Set Of Possible Faults Research Articles

Data-Driven Fault Diagnosis under Sparseness Assumption for LTI Systems

Model-based fault diagnosis for dynamical systems is a sophisticated task due to model inaccuracies, measurement noise and many possible fault scenarios. By presenting faults in terms of a dictionary, the latter obstacle is recently addressed using well-known techniques for recovering sparse information (e.g. lasso). However, current state-of-the-art methods still require accurate models and measurements for adequate diagnosis. In our contribution we address the problem of data-driven fault diagnosis in the sense that the model of the linear time-invariant (LTI) system is unknown in addition to the fault. Moreover, our aim is to diagnose (concurrent) faults while only having input/output data and the fault dictionary. This implies the user simply plugs in the data and specifies the set of possible faults in order to know the active faults together with an estimate of the dynamic model. The problem is formulated within a blind system identification context resulting in computationally efficient solutions based on convex optimization.

Robust fault estimation of a blade pitch and drivetrain system in wind turbine model

In this article, a novel robust fault estimation scheme to ensure efficient and reliable operation of wind turbines has been presented. Wind turbines are complex systems with large flexible structures that work under very turbulent and unpredictable environmental conditions for a variable electrical grid. The proposed observer-based estimation scheme consists of a set of possible faults affecting the dynamics, sensors, and actuators of wind turbines. First, the pitch and drivetrain system faults occur simultaneously with process and sensor disturbances that are called unknown input signals. Second, through a series of coordinate transformations, the faulty subsystem is decoupled from the rest of the system. The first subsystem is affected by unknown inputs, and the second one is affected by faults. A reduced-order unknown input observer is designed to reconstruct states accurately, whereas a reduced-order sliding mode observer is designed for the second subsystem such that it is robust against unknown inputs and faults. Moreover, the reduced-order unknown input observer guarantees the asymptotic stability of the error dynamics using the Lyapunov theory method and completely removes unknown inputs; on the other hand, the reduced-order sliding mode observer is designed to reconstruct faults for the faulty subsystem accurately. Until now, authors only focused on an unknown input signal in the dynamics of the system, especially in nonlinear systems. The estimated fault will be adequate to accommodate the control loop, and sufficient conditions are developed to guarantee the stability of the state estimation error. In the next step, to figure the effectiveness of the proposed approach, a wind turbine benchmark system model is considered with faults and unknown inputs scenarios. The simulation results are used to validate the robustness of the proposed algorithms under noise conditions, and the results show that the algorithm could classify faults robustly.

РАЗРАБОТКА СИСТЕМЫ ФУНКЦИОНАЛЬНОГО ДИАГНОСТИРОВАНИЯ ДВИЖИТЕЛЕЙ ПОДВОДНЫХ АППАРАТОВ

The aim of the study is to increase the efficiency of operation of underwater vehicles (UVs) by using systems of functional diagnosis of their thrusters, by providing detection, localization and identification of minor faults. To solve this problem, the article proposes a new method containing two main stages. At the first stage, a bank of diagnostic observers is built to detect and localize emerging faults. At the same time, each observer is constructed according to a special procedure in such a way to be sensitive to different set of possible faults. At the second stage, additional ob-servers working in the sliding mode are synthesized to accurately estimate the fault values. At the same time, in contrast to existing solutions, it is proposed to use a reduced (having a smaller di-mension) model of the original system when constructing these sliding mode observers. This makes it possible to reduce the complexity of the obtained observers in comparison with the known methods, where full-order observers are built. The results of the research showed the efficiency and high quality of all synthesized observers. In all the considered cases, it was possible to detect the occurrence of typical faults, as well as to ensure the identification of their values. Highly reliable UV control systems can be created on the basis of the considered method.

