A control-theoretical fault prognostics and accommodation framework for a class of nonlinear discrete-time systems

Fault diagnostics and prognostics schemes (FDP) are necessary for complex industrial systems to prevent unscheduled downtime resulting from component failures. Existing schemes in continuous-time are useful for diagnosing complex industrial systems and no work has been done for prognostics. Therefore, in this dissertation, a systematic design methodology for model-based fault prognostics and accommodation is undertaken for a class of nonlinear discrete-time systems. This design methodology, which does not require any failure data, is introduced in six papers. In Paper I, a fault detection and prediction (FDP) scheme is developed for a class of nonlinear system with state faults by assuming that all the states are measurable. A novel estimator is utilized for detecting a fault. Upon detection, an online approximator in discrete-time (OLAD) and a robust adaptive term are activated online in the estimator wherein the OLAD learns the unknown fault dynamics while the robust adaptive term ensures asymptotic performance guarantee. A novel update law is proposed for tuning the OLAD parameters. Additionally, by using the parameter update law, time to reach an a priori selected failure threshold is derived for prognostics. Subsequently, the FDP scheme is used to estimate the states and detect faults in nonlinear input-output systems in Paper II and to nonlinear discrete-time systems with both state and sensor faults in Paper III. Upon detection, a novel fault isolation estimator is used to identify the faults in Paper IV. It was shown that certain faults can be accommodated via controller reconfiguration in Paper V. Finally, the performance of the FDP framework is demonstrated via Lyapunov stability analysis and experimentally on the Caterpillar hydraulics test-bed in Paper VI by using an artificial immune system as an OLAD --Abstract, page iv

Fault detection and prediction with application to rotating machinery

In this thesis, the detection and prediction of faults in rotating machinery is undertaken and presented in two papers. In the first paper, Principal Component Analysis (PCA), a well known data-driven dimension reduction technique, is applied to data for normal operation and four fault conditions from a one-half horsepower centrifugal water pump. Fault isolation in this scheme is done by observing the location of the data points in the Principal Component domain, and the time to failure (TTF) is calculated by applying statistical regression on the resulting PC scores. The application of the proposed scheme demonstrated that PCA was able to detect and isolate all four faults. Additionally, the TTF calculation for the impeller failure was found to yield satisfactory results. On the other hand, in the second paper, the fault detection and failure prediction are done by using a model based approach which utilizes a nonlinear observer consisting of an online approximator in discrete-time (OLAD) and a robust adaptive term. Once a fault has been detected, both the OLAD and the robust adaptive term are initiated and the OLAD then utilizes its update law to learn the unknown dynamics of the encountered fault. While in similar applications it is common to use neural networks to be used for the OLAD, in this paper an Artificial Immune System (AIS) is used for the OLAD. The proposed approach was verified through implementation on data from an axial piston pump. The scheme was able to satisfactorily detect and learn both an incipient piston wear fault and an abrupt sensor failure --Abstract, page iv

Monitoring of power system dynamics using a hybrid state estimator

Modern power systems are undergoing a transformation process where distributed energy re-sources together with complex load technologies are increasingly integrated. This, in addition to a sustained growth in electricity consumption and a lack of significant investment in trans-mission infrastructure, leads power systems to face with new stochastic operating behavior and dynamics and to operate under stressed conditions. Under such operating conditions, the occurrence of a potential disturbance may cause a partial or a total collapse. Therefore, in order to minimize the risk of collapses and their impact, new monitoring tools must be adopted, capable of providing the right conditions for dynamic wide-area monitoring. The thesis presents a hybrid state estimator, that is a monitoring tool that combines fast synchronized phasor measurements with traditional measurements into a single scheme. It has the ability to estimate at high speed power system dynamics associated to slow and fast transient phenomena considering a reduced amount of phasor measurement units (PMUs). The developed scheme consists of two phases depending on the power system operating regime. In phase one the system is in stationary regime and bus voltages (magnitude and angle) together with related variables like power flows, current through lines, etc. are estimated by a static estimator at a low speed, which is determined by the supervisory control and data acquisition (SCADA) system. When a physical disturbance happens and the system is in transient regime phase two comes into operation. This time, two estimators work in sequence at high speed. First, a static state estimator is used to estimate bus voltages as soon as the synchronized phasor measurement set arrives. Then, a dynamic estimator is in charge of estimating dynamic states of all generators and motors in the system, even if the unit is not observed by a PMU. Full observability is re-stored through a novel data-mining based methodology, which defines, first, a PMU topology that allows monitoring the post-contingency bus voltage dynamics of the entire power system and, second, generates a number of bus voltage pseudo-measurements to extend the observability to the whole system

Reaction Wheels Fault Isolation Onboard 3-Axis Controlled Satellite using Enhanced Random Forest with Multidomain Features

With the increasing number of satellite launches throughout the years, it is only natural that an interest in the safety and monitoring of these systems would increase as well. However, as a system becomes more complex it becomes difficult to generate a high-fidelity model that accurately describes all the system components. With such constraints using data-driven approaches becomes a more feasible option. One of the most commonly used actuators in spacecraft is known as the reaction wheel. If these reaction wheels are not maintained or monitored, it could result in mission failure and unwarranted costs. That is why fault detection and isolation, which is detecting anomalies in real-time and finding the root cause of the failure, is crucial. This work proposes a novel approach for a data-driven machine learning technique for detecting and isolating multiple in-phase faults in nonlinear systems, with a case-study for an in-orbit closed loop-controlled satellite with reaction wheels as actuators. The proposed method uses a hierarchical approach with automated feature ex-traction, feature reduction and model selection. The method is also studied on three different datasets and three configurations. The results yield a performance accuracy of 98.91%, 97.87%, and 98.02% for all three configurations, respectively. Further-more, sensitivity analysis which includes missing values, missing sensors, and noise, are applied against the proposed method to test its robustness

Communication-protocol-based analysis and synthesis of networked systems: progress, prospects and challenges

In recent years, the communication-protocol-based synthesis and analysis issues have gained substantial research interest owing mainly to their significance in networked systems. In this work, we survey the control and filtering problems of networked systems under the effects induced by communication protocols. First, we introduce the engineering background of networked systems as well as the theoretical frameworks established to deal with the communication-protocol-based analysis and synthesis problems. Then, recent advances (especially the latest results) are reviewed on the stability analysis issue subject to protocol scheduling. Subsequently, the particular effort is devoted to presenting the latest progress on various communication-protocol-based control and filtering problems according to the characteristics of networked systems (e.g. time-varying nature, random behaviours, types of parameter uncertainties, and kinds of distributed structure). After that, we provide a systematic review of the communication-protocol-based fault diagnosis problems. Finally, some research challenges of communication-protocol-based control and filtering problems are outlined for future research

Measurements, Models, Systems and Design

531 s.

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions