Failure Diagnosis and Prognosis of Safety Critical Systems: Applications in Aerospace Industries

Many safety-critical systems such as aircraft, space crafts, and large power plants are required to operate in a reliable and efficient working condition without any performance degradation. As a result, fault diagnosis and prognosis (FDP) is a research topic of great interest in these systems. FDP systems attempt to use historical and current data of a system, which are collected from various measurements to detect faults, diagnose the types of possible failures, predict and manage failures in advance. This thesis deals with FDP of safety-critical systems. For this purpose, two critical systems including a multifunctional spoiler (MFS) and hydro-control value system are considered, and some challenging issues from the FDP are investigated. This research work consists of three general directions, i.e., monitoring, failure diagnosis, and prognosis. The proposed FDP methods are based on data-driven and model-based approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the remaining useful life (RUL) of the faulty components accurately and efficiently. In this regard, two dierent methods are developed. A modular FDP method based on a divide and conquer strategy is presented for the MFS system. The modular structure contains three components:1) fault diagnosis unit, 2) failure parameter estimation unit and 3) RUL unit. The fault diagnosis unit identifies types of faults based on an integration of neural network (NN) method and discrete wavelet transform (DWT) technique. Failure parameter estimation unit observes the failure parameter via a distributed neural network. Afterward, the RUL of the system is predicted by an adaptive Bayesian method. In another work, an innovative data-driven FDP method is developed for hydro-control valve systems. The idea is to use redundancy in multi-sensor data information and enhance the performance of the FDP system. Therefore, a combination of a feature selection method and support vector machine (SVM) method is applied to select proper sensors for monitoring of the hydro-valve system and isolate types of fault. Then, adaptive neuro-fuzzy inference systems (ANFIS) method is used to estimate the failure path. Similarly, an online Bayesian algorithm is implemented for forecasting RUL. Model-based methods employ high-delity physics-based model of a system for prognosis task. In this thesis, a novel model-based approach based on an integrated extended Kalman lter (EKF) and Bayesian method is introduced for the MFS system. To monitor the MFS system, a residual estimation method using EKF is performed to capture the progress of the failure. Later, a transformation is utilized to obtain a new measure to estimate the degradation path (DP). Moreover, the recursive Bayesian algorithm is invoked to predict the RUL. Finally, relative accuracy (RA) measure is utilized to assess the performance of the proposed methods

