Qualitative Fault Isolation of Hybrid Systems: A Structural Model Decomposition-Based Approach

Quick and robust fault diagnosis is critical to ensuring safe operation of complex engineering systems. A large number of techniques are available to provide fault diagnosis in systems with continuous dynamics. However, many systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete behavioral modes, each with its own continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task computationally more complex due to the large number of possible system modes and the existence of autonomous mode transitions. This paper presents a qualitative fault isolation framework for hybrid systems based on structural model decomposition. The fault isolation is performed by analyzing the qualitative information of the residual deviations. However, in hybrid systems this process becomes complex due to possible existence of observation delays, which can cause observed deviations to be inconsistent with the expected deviations for the current mode in the system. The great advantage of structural model decomposition is that (i) it allows to design residuals that respond to only a subset of the faults, and (ii) every time a mode change occurs, only a subset of the residuals will need to be reconfigured, thus reducing the complexity of the reasoning process for isolation purposes. To demonstrate and test the validity of our approach, we use an electric circuit simulation as the case study

Decentralized Fault Diagnosis and Prognosis Scheme for Interconnected Nonlinear Discrete-Time Systems

This paper deals with the design of a decentralized fault diagnosis and prognosis scheme for interconnected nonlinear discrete-time systems which are modelled as the interconnection of several subsystems. For each subsystem, a local fault detector (LFD) is designed based on the dynamic model of the local subsystem and the local states. Each LFD consists of an observer with an online neural network (NN)-based approximator. The online NN approximators only use local measurements as their inputs, and are always turned on and continuously learn the interconnection as well as possible fault function. A fault is detected by comparing the output of each online NN approximator with a predefined threshold instead of using the residual. Derivation of robust detection thresholds and fault detectability conditions are also included. Due to interconnected nature of the overall system, the effect of faults propagate to other subsystems, thus a fault might be detected in more than one subsystem. Upon detection, faults local to the subsystem and from other subsystems are isolated by using a central fault isolation unit which receives detection time information from all LFDs. The proposed scheme also provides the time-to-failure or remaining useful life information by using local measurements. Simulation results provide the effectiveness of the proposed decentralized fault detection scheme

Diagnosability-Based Sensor Placement through Structural Model Decomposition

Systems health management, and in particular fault diagnosis, is important for ensuring safe, correct, and efficient operation of complex engineering systems. The performance of an online health monitoring system depends critically on the available sensors of the system. However, the set of selected sensors is subject to many constraints, such as cost and weight, and hence, these sensors must be selected judiciously. This paper presents an offline design-time sensor placement approach for complex systems. Our diagnosis method is built upon the analysis of model-based residuals, which are computed using structural model decomposition. Sensor placement in this framework manifests as a residual selection problem, and we aim to find the set of residuals that achieves single-fault diagnosability of the system, uses the minimum number of sensors, and corresponds to the best model decomposition for the best distribution of the diagnosis system. We present a set of algorithms for solving this problem and compare their performance in terms of computational complexity and optimality of solutions. We demonstrate the approach using a benchmark multi-tank system

Health Monitoring in Small Satellite Design

Presentation/lecture on systems health monitoring (diagnostics, prognostics, decision-making) with applications to the design phase of small satellite components and systems

Machine diagnostics based on models

Hlavní myšlenka je zaměřena na diagnostiku konkrétního hydraulického systému, tj. snímání fyzikálních veličin hydraulického okruhu s nádrží a odstředivým čerpadlem poháněného asynchronním motorem. Jedná se o soustavu potrubí napojených na čerpadlo, kde vlivem jeho práce vytváří průtok vody a nárůst tlaku. V praxi je tato problematika řešena také v energetickém a atomovém průmyslu. Primární okruhy v některých případech nelze navrhnout nebo upravit tak, aby se dala hodnota tlaku snímat lokálně. Proto je zapotřebí měřit tuto veličinu nepřímo – a to z proudů motoru. Hlavní myšlenkou práce je diagnostikovat systém nepřímou metodou – konkrétně detekovat stav hydraulického okruhu (tlak, průtok) z hodnot, které jsme schopni měřit a odhalit poškození v předstihu. V druhé polovině závěrečné práce je aplikace částí konkrétního hydraulického systému do simulačního prostředí MATLAB Simulink. Model hydraulického obvodu obsahuje matematicko-fyzikální vztahy, které simulují průběh zmíněného experimentu. Výsledky simulace jsou porovnávány s výsledky experimentu. Model také vyšetřuje simulaci poruchového stavu, kdy přivádíme do hydraulického obvodu tlakové pulzace. Právě tyto změny v hydraulické části se projevují na charakteristikách čerpadla a asynchronního motoru, tím pádem jsme schopni diagnostikovat tento systém.The main idea is focused on the diagnostics of a specific hydraulic system, i.e. sensing the physical quantities of the hydraulic circuit with a tank and a centrifugal pump driven by an asynchronous motor. It is a system of pipes connected to the pump, where due to its work it creates a water flow and a pressure increase. In practice, this issue is also addressed in the energy and nuclear industries. Primary circuits in some cases cannot be designed or modified to be able measure locally the pressure value. It is necessary to measure this quantity indirectly - from the motor currents. The main idea of the work is to diagnose the system by an indirect method - specifically to detect the state of the hydraulic circuit (pressure, flow) from the values that we are able to measure and detect damage in advance. In the second part of the thesis is the application of the parts of a specific hydraulic system in the simulation environment MATLAB Simulink. The model of the hydraulic circuit contains mathematical-physical relations that simulate the course of the mentioned experiment. The results of the simulation are compared with the results of the experiment. The model also investigates the simulation of a fault condition, when we supply pressure pulsations to the hydraulic circuit. It is these changes in the hydraulic part that affect the characteristics of the pump and the asynchronous motor, so we are able to diagnose this system.