Fault recovery in process control

Fault Recovery in process control requires effective fault detection, diagnosis and recovery schemes, and a fault-tolPi-ant system design. Fault detection and diagnosis involves creating a realistic model of the process, and using this model to analyse for fault conditions. The fault detection principles include feature extraction and pattern recognition, and analogue value limits and rate cf change limits. Fault recovery scheme? cover the realisation of redundancy ana back-up sub-systems, and state restoration techniques in the form of complete shutdowns, backward and forward recovery to a safe operating state. System design concepts include for the development of process control systems towards *hierarchical, level based distribution of functions. The level-based discussion is used as the basis for effective fault tolerant system design. Two case studies are included to show how fault recovery schemes were effected in a single process computer and in a distributed control system. Abstrac

On Integrating Error Detection into a Fault Diagnosis Algorithm for Massively Parallel Computers

Scalable fault diagnosis is necessary for constructing fault tolerance mechanisms in large massively parallel multiprocessor systems. The diagnosis algorithm must operate efficiently even if the system consists of several thousand processors. In this paper we introduce an event-driven, distributed system-level diagnosis algorithm. It uses a small number of messages and is based on a general diagnosis model without the limitation of the number of simultaneously existing faults (an important requirement for massively parallel computers). The algorithm integrates both error detection techniques like messages, and built in hardware mechanisms. The structure of the implemented algorithm is presented, and the essential program modules are described. The paper also discusses the use of test results generated by error detection mechanisms for fault localization. Measurement results illustrate the effect of the diagnosis algorithm, in particular the error detection mechanism by messages, on the application performance.Supported by the EU (European Unit) as part of the Esprit Project 6731, Fault Tolerance for Massively Parallel Systems, and the Hungarian-German Joint Scientific Research Project #70 with additional support from OTKA-F007414

Constraint Based System-Level Diagnosis of Multiprocessors

Massively parallel multiprocessors induce new requirements for system-level fault diagnosis, like handling a huge number of processing elements in an inhomogeneous system. Traditional diagnostic models (like PMC, BGM, etc.) are insufficient to fulfill all of these requirements. This paper presents a novel modelling technique, based on a special area of artificial intelligence (AI) methods: constraint satisfaction (CS). The constraint based approach is able to handle functional faults in a similar way to the Russel-Kime model. Moreover, it can use multiple-valued logic to deal with system components having multiple fault modes. The resolution of the produced models can be adjusted to fit the actual diagnostic goal. Consequently, constrint based methods are applicable to a much wider range of multiprocessor architectures than earlier models. The basic problem of system-level diagnosis, syndrome decoding, can be easily transformed into a constraint satisfaction problem (CSP). Thus, the diagnosis algorithm can be derived from the related constraint solving algorithm. Different abstraction leveles can be used for the various diagnosis resolutions, employing the same methodology. As examples, two algorithms are described in the paper; both of them is intended for the Parsytec GCel massively parallel system. The centralized method uses a more elaborate system model, and provides detailed diagnostic information, suitable for off-line evaluation. The distributed method makes fast decisions for reconfiguration control, using a simplified model. Keywords system-level self-diagnosis, massively parallel computing systems, constraint satisfaction, diagnostic models, centralized and distributed diagnostic algorithms

