INTELLIGENT FAULT DETECTION AND ISOLATION FOR PROTON EXCHANGE MEMBRANE FUEL CELL SYSTEMS

This work presents a new approach for detecting and isolating faults in nonlinear processes using independent neural network models. In this approach, an independent neural network is used to model the proton exchange membrane fuel cell nonlinear systems using a multi-input multi-output structure. This research proposed the use of radial basis function network and multilayer perceptron network to perform fault detection. After training, the neural network models can give accurate prediction of the system outputs, based on the system inputs. Using the residual generation concept developed in the model-based diagnosis, the difference between the actual and estimated outputs are used as residuals to detect faults. When the magnitude of these residuals exceed a predefined threshold, it is likely that the system is faulty. In order to isolate faults in the system, a second neural network is used to examine features in the residual. A specific feature would correspond to a specific fault. Based on features extracted and classification principles, the second neural network can isolate faults reliably and correctly. The developed method is applied to a benchmark simulation model of the proton exchange membrane fuel cell stacks developed at Michigan University. One component fault, one actuator fault and three sensor faults were simulated on the benchmark model. The simulation results show that the developed approach is able to detect and isolate the faults to a fault size of ±10% of nominal values. These results are promising and indicate the potential of the method to be applied to the real world of fuel cell stacks for dynamic monitoring and reliable operations

Deep Learning-Based, Passive Fault Tolerant Control Facilitated by a Taxonomy of Cyber-Attack Effects

In the interest of improving the resilience of cyber-physical control systems to better operate in the presence of various cyber-attacks and/or faults, this dissertation presents a novel controller design based on deep-learning networks. This research lays out a controller design that does not rely on fault or cyber-attack detection. Being passive, the controller’s routine operating process is to take in data from the various components of the physical system, holistically assess the state of the physical system using deep-learning networks and decide the subsequent round of commands from the controller. This use of deep-learning methods in passive fault tolerant control (FTC) is unique in the research literature. The proposed controller is applied to both linear and nonlinear systems. Additionally, the application and testing are accomplished with both actuators and sensors being affected by attacks and /or faults

Robust model-based fault diganosis [sic] for a DC zonal electrical distribution system

A key element of the U.S. Navy's transition to an electric naval force is an Integrated Power System (IPS) that provides continuity of service to vital systems despite combat damage. In order to meet subsequent survivability standards under a reduced manning constraint, the IPS system must include a fault tolerant control scheme, capable of achieving automated graceful degradation despite major disruptions involving cascading failures. Toward this objective, online modelbased residual generation techniques are proposed, which identify explicitly defined faults within a stochastic DC Zonal Electrical Distribution System (DC ZEDS). Two novel polynomial approaches to the design of unknown input observers (UIO) are developed to estimate the partial state and, under certain conditions, the unknown input. These methods are shown to apply to a larger class of systems compared to standard projection based approaches where the UIO rank condition is not satisfied. It is shown that the partial-state estimate is sufficient to the computation of residuals for fault diagnosis, even in such cases where full-state estimation is not possible. In order to reduce the complexity of the system, a modular approach to Fault Detection and Isolation (FDI) is presented. Here, the innovations generated from a bank of Kalman filters (some of them UIOs) act as a structured residual set for the stochastic DC ZEDS subsystem modules and are shown to detect and isolate various classes of faults. Certain mathematical models are also shown to effectively identify input/output consistency of systems in explicitly defined fault conditions. Numerical simulation results are based on the well-documented Office of Naval Research Control Challenge benchmark system, which represents a prototypical U.S. Navy shipboard IPS power distribution system.http://archive.org/details/robustmodelbased1094510226Approved for public release; distribution is unlimited