Fault estimation of wind turbines using combined adaptive and parameter estimation schemes

SummaryThis paper addresses the problem of fault estimation in wind turbines using a joint fault and state estimation scheme. The scheme assumes a set of possible faults affecting the dynamics of the wind turbine. Then, a combined adaptive and parameter estimation scheme is developed taking into consideration that process disturbances and sensor noises are unknown but bounded in an ellipsoid. Two subcases are considered depending on the satisfaction of a certain rank condition. The proposed approach is applied to a wind turbine based on a benchmark system model. The benchmark represents relevant fault scenarios, including sensor, actuator, and process faults. The proposed approach focuses on the last group of faults. Finally, the obtained results show that a satisfactory fault estimation is achieved against a set of predefined fault scenarios.

Combining Model-Based and Feature-Driven Diagnosis Approaches – A Case Study on Electromechanical Actuators

Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However, this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for vibration data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In this paper we present an approach that combines the analytic model-based and feature-driven diagnosis approaches. The analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults. We describe an implementation of this approach on the Flyable Electromechanical Actuator (FLEA) test bed.

Kalman filter-based multialternative method for fault detection and estimation

Proposed was a computationally efficient multialternative method for detection and estimation of faults additively involved in the right-hand sides of the linear equations of the state and measurement vectors. In distinction to the classical approach using a Kalman filter bank for each fault, the paper suggested an extended Kalman filter estimating a set of possible faults. The a posteriori probabilities and estimates of individual faults were shown to be readily calculable from the estimates and covariance matrices generated by the extended Kalman filter. Efficiency of the method is corroborated by modeling of a navigation complex with two inertial systems.

Fault detection in autonomous robots based on fault injection and learning

In this paper, we study a new approach to fault detection for autonomous robots. Our hypothesis is that hardware faults change the flow of sensory data and the actions performed by the control program. By detecting these changes, the presence of faults can be inferred. In order to test our hypothesis, we collect data from three different tasks performed by real robots. During a number of training runs, we record sensory data from the robots while they are operating normally and after a fault has been injected. We use back-propagation neural networks to synthesize fault detection components based on the data collected in the training runs. We evaluate the performance of the trained fault detectors in terms of number of false positives and time it takes to detect a fault. The results show that good fault detectors can be obtained. We extend the set of possible faults and go on to show that a single fault detector can be trained to detect several faults in both a robot's sensors and actuators. We show that fault detectors can be synthesized that are robust to variations in the task, and we show how a fault detector can be trained to allow one robot to detect faults that occur in another robot.

A BAYESIAN APPROACH TO FAULT ISOLATION - STRUCTURE ESTIMATION AND INFERENCE

This paper considers a Bayesian inference method for fault isolation. Given a set of residuals, and a set of possible faults, the task is to calculate the probability distribution of the faults. The method requires the conditional probability distribution of how the residuals respond given the possible faults. Especially important is to know the structure of this conditional probability distribution since it facilitates the use of efficient Baysian network techniques for the inference. The conditional probability distribution, and in particular its structure, is estimated from training data using a Bayesian approach. The approach is evaluated on a simple but illustrative example, where it is shown that the estimated structure and the distributions capture the dependencies that are important to make the correct isolation decisions.

Faults analysis for MSF plants

This work analyses the faults consequences in MSF plants. This analysis will help to design an optimal action plans in order to mitigate the faults effects. The nature of the faults consequences are studied at two levels: process level and mathematical level. At the first one, faults are grouped according its effects over the process state; at the second level, they are grouped according the mathematical features of its consequences analyzed as functions of time (i.e. signals). The study was carried out by using a dynamic simulator for MSF desalination plants. It takes into account the heaters and stages dynamic, hydraulic, standard instrumentation and control systems. This simulator was developed to study the effects of faults that may affect a MSF plant. In order to extend the results scope, the simulator allows the modification of topology and parameters into a wide range. Indeed, it is possible to change the number of stages belonging to the recovery and rejection sections, controllers’ parameters (set point, integral time and gain), valves size, pumps characteristics, sea water conditions, stages and heater dimensions, etc. Since faults simulation is the main simulator goal, the model and its resolution were carefully designed to enhance stability and speed. The user can select the fault to simulate among a set of possible faults (fault in controllers, sensors, pumps, etc.), and can specify the activating time (at which the fault starts), the developing time (time elapsed from the fault start up until the fault reaches its maximum magnitude) and the fault magnitude. Thus, it is possible to simulate step and ramp perturbations. The simulator was tested with data from real plants and it has shown a good performance.

