JOINT DETECTION-STATE ESTIMATION AND SECURE SIGNAL PROCESSING

In this dissertation, joint detection-state estimation and secure signal processing are studied. Detection and state estimation are two important research topics in surveillance systems. The detection problems investigated in this dissertation include object detection and fault detection. The goal of object detection is to determine the presence or absence of an object under measurement uncertainty. The aim of fault detection is to determine whether or not the measurements are provided by faulty sensors. State estimation is to estimate the states of moving objects from measurements with random measurement noise or disturbance, which typically consist of their positions and velocities over time. Detection and state estimation are typically implemented separately and state estimation is usually performed after the decision is made. In this two-stage approach, missed detection and false alarms in detection stage decrease accuracy of state estimation. In this dissertation, several joint detection and state estimation algorithms are proposed. Secure signal processing is indispensable in dynamic systems especially when an adversary exists. In this dissertation, the developed joint fault detection and state estimation approach is used to detect attacks launched by an adversary on the system and improve state estimation accuracy. The security problem in satellite communication systems is studied and a minimax anti-jammer is designed in a frequency hopping spread spectrum (FHSS)/quadrature phase-shift keying (QPSK) satellite communication system

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Fault Diagnosis and Fault Tolerant Control of Wind Turbines: An Overview

Wind turbines are playing an increasingly important role in renewable power generation. Their complex and large-scale structure, however, and operation in remote locations with harsh environmental conditions and highly variable stochastic loads make fault occurrence inevitable. Early detection and location of faults are vital for maintaining a high degree of availability and reducing maintenance costs. Hence, the deployment of algorithms capable of continuously monitoring and diagnosing potential faults and mitigating their effects before they evolve into failures is crucial. Fault diagnosis and fault tolerant control designs have been the subject of intensive research in the past decades. Significant progress has been made and several methods and control algorithms have been proposed in the literature. This paper provides an overview of the most recent fault diagnosis and fault tolerant control techniques for wind turbines. Following a brief discussion of the typical faults, the most commonly used model-based, data-driven and signal-based approaches are discussed. Passive and active fault tolerant control approaches are also highlighted and relevant publications are discussed. Future development tendencies in fault diagnosis and fault tolerant control of wind turbines are also briefly stated. The paper is written in a tutorial manner to provide a comprehensive overview of this research topic

Malicious and Malfunctioning Node Detection via Observed Physical Layer Data

There are many mechanisms that can cause inadequate or unreliable information in sensor networks. A user of the network might be interested in detecting and classifying specific sensors nodes causing these problems. Several network layer based trust methods have been developed in previous research to assess these issues; in contrast this work develops a trust protocol based on observations of physical layer data collected by the sensors. Observations of physical layer data are used for decisions and calculations, and are based on just the measurements collected by the sensors. Although this information is packaged and distributed on the network layer, only the physical measurement is considered. This protocol is used to detect faulty nodes operating in the sensor network. The context of this research is Wireless Network Discovery (WND), which refers to modeling all layers of a non-cooperative wireless network. The focus in particular is the localization of transmitters, and detection of sensors affecting the localization. To accomplish this, a model for faulty sensors and two methods of detection are developed. Detection rates are analyzed with Receiver Operating Characteristic (ROC) curves, and the trade-off of detection versus localization error is discussed. Classification between faulty sensors is also considered to determine appropriate response to potential network attacks

