Fault Diagnosis of Hybrid Systems with Dynamic Bayesian Networks and Hybrid Possible Conficts

Hybrid systems are very important in our society, we can find them in many engineering fields. They can develop a task by themselves or they can interact with people so they have to work in a nominal and safe state. Model-based Diagnosis (MBD) is a diagnosis branch that bases its decisions in models. This dissertation is placed in the MBD framework with Artificial Intelligence techniques, which is known as DX community. The kind of hybrid systems we focus on have a continuous behaviour commanded by discrete events. There are several works already done in the diagnosis of hybrid systems field. Most of them need to pre-enumerate all the possible modes in the system even if they are never visited during the process. To solve that problem, some authors have presented the Hybrid Bond Graph (HBG) modeling technique, that is an extension of Bond Graphs. HBGs do not need to enumerate all the system modes, they are built as the system visits them at run time. Regarding the faults that can appear in a hybrid system, they can be divided in two main groups: (1) Discrete faults, and (2) parametric or continuous faults. The discrete faults are related to the hybrid nature of the systems while the parametric or continuous faults appear as faults in the system parameters or in the sensors. Both types af faults have not been considered in a unified diagnosis architecture for hybrid systems. The diagnosis process can be divided in three main stages: Fault Detection, Fault Isolation and Fault Identification. Computing the set of Possible Conflicts (PCs) is a compilation technique used in MBD of continuous systems. They provide a decomposition of a system in subsystems with minimal analytical redundancy that makes the isolation process more efficient. They can be used for fault detection and isolation tasks by means of the Fault Signature Matrix (FSM). The FSM is a matrix that relates the different parameters (fault candidates) in a system and the PCs where they are used

Fault Diagnosis for Polynomial Hybrid Systems

Safety requirements of technological processes trigger an increased demand for elaborate fault diagnosis tools. However, abrupt changes in system behavior are hard to formulate with continuous models but easier to represent in terms of hybrid systems. Therefore, we propose a set-based approach for complete fault diagnosis of hybrid polynomial systems formulated as a feasibility problem. We employ mixed-integer linear program relaxation of this formulation to exploit the presence of discrete variables. We improve the relaxation with additional constraints for the discrete variables. The efficiency of the method is illustrated with a simple two-tank example subject to multiple faults

FAULT DIAGNOSIS USING SYSTEM IDENTIFICATION FOR CHEMICAL PROCESS PLANT

Fault detection and diagnosis have gained an importance in the automation process industries over the past decade. This is due to several reasons; one of them being that sufficient amount of data is available from the process plants. The goal of this project is to develop such fault diagnosis systems, which use the input-output data of the realm process plant to detect, isolate, and reconstruct faults. The first part of this project focused on developing a different prediction models to the real system. Moreover, a linearized model using Taylor Series Expansion approach and ARX (Autoregressive with external input) model of the real system have been designed. In addition, the most accurate identification model which describes the dynamic behavior of the monitored system has been selected. Furthermore, a technique Statistical Process Control (SPC) used in fault diagnosis. This method depends on central limit theorem and used to detect faults by the analysis of the mismatch between the ARX model estimation and the process plant output. Finally the proposed methodology for fault diagnosis has been applied in numerical simulations to a non-isothermal CSTR (continuous stirred tank reactor) and the results and conclusion have been reported and showed excellent estimation of ARX model and good fault diagnosis performance of SPC

Real-Time Fault Detection and Diagnosis Using Intelligent Monitoring and Supervision Systems

In monitoring and supervision schemes, fault detection and diagnosis characterize high efficiency and quality production systems. To achieve such properties, these structures are based on techniques that allow detection and diagnosis of failures in real time. Detection signals faults and diagnostics provide the root cause and location. Fault detection is based on signal and process mathematical models, while fault diagnosis is focused on systems theory and process modeling. Monitoring and supervision complement each other in fault management, thus enabling normal and continuous operation. Its application avoids stopping productive processes by early detection of failures and by applying real-time actions to eliminate them, such as predictive and proactive maintenance based on process conditions. The integration of all these methodologies enables intelligent monitoring and supervision systems, enabling real-time fault detection and diagnosis. Their high performance is associated with statistical decision-making techniques, expert systems, artificial neural networks, fuzzy logic and computational procedures, making them efficient and fully autonomous in making decisions in the real-time operation of a production system