Optimal slip control for tractors with feedback of drive torque

Traction efficiency of tractors barely reaches 50 % in field operations. On the other hand, modern trends in agriculture show growth of the global tractor markets and at the same time increased demands for greenhouse gas emission reduction as well as energy efficiency due to increasing fuel costs. Engine power of farm tractors is growing at 1.8 kW per year reaching today about 500 kW for the highest traction class machines. The problem of effective use of energy has become crucial. Existing slip control approaches for tractors do not fulfil this requirement due to fixed reference set-point. The present work suggests an optimal control scheme based on set-point optimization and on assessment of soil conditions, namely, wheel-ground parameter identification using fuzzy-logic-assisted adaptive unscented Kalman filter.:List of figures VIII List of tables IX Keywords XI List of abbreviations XII List of mathematical symbols XIII Indices XV 1 Introduction 1 1.1 Problem description and challenges 1 1.1.1 Development of agricultural industry 1 1.1.2 Power flows and energy efficiency of a farm tractor 2 1.2 Motivation 9 1.3 Purpose and approach 12 1.3.1 Purpose and goals 12 1.3.2 Brief description of methodology 14 1.3.2.1 Drive torque feedback 14 1.3.2.2 Measurement signals 15 1.3.2.3 Identification of traction parameters 15 1.3.2.4 Definition of optimal slip 15 1.4 Outline 16 2 State of the art in traction management and parameter estimation 17 2.1 Slip control for farm tractors 17 2.2 Acquisition of drive torque feedback 23 2.3 Tire-ground parameter estimation 25 2.3.1 Kalman filter 25 2.3.2 Extended Kalman filter 27 2.3.3 Unscented Kalman filter 27 2.3.4 Adaptation algorithms for Kalman filter 29 3 Modelling vehicle dynamics for traction control 31 3.1 Tire-soil interaction 31 3.1.1 Forces in wheel-ground contact 32 3.1.1.1 Vertical force 32 3.1.1.2 Tire-ground surface geometry 34 3.1.2 Longitudinal force 36 3.1.3 Zero-slip condition 37 3.1.3.1 Soil shear stress 38 3.1.3.2 Rolling resistance 39 3.2 Vehicle body and wheels 40 3.2.1 Short description of Multi-Body-Simulation 40 3.2.2 Vehicle body and wheel models 42 3.2.3 Wheel structure 43 3.3 Stochastic input signals 45 3.3.1 Influence of trend and low-frequency components 47 3.3.2 Modelling stochastic signals 49 3.4 Further components and general view of tractor model 53 3.4.1 Generator, intermediate circuit, electrical motors and braking resistor 53 3.4.2 Diesel engine 55 4 Identification of traction parameters 56 4.1 Description of identification approaches 56 4.2 Vehicle model 58 4.2.1 Vehicle longitudinal dynamics 58 4.2.2 Wheel rotational dynamics 59 4.2.3 Tire dynamic rolling radius and inner rolling resistance coefficient 60 4.2.4 Whole model 61 4.3 Static methods of parameter identification 63 4.4 Adaptation mechanism of the unscented Kalman filter 63 4.5 Fuzzy supervisor for the adaptive unscented Kalman filter 66 4.5.1 Structure of the fuzzy supervisor 67 4.5.2 Stability analysis of the adaptive unscented Kalman filter with the fuzzy supervisor 69 5 Optimal slip control 73 5.1 Approaches for slip control by means of traction control system 73 5.1.1 Feedback compensation law 73 5.1.2 Sliding mode control 74 5.1.3 Funnel control 77 5.1.4 Lyapunov-Candidate-Function-based control, other approaches and choice of algorithm 78 5.2 General description of optimal slip control algorithm 79 5.3 Estimation of traction force characteristic curves 82 5.4 Optimal slip set-point computation 85 6 Verification of identification and optimal slip control systems 91 6.1 Simulation results 91 6.1.1 Identification of traction parameters 91 6.1.1.1 Comparison of extended Kalman filter and unscented Kalman filter 92 6.1.1.2 Comparison of ordinary and adaptive unscented Kalman filters 96 6.1.1.3 Comparison of the adaptive unscented Kalman filter with the fuzzy supervisor and static methods 99 6.1.1.4 Description of soil conditions 100 6.1.1.5 Identification of traction parameters under changing soil conditions 101 6.1.2 Approximation of characteristic curves 102 6.1.3 Slip control with reference of 10% 103 6.1.4 Comparison of operating with fixed and optimal slip reference 104 6.2 Experimental verification 108 6.2.1 Setup and description of the experiments 108 6.2.2 Virtual slip control without load machine 109 6.2.3 Virtual slip control with load machine 113 7 Summary, conclusions and future challenges 122 7.1 Summary of results and discussion 122 7.2 Contributions of the dissertation 123 7.3 Future challenges 123 Bibliography 125 A Measurement systems 137 A.1 Measurement of vehicle velocity 137 A.2 Measurement of wheel speed 138 A.3 Measurement or estimation of wheel vertical load 139 A.4 Measurement of draft force 140 A.5 Further possible measurement systems 141 B Basic probability theoretical notions 142 B.1 Brief description of the theory of stochastic processes 142 B.2 Properties of stochastic signals 144 B.3 Bayesian filtering 145 C Modelling stochastic draft force and field microprofile 147 D Approximation of kappa-curves 152 E Simulation parameters 15

On Increasing the Automation Level of Heavy-Duty Hydraulic Manipulators with Condition Monitoring of the Hydraulic System and Energy-Optimised Redundancy Resolution

Hydraulic manipulators on mobile machines are predominantly used for excavation and lifting applications at construction sites and for heavy-duty material handling in the forest industry due to their superior power-density and rugged nature. These manipulators are conventionally open-loop controlled by human operators who are sufficiently skilled to operate the machines. However, in the footsteps of pioneering original equipment manufacturers (OEMs) and to keep up with the intensifying demand for innovation, more and more mobile machine OEMs have a major interest in significantly increasing the automation level of their hydraulic manipulators and improving the operation of manipulators. In this thesis, robotic software-based functionalities in the form of modelbased condition monitoring and energy-optimal redundancy resolution which facilitate increased automation level of hydraulic manipulators are proposed.A condition monitoring system generally consists of software modules and sensors which co-operate harmonically and monitor the hydraulic system’s health in real-time based on an indirect measure of this system’s health. The premise is that when this condition monitoring system recognises that the system’s health has deteriorated past a given threshold (in other words, when a minor fault is detected, such as a slowly increasing internal leakage of the hydraulic cylinder), the condition monitoring module issues an alarm to warn the system operator of the malfunction, and the module could ideally diagnose the fault cause. In addition, when faced with severe faults, such as an external leakage or an abruptly increasing internal leakage in the hydraulic system, an alarm from the condition monitoring system ensures that the machine is quickly halted to prevent any further damage to the machine or its surroundings.The basic requirement in the design of such a condition monitoring system is to make sure that this system is robust and fault-sensitive. These properties are difficult to achieve in complex mobile hydraulic systems on hydraulic manipulators due to the modelling uncertainties affecting these systems. The modelling uncertainties affecting mobile hydraulic systems are specific compared with many other types of systems and are large because of the hydraulic system complexities, nonlinearities, discontinuities and inherently time-varying parameters. A feasible solution to this modelling uncertainty problem would be to either attenuate the effect of modelling errors on the performance of model-based condition monitoring or to develop improved non-model-based methods with increased fault-sensitivity. In this research work, the former model-based approach is taken. Adaptation of the model residual thresholds based on system operating points and reliable, load-independent system models are proposed as integral parts of the condition monitoring solution to the modelling uncertainty problem. These proposed solutions make the realisation of condition monitoring solutions more difficult on heavy-duty hydraulic manipulators compared with fixed-load manipulators, for example. These solutions are covered in detail in a subset of the research publications appended to this thesis.There is wide-spread interest from hydraulic manipulator OEMs in increasing the automation level of their hydraulic manipulators. Most often, this interest is related to semi-automation of repetitive work cycles to improve work productivity and operator workload circumstances. This robotic semi-automated approach involves resolving the kinematic redundancy of hydraulic manipulators to obtain motion references for the joint controller to enable desirable closed-loop controlled motions. Because conventional redundancy resolutions are usually sub-optimal at the hydraulic system level, a hydraulic energy-optimised, global redundancy resolution is proposed in this thesis for the first time. Kinematic redundancy is resolved energy optimally from the standpoint of the hydraulic system along a prescribed path for a typical 3-degrees-of-freedom (3-DOF) and 4-DOF hydraulic manipulator. Joint motions are also constrained based on the actuators’ position, velocity and acceleration bounds in hydraulic manipulators in the proposed solution. This kinematic redundancy resolution topic is discussed in the last two research papers. Overall, both designed manipulator features, condition monitoring and energy-optimised redundancy resolution, are believed to be essential for increasing the automation of hydraulic manipulators

