostering innovation in a variety of technical sectors. For illustrative purposes, the methodology is applied to the design of a FDI system for a fluid mixer. Copyright © 2006 John Wiley & Sons, Ltd.

원전 증기발생기 수위제어계통의 고장검출 시스템 설계

본 논문에서는 원저 증기발생기 수위제어계통을 위한 고장검출 시스템을 설계한다. 이를 위하여 증기발생기 수위제어계통의 비선형 동적방정식을 미지의 매개변수를 갖는 T-S 퍼지시스템으로 모델링하고 좌 소인수 분해를 이용하여 오차발생기를 설계한다. 고장검출의 성능을 향상시키기 위하여 고장검출 필터의 설계법을 제안한다. 제시한 방법의 효용성을 컴퓨터 모의실험을 통하여 입증한다. This paper deals with a fault detection system design for nuclear steam generator water level control system. We expressed the nonlinear properties of the steam generator level system as a T-S fuzzy system with time varying uncertain parameters. We design a residual generator using a left coprime factorization of the T-S fuzzy model and a fault detection filter in order to improve the fault detection performance. We demonstrate the efficiency of the suggested design method via many computer simulations.

A Fault Detection System Design for Uncertain Fuzzy Systems

This paper deals with a fault detection system design for uncertain nonlinear systems modelled as T-S fuzzy systems with the integral quadratic constraints. In order to generate a residual signal, we used a left coprime factorization of the T-S fuzzy system. From the filtered signal of the residual generator, the fault occurence can be detected effectively. A simulation study with nuclear steam generator level control system shows that the suggested method can be applied to detect the fault in actual applications.

FAULT DETECTION OF NETWORKED CONTROL SYSTEMS WITH LIMITED COMMUNICATION

This paper considers the fault detection problem of networked control systems with limited data transmission rate. In order to reduce the network load and thus avoid the uncertainty caused by transmission delays and packet loss, a so-called periodic communication sequence is employed for the allocation of network resource. Instead of transmitting each control input and measured output signal at each sampling time, only a part of sensors and actuators have access to the network. An approach is presented based on the periodic system theory for the design of fault detection systems under this new communication pattern. Then, the influence of the new communication pattern on fault detection, including full decoupling and optimal achievable performance, is analyzed. Finally, the optimal selection of the periodic communication sequence is discussed.

MULTIOBJECTIVE DESIGN OF ROBUST FAULT DETECTION SYSTEMS

This paper considers the multiobjective integrated design of fault detection systems for linear time-invariant systems with unknown inputs. First, the residual generator is designed in such a way that the residual is decoupled from the unknown inputs that mainly influence the system. Then, the rest freedom is used to improve the robustness of the residual generator to other unknown inputs and its sensitivity to faults. By virtue of the close relationship between the parity space approach and the observer based approach, the order of the residual generator can be kept at a low and flexible level.

INTEGRATED DESIGN OF FAULT DETECTION SYSTEM WITH MULTI-OBJECTIVE OPTIMIZATION

In this paper, problems related to the integrated design of fault detection (FD) systems for linear dynamic systems with deterministic and stochastic disturbances are studied. Core of this study is to consider the design problems for the residual generator and residual evaluation under two well-developed model-based FD strategies, i.e. deterministic and stochastic strategies. Based on the properties of integrated residual evaluation, the design of residual generators is formulated as multi-objective optimization problems, and solved by the LMI technique. The proposed approach is illustrated with the aid of an example of vehicle lateral dynamic control system.

OPTIMAL IDENTIFICATION OF TAKAGI-SUGENO FUZZY MODELS FOR NONLINEAR FDI

The design of Fault Detection and Isolation (FDI) systems is generally based on models identified by minimizing adjustment error. For non-linear processes, the use of observers based on Takagi-Sugeno fuzzy models have been proposed. However, from a detection and isolation point of view, models obtained using known identification algorithms are not necessarily optimal. This is showed through evaluations made using a benchmark hydraulic system simulator, with a confusion matrix, mean detection time and mean time between false alarms as performance indicators. Based on these results, we propose a new design procedure employing multiobjective optimization that improves on the above-mentioned time indicators by a factor varying between 1.5 and 3.7.

Design and Evaluation of Hybrid Fault-Detection Systems

As chip densities and clock rates increase, processors are becoming more susceptible to transient faults that can affect program correctness. Up to now, system designers have primarily considered hardware-only and software-only fault-detection mechanisms to identify and mitigate the deleterious effects of transient faults. These two fault-detection systems, however, are extremes in the design space, representing sharp trade-offs between hardware cost, reliability, and performance. In this paper, we identify hybrid hardware/software fault-detection mechanisms as promising alternatives to hardware-only and software-only systems. These hybrid systems offer designers more options to fit their reliability needs within their hardware and performance budgets. We propose and evaluate CRAFT, a suite of three such hybrid techniques, to illustrate the potential of the hybrid approach. For fair, quantitative comparisons among hardware, software, and hybrid systems, we introduce a new metric, Mean Work To Failure, which is able to compare systems for which machine instructions do not represent a constant unit of work. Additionally, we present a new simulation framework which rapidly assesses reliability and does not depend on manual identification of failure modes. Our evaluation illustrates that CRAFT, and hybrid techniques in general, offer attractive options in the fault-detection design space.

Design of parametric fault detection systems: An H-infinity optimization approach

Problems related to the design of observer-based parametric fault detection (PFD) systems are studied. The core of our study is to first describe the faults occurring in system actuators, sensors and components in the form of additive parameter deviations,then to transform the PFD problems into a similar additive fault setup, based on which an optimal observer-based optimization fault detection approach is proposed. A constructive solution optimal in the sense of minimizing a certain performance index is developed. The main results consist of defining parametric fault detectability, formulating a PFD optimization problem and its solution. A numerical example to demonstrate the effectiveness of the proposed approach is provided.

Fault detection of linear discrete-time periodic systems

In this note, an approach to the design of optimal fault detection systems for linear discrete-time periodic systems is proposed, which leads to an optimized compromise between robustness to unknown disturbances and sensitivity to faults. The needed computation mainly consists in solving a difference periodic Riccati system. The proposed approach is finally illustrated by a numerical example.

MODEL-BASED SENSOR FAULT DETECTION SYSTEM FOR A SMART WHEELCHAIR

This paper presents a preliminary set of results on the design of a Sensor Fault Detection System (SFDS) for a smart wheelchair to prevent simples navigation sensor faults and hence to reduce the risk of safety hazards. The Structural Analysis technique is considered which uses graph theory to determine which redundancy exists in the sensory system and thus shows the possibility to detect and handle the navigation sensor faults. The considered navigation sensor equipment includes odometric, gyroscopic and laser range measures.

Transient model for induction machines with stator winding turn faults

A transient model for an induction machine with stator winding turn faults is derived using reference frame transformation theory. A state-space representation of the dynamic equations, suitable for digital simulation, is provided. Steady state equivalent circuits are derived, from which the sequence components of the line currents can be estimated. Experimental results are provided to validate the derived model. This model would find use in the design of turn fault detection systems for induction machines.