Fault-tolerant control of the ship propulsion system benchmark

The application of a new fault-tolerant control methodology to the benchmark proposed in Zamanabadi and Blanke (Control Engineering Practice 7(2) (1999)) is considered. The benchmark regards the model of a propulsion system for a marine vehicle developed by the Control Engineering Department of Aalborg University. After a brief description of the system, a fault analysis is carried out leading to a set of possible faults and remedial actions. Then the fault detection and identification method, and the reconfiguration algorithm are described. Simulations on the model illustrate the performance of the fault tolerant system for some selected fault scenarios.

A dynamic simulator for MSF plants

This work presents a dynamic simulator for MSF desalination plants. It takes into account the heaters and stages dynamic, hydraulic, standard instrumentation and control systems. This simulator was developed to study the effects of faults that may affect a MSF system. In order to extend the results scope, the simulator allows the modification of MSF topology and parameters into a wide range. Indeed, it is possible to change the number of stages belonging to the recovery and rejection sections, controller parameters (set point, integral time and gain), valve size, pump characteristics, seawater conditions, stages and heater dimensions, etc. Since fault simulation is the main simulator goal, the model and its resolution were carefully designed to enhance stability and speed. The user can select the fault to simulate among a set of possible faults (fault in controllers, sensors, pumps, etc.), and can specify the activation time (at which the fault starts), the development time (time elapsed from the fault start up until the fault reaches its maximum magnitude) and the fault magnitude. Thus, it is possible simulate step and ramp perturbations. The simulator was tested with data from real plants and it has shown a good performance. To make the simulator operation easier, it was developed by using a visual language for Windows 95.

Application of Information System Theory for Actuators Diagnosis

For actuator assembly consisting of: control valve, pneumatic linear servomotor and positioner the set of possible faults was defined. Fault related algorithms are given. For description of the faults-symptoms relations the information system theory was applied. The fault identification algorithms basing on the information system rules are given.

Some theoretical aspects in simulation of multiple stage desalination systems

This work presents a dynamic simulator for MSF desalination plants. It takes into account the heaters and stages dynamic, hydraulic, standard instrumentation and control systems. This simulator was developed to study the effects of faults that may affect a MSF system. In order to extend the results scope, the simulator allows the modification of MSF topology and parameters into a wide range. Indeed, it is possible to change the number of stages belonging to the recovery and rejection sections, controller parameters (set point, integral time and gain), valve size, pump characteristics, seawater conditions, stages and heater dimensions, etc. Since fault simulation is the main simulator goal, the model and its resolution were carefully designed to enhance stability and speed. The user can select the fault to simulate among a set of possible faults (fault in controllers, sensors, pumps, etc.), and can specify the activation time (at which the fault starts), the development time (time elapsed from the fault start up until the fault reaches its maximum magnitude) and the fault magnitude. Thus, it is possible simulate step and ramp perturbations. The simulator was tested with data from real plants and it has shown a good performance. To make the simulator operation easier, it was developed by using a visual language for Windows 95.

Some Components of Fault-Finding

The aim of this study is to examine some of the components involved up to and including choice of the first test in a network fault-finding task. The components investigated were identifying the set of possible faults or consistent fault set (CFS), applying the half-split rule to a specified CFS, and making the first test without the CFS being specified. Factors of network size (54 units and 24 units) and complexity, as defined by four types of CFS configuration, were manipulated. Results indicated that errors occurring in identification of the CFS are mostly omission errors. Performance of this component is degraded by increasing both network size and complexity of the CFS. The same pattern of effects is observed in selecting the first test when the CFS is not specified. Ability to select a half-split test from a given CFS is affected only by complexity of the CFS configuration. Future studies need to differentiate further the psychological demands imposed during the initial stages of faultfinding.