Gas Turbine Engine Prognostics Using Time-Series Based Approaches

In todays market, the increasing demand on utilizing gas turbine engines can be quite costly if users rely only on traditional time-based maintenance schedules. Meeting both the safety and the economical aspects of such systems could be realized by using an appropriate maintenance strategy in which the prediction of the engine health condition is employed to ensure that the system is maintained only if necessary. Towards this end, in this thesis the prognosis problem in the gas turbine engines is investigated. As in every rotational mechanical equipment, gas turbine rotating components also degrade during the engine operation which may deteriorate their performance. The engine degradation may originate from different sources such as aging, erosion, fouling, corrosion, etc. Hard particles mixed with the air can remove the materials from the flow path components (erosion) and cause aerodynamic changes in the blades, which can consequently reduce the affected components performance. Accumulated particles on the flow path components and annulus surfaces of the gas turbine (fouling) can also reduce the flow rate of the gas and consequently decrease the power and efficiency of the affected components. Among different degradation sources in the engine, erosion and fouling are considered as two well-known degradation phenomena and their effects on the engine system prognostics are studied in this thesis. Towards the above end, a controller is designed to control the thrust level of the engine and a Matlab/Simulink platform is employed to incorporate the effects of the above degradation factors and the engine dynamic model. The engine performance degradation trends are modeled by using three types of time-series based techniques namely, the autoregressive integrated moving average (ARIMA), the vector autoregressive (VAR) and the hybrid fuzzy autoregressive integrated moving average (hybrid fuzzy ARIMA) models. One of the challenges associated with time-series approaches is selecting a proper model which represents the structure of the time-series and is employed for prediction and prognosis purposes. Two widely used criteria namely, the Akaike’s information criterion (AIC) and the Bayesian information criterion (BIC) are used in order to select the best model. The challenges of coping with the uncertainties due to variety of sources such as measurement noise, insufficient data and changing operating conditions are inevitable factors. Taking the above facts into account, it may not be practical to obtain or be concerned with an exact prediction information. Therefore, we construct instead confidence bounds that provide a realistic boundary for the prediction and this is applied to all our proposed approaches in this thesis. The first method in this thesis deals with modeling a measurable parameter using its historical data which is a fine-tuned version of the ARMA model for non-stationary time series analysis. The second method, VAR model, models the measurable parameters by fusing historical data with the current and past data of some other engine measurable data in a vector form so that one can get benefit of more measurement parameters of the engine. The third method deals with fusing two measurable parameters using a Takagi-Sugeno fuzzy inference engine. In this thesis we are focused on modeling the engine performance degradations due to the fouling and the erosion which are the two main causes of gas turbine engine deterioration. In order to evaluate the performance of the proposed methods, they are applied to three different scenarios. These scenarios include the compressor fouling, turbine erosion phenomena and their combination with different severities. Our numerical simulation results show that the performance of the hybrid fuzzy ARIMA model is superior to that of the ARIMA and VAR methods

Autonomous Control of Space Reactor Systems

Autonomous and semi-autonomous control is a key element of space reactor design in order to meet the mission requirements of safety, reliability, survivability, and life expectancy. Interrestrial nuclear power plants, human operators are avilable to perform intelligent control functions that are necessary for both normal and abnormal operational conditions

Fault detection, isolation, and identification for nonlinear systems using a hybrid approach

This thesis presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems; taking advantage of both system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution are a bank of adaptive neural parameter estimators (NPE) and a set of single-parameterized fault models. The NPEs continuously estimate unknown fault parameters (FP) that are indicators of faults in the system. In view of the availability of full-state measurements, two NPE structures, namely series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. On the contrary, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Simple neural network architecture and update laws make both schemes suitable for real-time implementations. A fault tolerant observer (FTO) is then designed to extend the FDII schemes to systems with partial-state measurement. The proposed FTO is a neural state estimator that can estimate unmeasured states even in presence of faults. The estimated and the measured states then comprise the inputs to the FDII schemes. Simulation results for FDII of reaction wheels of a 3-axis stabilized satellite in presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solution under both full and partial-state measurements