A Hybrid Approach to Fault Diagnosis in Teams of Autonomous Systems

Discrete event systems (DES) are dynamical systems equipped with a discrete state set and an event driven state transition structure. An event in a DES occurs instantaneously causing transition from one state to another. DES models have emerged to provide a formal treatment of many man-made systems such as automated manufacturing systems, computer systems, communication networks and air traffic control systems. In this thesis, we study fault diagnosis in teams of autonomous systems. In particular, one consider a team of two spacecraft in deep space. The spacecraft cooperate with each other in leader-follower formation flying. Formation flying demonstrates the capability of spacecraft to react to each other in order to maintain a desired relative distance autonomously without human intervention. In the system considered here, instruments (actuators and sensors) may fail and cause error. Because of the communication delays in deep space, each entity should be able to diagnose the failure and decide how to reconfigure itself. Basically, fault diagnosis in such systems requires information exchange between the autonomous elements of the team. The exchanged information for example may include position and velocity data. Our goal in the thesis is to propose a method for fault diagnosis with reduced information exchange. One solution is to transmit only discrete event information between autonomous systems. Transmission of discrete event data occurs less frequently than the transmission of continuous streams of data. The discrete event data may include high level supervisory commands issued every now and then and discretized values of continuous data that are transmitted only when a continuous-variable data (such as angle or acceleration) crosses the threshold. The fault diagnosis scheme proposed in this thesis is an adaptation of hybrid fault diagnosis for distributed autonomous systems. This system is simulated using MATLAB/SIMULINK Software and DECK Toolbox. We examined different maneuvers for spacecraft and investigated the effect of faults on the overall system and the performance of our designed fault diagnoser

Fault Diagnosis Algorithms for Wireless Sensor Networks

The sensor nodes in wireless sensor networks (WSNs) are deployed in unattended and hostile environments. The ill-disposed environment affects the monitoring infrastructure that includes the sensor nodes and the links. In addition, node failures and environmental hazards cause frequent topology change, communication failure, and network partition. This in turn adds a new dimension to the fragility of the WSN topology. Such perturbations are far more common in WSNs than those found in conventional wireless networks. These perturbations demand efficient techniques for discovering disruptive behavior in WSNs. Traditional fault diagnosis techniques devised for wired interconnected networks, and conventional wireless networks are not directly applicable to WSNs due to its specific requirements and limitations. System-level diagnosis is a technique to identify faults in distributed networks such as multiprocessor systems, wired interconnected networks, and conventional wireless networks. Recently, this has been applied on ad hoc networks and WSNs. This is performed by deduction, based on information in the form of results of tests applied to the sensor nodes. Neighbor coordination-based system-level diagnosis is a variation of this method, which exploits the spatio-temporal correlation between sensor measurements. In this thesis, we present a new approach to diagnose faulty sensor nodes in a WSN, which works in conjunction with the underlying clustering protocol and exploits spatio-temporal correlation between sensor measurements. An advantage of this method is that the diagnostic operation constitutes real work performed by the system, rather than a specialized diagnostic task. In this way, the normal operation of the network can be used for the diagnosis and resulting less time and message overhead. In this thesis, we have devised and evaluated fault diagnosis algorithms for WSNs considering persistence of the faults (transient, intermittent, and permanent), faults in communication channels and in one of the approaches, we attempt to solve the issue of node mobility in diagnosis. A cluster based distributed fault diagnosis (CDFD) algorithm is proposed where the diagnostic local view is obtained by exploiting the spatially correlated sensor measurements. We derived an optimal threshold for effective fault diagnosis in sparse networks. The message complexity of CDFD is O(n) and the number of bits exchanged to diagnose the network are O(n log2 n). The intermittent fault diagnosis is formulated as a multiobjective optimization problem based on the inter-test interval and number of test repetitions required to diagnose the intermittent faults. The two objectives such as detection latency and energy overhead are taken into consideration with a constraint of detection errors. A high level (> 95%) of detection accuracy is achieved while keeping the false alarm rate low (< 1%) for sparse networks. The proposed cluster based distributed intermittent fault diagnosis (CDIFD) algorithm is energy efficient because in CDIFD, diagnostic messages are sent as the output of the routine tasks of the WSNs. A count and threshold-based mechanism is used to discriminate the persistence of faults. The main characteristics of these faults are the amounts of time the fault disappears. We adopt this state-holding time to discriminate transient from intermittent or permanent faults. The proposed cluster based distributed fault diagnosis and discrimination (CDFDD) algorithm is energy efficient due to the improved network lifetime which is greater than 1150 data-gathering rounds with transient fault rates as high as 20%. A mobility aware hierarchal architecture is proposed which is to detect hard and soft faults in dynamic WSN topology assuming random movements of nodes in the WSN. A test pattern that ensures error checking of each functional block of a sensor node is employed to diagnose the network. The proposed mobility aware cluster based distributed fault diagnosis (MCDFD) algorithm assures a better packet delivery ratio (> 80%) in highly dynamic networks with a fault rate as high as 30%. The network lifetime is more than 900 data-gathering rounds in a highly dynamic network with a fault rate as high as 20%

