Advanced Mathematics and Computational Applications in Control Systems Engineering

Control system engineering is a multidisciplinary discipline that applies automatic control theory to design systems with desired behaviors in control environments. Automatic control theory has played a vital role in the advancement of engineering and science. It has become an essential and integral part of modern industrial and manufacturing processes. Today, the requirements for control precision have increased, and real systems have become more complex. In control engineering and all other engineering disciplines, the impact of advanced mathematical and computational methods is rapidly increasing. Advanced mathematical methods are needed because real-world control systems need to comply with several conditions related to product quality and safety constraints that have to be taken into account in the problem formulation. Conversely, the increment in mathematical complexity has an impact on the computational aspects related to numerical simulation and practical implementation of the algorithms, where a balance must also be maintained between implementation costs and the performance of the control system. This book is a comprehensive set of articles reflecting recent advances in developing and applying advanced mathematics and computational applications in control system engineering

Learning for predictions: Real-time reliability assessment of aerospace systems

Prognostics and Health Management (PHM) aim to predict the Remaining Useful Life (RUL) of a system and to allow a timely planning of replacement of components, limiting the need for corrective maintenance and the down time of equipment. A major challenge in system prognostics is the availability of accurate physics based representations of the grow rate of faults. Additionally, the analysis of data acquired during flight operations is traditionally time consuming and expensive. This work proposes a computational method to overcome these limitations through the dynamic adaptation of the state-space model of fault propagation to on-board observations of system’s health. Our approach aims at enabling real-time assessment of systems health and reliability through fast predictions of the Remaining Useful Life that account for uncertainty. The strategy combines physics-based knowledge of the system damage propagation rate, machine learning and real-time measurements of the health status to obtain an accurate estimate of the RUL of aerospace systems. The RUL prediction algorithm relies on a dynamical estimator filter, which allows to deal with nonlinear systems affected by uncertainties with unknown distribution. The proposed method integrates a dynamical model of the fault propagation, accounting for the current and past measured health conditions, the past time history of the operating conditions (such as input command, load, temperature, etc.), and the expected future operating conditions. The model leverages the knowledge collected through the record of past fault measurements, and dynamically adapts the prediction of the damage propagation by learning from the observed time history. The original method is demonstrated for the RUL prediction of an electromechanical actuator for aircraft flight controls. We observe that the strategy allows to refine rapid predictions of the RUL in fractions of seconds by progressively learning from on-board acquisitions

Design and modelling of permanent magnet machine's windings for fault-tolerant applications

The research described in this thesis focuses on the mitigation of inter-turn short-circuit (SC) faults in Fault tolerant Permanent Magnet (FT-PM) machines. An analytical model is proposed to evaluate the inter-turn SC fault current accounting for the location in the slot of the short-circuited turn(s). As a mitigation strategy to SC faults at the design stage, a winding arrangement called VSW (Vertically placed Strip Winding) is proposed and analysed. The proposed analytical model is benchmarked against finite element (FE) calculation and validated experimentally. The results demonstrate that the proposed winding arrangement in the slot improves the fault tolerance (FT) capability of the machine by limiting the inter-turn SC fault current regardless the fault location in the slot. Electromagnetic and thermal studies are conducted to verify the merits and drawbacks of the proposed winding compared to the conventional winding using round conductors (RCW). The study shows that the proposed winding scheme, in addition to being fault-tolerant, has an improved bulk radial conductivity, can achieve a good fill factor, but has a significantly higher frequency-dependent AC copper loss. To predict the AC losses an analytical model based on an exact analytical 2D field solution is proposed. This model consists of first solving the two-dimensional magneto-static problem based on Laplace’s and Poisson’s equations using the separation of variables technique. Then, based on that solved solution, by defining the tangential magnetic field (Ht) at the slot opening radius, Helmholtz’ equation is solved in the slot sub-domain. Subsequently, an FE and MATLAB® coupled parametric design is undertaken to maximise the VSW wound machine’s efficiency whilst maintaining its FT capability. The proposed analytical models for prediction of the SC fault current and AC copper losses are integrated into the coupled optimisation. It is shown that the effective losses of the VSW can be minimised through the parametric design while maintaining the required level of machine performance. Using an existing FT-PM machine of which the rotor is kept unchanged two stators were designed, manufactured and wound with RCW and VSW respectively and experimental tests are carried out to validate the analytical models and the new winding concept