Blade fault diagnosis using artificial intelligence technique

Blade fault diagnosis is conventionally based on interpretation of vibration spectrum and wavelet map. These methods are however found to be difficult and subjective as it requires visual interpretation of chart and wavelet color map. To overcome this problem, important features for blade fault diagnosis in a multi row of rotor blade system was selected to develop a novel blade fault diagnosis method based on artificial intelligence techniques to reduce subjective interpretation. Three artificial neural network models were developed to detect blade fault, classify the type of blade fault, and locate the blade fault location. An experimental study was conducted to simulate different types of blade faults involving blade rubbing, loss of blade part, and twisted blade. Vibration signals for all blade fault conditions were measured with a sampling rate of 5 kHz under steady-state conditions at a constant rotating speed. Continuous wavelet transform was used to analyse the vibration signals and its results were used subsequently for feature extraction. Statistical features were extracted from the continuous wavelet coefficients of the rotor operating frequency and its corresponding blade passing frequencies. The extracted statistical features were grouped into three different feature sets. In addition, two new feature sets were proposed: blade statistical curve area and blade statistical summation. The effectiveness of the five different feature sets for blade fault detection, classification, and localisation was investigated. Classification results showed that the statistical features extracted from the operating frequency to be more effective for blade fault detection, classification, and localisation than the statistical features from blade passing frequencies. Feature sets of blade statistical curve area was found to be more effective for blade fault classification, while feature sets of blade statistical summation were more effective for blade fault localisation. The application of feature selection using genetic algorithm showed good accuracy performance with fewer features achieved. The neural network developed for blade fault detection, classification, and localisation achieved accuracy of 100%, 98.15% and 83.47% respectively. With the developed blade fault diagnosis methods, manual interpretation solely dependent on knowledge and the experience of individuals can be reduced. The novel methods can therefore be used as an alternative method for blade fault diagnosis

Model based fault diagnosis for hybrid systems : application on chemical processes

The complexity and the size of the industrial chemical processes induce the monitoring of a growing number of process variables. Their knowledge is generally based on the measurements of system variables and on the physico-chemical models of the process. Nevertheless, this information is imprecise because of process and measurement noise. So the research ways aim at developing new and more powerful techniques for the detection of process fault. In this work, we present a method for the fault detection based on the comparison between the real system and the reference model evolution generated by the extended Kalman filter. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. It is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of industrial systems. The use of this method is illustrated through a didactic example relating to the field of Chemical Process System Engineering

FAULT DIAGNOSIS USING SYSTEM IDENTIFICATION FOR CHEMICAL PROCESS PLANT

Fault detection and diagnosis have gained an importance in the automation process industries over the past decade. This is due to several reasons; one of them being that sufficient amount of data is available from the process plants. The goal of this project is to develop such fault diagnosis systems, which use the input-output data of the realm process plant to detect, isolate, and reconstruct faults. The first part of this project focused on developing a different prediction models to the real system. Moreover, a linearized model using Taylor Series Expansion approach and ARX (Autoregressive with external input) model of the real system have been designed. In addition, the most accurate identification model which describes the dynamic behavior of the monitored system has been selected. Furthermore, a technique Statistical Process Control (SPC) used in fault diagnosis. This method depends on central limit theorem and used to detect faults by the analysis of the mismatch between the ARX model estimation and the process plant output. Finally the proposed methodology for fault diagnosis has been applied in numerical simulations to a non-isothermal CSTR (continuous stirred tank reactor) and the results and conclusion have been reported and showed excellent estimation of ARX model and good fault diagnosis performance of SPC

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

Integration of a failure monitoring within a hybrid dynamic simulation environment

The complexity and the size of the industrial chemical processes induce the monitoring of a growing number of process variables. Their knowledge is generally based on the measurements of system variables and on the physico-chemical models of the process. Nevertheless this information is imprecise because of process and measurement noise. So the research ways aim at developing new and more powerful techniques for the detection of process fault. In this work, we present a method for the fault detection based on the comparison between the real system and the reference model evolution generated by the extended Kalman filter. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. It is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of industrial systems. The use of this method is illustrated through a didactic example relating to the field of Chemical Process System Engineering