Constraint Based Diagnosis Algorithms For Multiprocessors

Constraint-based diagnosis algorithms for multiprocessors A. Petri, P. Urban, J. Altmann, M. Dal Cin, E. Selenyi, K. Tilly, A. Pataricza In the latest years, new ideas appeared in system level diagnosis of multiprocessor systems. In contrary to the traditional diagnosis models (like PMC, BGM, etc.) which use strictly graph-oriented methods to determine the faulty components in a system, these new theories prefer AI-based algorithms, especially CSP methods. Syndrome decoding, the basic problem of self-diagnosis, can be easily transformed into constraints between the state of the tester and the tested components. Therefore, the diagnosis algorithm can be derived from a special constraint solving algorithm. The "benign" nature of the constraints (all their variables, representing the fault states of the components, have a very limited domain; the constraints are simple and similar to each other) reduces the algorithm's complexity so it can be converted to a powerful distributed diagnosis method with a minimal overhead. Experimental algorithms (using both centralized and distributed approach) were implemented for a Parsytec GC massively parallel multiprocessor system

A self-validating control system based approach to plant fault detection and diagnosis

An approach is proposed in which fault detection and diagnosis (FDD) tasks are distributed to separate FDD modules associated with each control system located throughout a plant. Intended specifically for those control systems that inherently eliminate steady state error, it is modular, steady state based, requires very little process specific information and therefore should be attractive to control systems implementers who seek economies of scale. The approach is applicable to virtually all types of process plant, whether they are open loop stable or not, have a type or class number of zero or not and so on. Based on qualitative reasoning, the approach is founded on the application of control systems theory to single and cascade control systems with integral action. This results in the derivation of cause-effect knowledge and fault isolation procedures that take into account factors like interactions between control systems, and the availability of non-control-loop-based sensors

A distributed networked approach for fault detection of large-scale systems

Networked systems present some key new challenges in the development of fault diagnosis architectures. This paper proposes a novel distributed networked fault detection methodology for large-scale interconnected systems. The proposed formulation incorporates a synchronization methodology with a filtering approach in order to reduce the effect of measurement noise and time delays on the fault detection performance. The proposed approach allows the monitoring of multi-rate systems, where asynchronous and delayed measurements are available. This is achieved through the development of a virtual sensor scheme with a model-based re-synchronization algorithm and a delay compensation strategy for distributed fault diagnostic units. The monitoring architecture exploits an adaptive approximator with learning capabilities for handling uncertainties in the interconnection dynamics. A consensus-based estimator with timevarying weights is introduced, for improving fault detectability in the case of variables shared among more than one subsystem. Furthermore, time-varying threshold functions are designed to prevent false-positive alarms. Analytical fault detectability sufficient conditions are derived and extensive simulation results are presented to illustrate the effectiveness of the distributed fault detection technique

Towards distributed diagnosis of the Tennessee Eastman process benchmark

A distributed hybrid strategy is outlined for the isolation of faults and disturbances in the Tennessee Eastman process, which would build on existing structures for distributed control systems, so should be easy to implement, be cheap and be widely applicable. The main emphasis in the paper is on one component of the strategy, a steady-state-based approach. Results obtained by applying this approach are presented and knowledge limitations are discussed. In particular a way in which a knowledge-base might evolve to improve isolation capabilities is suggested and the role of the operator is briefly discussed