Design and modelling of permanent magnet machine's windings for fault-tolerant applications

The research described in this thesis focuses on the mitigation of inter-turn short-circuit (SC) faults in Fault tolerant Permanent Magnet (FT-PM) machines. An analytical model is proposed to evaluate the inter-turn SC fault current accounting for the location in the slot of the short-circuited turn(s). As a mitigation strategy to SC faults at the design stage, a winding arrangement called VSW (Vertically placed Strip Winding) is proposed and analysed. The proposed analytical model is benchmarked against finite element (FE) calculation and validated experimentally. The results demonstrate that the proposed winding arrangement in the slot improves the fault tolerance (FT) capability of the machine by limiting the inter-turn SC fault current regardless the fault location in the slot. Electromagnetic and thermal studies are conducted to verify the merits and drawbacks of the proposed winding compared to the conventional winding using round conductors (RCW). The study shows that the proposed winding scheme, in addition to being fault-tolerant, has an improved bulk radial conductivity, can achieve a good fill factor, but has a significantly higher frequency-dependent AC copper loss. To predict the AC losses an analytical model based on an exact analytical 2D field solution is proposed. This model consists of first solving the two-dimensional magneto-static problem based on Laplace’s and Poisson’s equations using the separation of variables technique. Then, based on that solved solution, by defining the tangential magnetic field (Ht) at the slot opening radius, Helmholtz’ equation is solved in the slot sub-domain. Subsequently, an FE and MATLAB® coupled parametric design is undertaken to maximise the VSW wound machine’s efficiency whilst maintaining its FT capability. The proposed analytical models for prediction of the SC fault current and AC copper losses are integrated into the coupled optimisation. It is shown that the effective losses of the VSW can be minimised through the parametric design while maintaining the required level of machine performance. Using an existing FT-PM machine of which the rotor is kept unchanged two stators were designed, manufactured and wound with RCW and VSW respectively and experimental tests are carried out to validate the analytical models and the new winding concept

An Uncertainty Quantification Framework for Autonomous System Tracking and Health Monitoring

This work proposes a perspective towards establishing a framework for uncertainty quantification of autonomous system tracking and health monitoring. The approach leverages the use of a predictive process structure, which maps uncertainty sources and their interaction according to the quantity of interest and the goal of the predictive estimation. It is systematic and uses basic elements that are system agnostic, and therefore needs to be tailored according to the specificity of the application. This work is motivated by the interest in low-altitude unmanned aerial vehicle operations, where awareness of vehicle and airspace state becomes more relevant as the density of autonomous operations grows rapidly. Predicted scenarios in the area of small vehicle operations and urban air mobility have no precedent, and holistic frameworks to perform prognostics and health management (PHM) at the system- and airspace-level are missing formal approaches to account for uncertainty. At the end of the paper, two case studies demonstrate implementation framework of trajectory tracking and health diagnosis for a small unmanned aerial vehicle

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations

As aviation adopts new and increasingly complex operational paradigms, vehicle types, and technologies to broaden airspace capability and efficiency, maintaining a safe system will require recognition and timely mitigation of new safety issues as they emerge and before significant consequences occur. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. In-time safety assurance comprises monitoring, assessment, and mitigation functions that proactively reduce risk in complex operational environments where the interplay of hazards may not be known (and therefore not accounted for) during design. These functions can also help to understand and predict emergent effects caused by the increased use of automation or autonomous functions that may exhibit unexpected non-deterministic behaviors. The envisioned monitoring and assessment functions can look for precursors, anomalies, and trends (PATs) by applying model-based and data-driven methods. Outputs would then drive downstream mitigation(s) if needed to reduce risk. These mitigations may be accomplished using traditional design revision processes or via operational (and sometimes automated) mechanisms. The latter refers to the in-time aspect of the system concept. This report comprises architecture and information requirements and considerations toward enabling such a capability within the domain of low altitude highly autonomous urban flight operations. This domain may span, for example, public-use surveillance missions flown by small unmanned aircraft (e.g., infrastructure inspection, facility management, emergency response, law enforcement, and/or security) to transportation missions flown by larger aircraft that may carry passengers or deliver products. Caveat: Any stated requirements in this report should be considered initial requirements that are intended to drive research and development (R&D). These initial requirements are likely to evolve based on R&D findings, refinement of operational concepts, industry advances, and new industry or regulatory policies or standards related to safety assurance