Intelligent Data Fusion for Applied Decision Support

Data fusion technologies are widely applied to support a real-time decision-making in complicated, dynamically changing environments. Due to the complexity in the problem domain, artificial intelligent algorithms, such as Bayesian inference and particle swarm optimization, are employed to make the decision support system more adaptive and cognitive. This dissertation proposes a new data fusion model with an intelligent mechanism adding decision feedback to the system in real-time, and implements this intelligent data fusion model in two real-world applications. The first application is designing a new sensor management system for a real-world and highly dynamic air traffic control problem. The main objective of sensor management is to schedule discrete-time, two-way communications between sensors and transponder-equipped aircraft over a given coverage area. Decisions regarding allocation of sensor resources are made to improve the efficiency of sensors and communications, simultaneously. For the proposed design, its loop nature takes account the effect of the current sensor model into the next scheduling interval, which makes the sensor management system able to respond to the dynamically changing environment in real-time. Moreover, it uses a Bayesian network as the mission manager to come up with operating requirements for each region every scheduling interval, so that the system efficiently balances the allocation of sensor resources according to different region priorities. As one of this dissertation\u27s contribution in the area of Bayesian inference, the resulting Bayesian mission manager is shown to demonstrate significant performance improvement in resource usage for prioritized regions such as a runway in the air traffic control application for airport surfaces. Due to wind\u27s importance as a renewable energy resource, the second application is designing an intelligent data-driven approach to monitor the wind turbine performance in real-time by fusing multiple types of maintenance tests, and detect the turbine failures by tracking the turbine maintenance statistics. The current focus has been on building wind farms without much effort towards the optimization of wind farm management. Also, under performing or faulty turbines cause huge losses in revenue as the existing wind farms age. Automated monitoring for maintenance and optimizing of wind farm operations will be a key element in the transition of wind power from an alternative energy form to a primary form. Early detection and prediction of catastrophic failures helps prevent major maintenance costs from occurring as well. I develop multiple tests on several important turbine performance variables, such as generated power, rotor speed, pitch angle, and wind speed difference. Wind speed differences are particularly effective in the detection of anemometer failures, which is a very common maintenance issue that greatly impacts power production yet can produce misleading symptoms. To improve the detection accuracy of this wind speed difference test, I discuss a new method to determine the decision boundary between the normal and abnormal states using a particle swarm optimization (PSO) algorithm. All the test results are fused to reach a final conclusion, which describes the turbine working status at the current time. Then, Bayesian inference is applied to identify potential failures with a percentage certainty by monitoring the abnormal status changes. This approach is adaptable to each turbine automatically, and is advantageous in its data-driven nature to monitor a large wind farm. This approach\u27s results have verified the effectiveness of detecting turbine failures early, especially for anemometer failures

Increasing the robustness of autonomous systems to hardware degradation using machine learning

Autonomous systems perform predetermined tasks (missions) with minimum supervision. In most applications, the state of the world changes with time. Sensors are employed to measure part or whole of the world’s state. However, sensors often fail amidst operation; feeding as such decision-making with wrong information about the world. Moreover, hardware degradation may alter dynamic behaviour, and subsequently the capabilities, of an autonomous system; rendering the original mission infeasible. This thesis applies machine learning to yield powerful and robust tools that can facilitate autonomy in modern systems. Incremental kernel regression is used for dynamic modelling. Algorithms of this sort are easy to train and are highly adaptive. Adaptivity allows for model adjustments, whenever the environment of operation changes. Bayesian reasoning provides a rigorous framework for addressing uncertainty. Moreover, using Bayesian Networks, complex inference regarding hardware degradation can be answered. Specifically, adaptive modelling is combined with Bayesian reasoning to yield recursive estimation algorithms that are robust to sensor failures. Two solutions are presented by extending existing recursive estimation algorithms from the robotics literature. The algorithms are deployed on an underwater vehicle and the performance is assessed in real-world experiments. A comparison against standard filters is also provided. Next, the previous algorithms are extended to consider sensor and actuator failures jointly. An algorithm that can detect thruster failures in an Autonomous Underwater Vehicle has been developed. Moreover, the algorithm adapts the dynamic model online to compensate for the detected fault. The performance of this algorithm was also tested in a real-world application. One step further than hardware fault detection, prognostics predict how much longer can a particular hardware component operate normally. Ubiquitous sensors in modern systems render data-driven prognostics a viable solution. However, training is based on skewed datasets; datasets where the samples from the faulty region of operation are much fewer than the ones from the healthy region of operation. This thesis presents a prognostic algorithm that tackles the problem of imbalanced (skewed) datasets

Blind Sensor Calibration using Approximate Message Passing

The ubiquity of approximately sparse data has led a variety of com- munities to great interest in compressed sensing algorithms. Although these are very successful and well understood for linear measurements with additive noise, applying them on real data can be problematic if imperfect sensing devices introduce deviations from this ideal signal ac- quisition process, caused by sensor decalibration or failure. We propose a message passing algorithm called calibration approximate message passing (Cal-AMP) that can treat a variety of such sensor-induced imperfections. In addition to deriving the general form of the algorithm, we numerically investigate two particular settings. In the first, a fraction of the sensors is faulty, giving readings unrelated to the signal. In the second, sensors are decalibrated and each one introduces a different multiplicative gain to the measures. Cal-AMP shares the scalability of approximate message passing, allowing to treat big sized instances of these problems, and ex- perimentally exhibits a phase transition between domains of success and failure.Comment: 27 pages, 9 figure