Approach to health monitoring and assessment of rolling bearing

A bearing is the most common and vital element in the majority of rotating machinery. Condition monitoring and performance assessment of rolling bearing have recently attracted significant attention. This paper proposes a set of methodologies to realize the efficient health monitoring and assessment of rolling bearing. Considering the difficulties and disadvantages in detecting the fault signal of rolling bearing with background noise, this paper presents a method based on the Duffing oscillator and Hu’s moment invariant for health monitoring. The proposed method mainly combines the chaotic oscillator and moment invariant, fully utilizing the sensitivity of the former to detect the fault signal and taking the latter as a quantitative index for fault identification without the need for a qualitative artificial judgment on the Duffing oscillator phase trajectory map. To provide the optimal performance of Hu’s moment invariant in automatic recognition for the phase trajectory map, the influencing principle of different oscillator parameters was analyzed. Therefore, the health state of rolling bearing can be automatically monitored by quantitatively identifying the transition state of the phase trajectory map. A health assessment model was established to evaluate the health state of bearings. Wavelet packet transform was used to extract the features (approximate entropy) of bearing vibration signal, which were input into the self-organizing map (SOM) network. The health state of rolling bearings was then assessed using the SOM network and confidence values. A case study on health monitoring and assessment for rolling bearing was conducted to demonstrate the effectiveness and accuracy of the proposed methods

Analysis of Ball Bearing Defects in Synchronous Machines using Electrical Measurements

Rolling element bearings are used in most electrical machines, especially for small and medium size applications. Under non-ideal operating conditions, ball bearing condition degrades by fatigue, ambient vibration, misalignment, overloading, contamination, corrosion from water or chemicals, improper lubrication, shaft currents and residual stress left from the bearing manufacturing process. All of these conditions eventually lead to increased vibration and acoustic noise during machine operation which at some point in time results in unexpected bearing failure. Over the years, a great number of publications have been devoted to the detection of mechanical faults, including rolling element bearing defects and torsional defects, in electrical machines based on Electrical Signature Analysis (ESA). It has been observed that these faults can affect either the stator to rotor air-gap distribution or the running speed of the machine, which can be reflected in the signature of the electrical signals. However, the physical link between the mechanical degradation and the electrical signature is still not explained well. A multi-physics model is developed by joining the detailed mechanical model of a rotor bearing system and the electrical model of a synchronous machine in this research. This combined model is capable of describing the transmission of information originating from bearing faults and their impact on the variations of the measured electrical signals. The electrical machine model is developed based on winding function approach and its validity is demonstrated by a more accurate Finite Element Method (FEM) model. The mechanical model consists of a high fidelity rotor-bearing system with detailed nonlinear ball bearing model and a flexible finite element shaft model. It is validated using the housing vibration data collected from some experiments. Generalized roughness bearing anomalies are linked to load torque ripples and airgap variations, while being related to current signature by phase and amplitude modulation. Considering that the induced characteristic signatures are usually subtle broadband changes in the current spectra, these signatures are easily affected by input power quality variations, machine manufacturing imperfections and environmental noise. In this research, a new algorithm is proposed to isolate the influence of the external disturbances of power quality, machine manufacturing imperfections and environmental noise, and to improve the effectiveness of applying the ESA for generalized roughness bearing defects. The results show that the proposed method is effective in analyzing the generalized roughness bearing anomaly in synchronous machines. Furthermore, the electrical signatures are analyzed in a synchronous machine with bearing defects. The proposed fault detection method employs a Zoomed Fast Fourier Transform (ZFFT) and Principal Component Analysis (PCA) and it is also tested on the available experimental data. The results show that amplitude induced electrical harmonics are related to the level of vibration, and the electrical signatures are affected heavily by other variables, such as power quality and load fluctuation. The proposed method is shown to be effective on detecting generalized roughness bearing defects in synchronous machines