State estimators in soft sensing and sensor fusion for sustainable manufacturing

State estimators, including observers and Bayesian filters, are a class of model-based algorithms for estimating variables in a dynamical system given sensor measurements of related system states. They can be used to derive fast and accurate estimates of system variables which cannot be measured directly (’soft sensing’) or for which only noisy, intermittent, delayed, indirect or unreliable measurements are available, perhaps from multiple sources (’sensor fusion’). In this paper we introduce the concepts and main methods of state estimation and review recent applications in improving the sustainability of manufacturing processes. It is shown that state estimation algorithms can play a key role in manufacturing systems to accurately monitor and control processes to improve efficiencies, lower environmental impact, enhance product quality, improve the feasibility of processing more sustainable raw materials, and ensure safer working environments for humans. We discuss current and emerging trends in using state estimation as a framework for combining physical knowledge with other sources of data for monitoring and control of distributed manufacturing systems

Real time kick estimation and monitoring in managed pressure drilling system

The influx of reservoir fluid (kick) has a significant impact on drilling operations. Unmitigated kick can lead to a blowout causing financial losses and impacting human lives on the rig. Kick is an unmeasured disturbance in the system, and so detection, estimation, and mitigation are essential for the safety and efficiency of the drilling operation. Our main objective is to develop a real time warning system for a managed pressure drilling (MPD) system. In the first part of the research, an unscented Kalman filter (UKF) based estimator was implemented to simultaneously estimate the bit flow-rate, and kick. The estimated kick is further used to predict the impact of the kick. Optimal control theory is used to calculate the time to mitigate the kick in the best case scenario. An alarm system is developed based on total predicted influx and pressure rise in the system and compared with actual well operation control matrix. Thus, the proposed method can estimate, monitor, and manage kick in real time, enhancing the safety and efficiency of the MPD operation. So, a robust warning framework for the operators based on real life operational conditions is created in the second part of the research. Proposed frameworks are successfully validated by applying to several case studies

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

Detection and Localisation of Pipe Bursts in a District Metered Area Using an Online Hydraulic Model

This thesis presents a research work on the development of new methodology for near-real-time detection and localisation of pipe bursts in a Water Distribution System (WDS) at the District Meters Area (DMA) level. The methodology makes use of online hydraulic model coupled with a demand forecasting methodology and several statistical techniques to process the hydraulic meters data (i.e., flows and pressures) coming from the field at regular time intervals (i.e. every 15 minutes). Once the detection part of the methodology identifies a potential burst occurrence in a system it raises an alarm. This is followed by the application of the burst localisation methodology to approximately locate the event within the District Metered Area (DMA). The online hydraulic model is based on data assimilation methodology coupled with a short-term Water Demand Forecasting Model (WDFM) based on Multi-Linear Regression. Three data assimilation methods were tested in the thesis, namely the iterative Kalman Filter method, the Ensemble Kalman Filter method and the Particle Filter method. The iterative Kalman Filter (i-KF) method was eventually chosen for the online hydraulic model based on the best overall trade-off between water system state prediction accuracy and computational efficiency. The online hydraulic model created this way was coupled with the Statistical Process Control (SPC) technique and a newly developed burst detection metric based on the moving average residuals between the predicted and observed hydraulic states (flows/pressures). Two new SPC-based charts with associated generic set of control rules for analysing burst detection metric values over consecutive time steps were introduced to raise burst alarms in a reliable and timely fashion. The SPC rules and relevant thresholds were determined offline by performing appropriate statistical analysis of residuals. The above was followed by the development of the new methodology for online burst localisation. The methodology integrates the information on burst detection metric values obtained during the detection stage with the new sensitivity matrix developed offline and hydraulic model runs used to simulate potential bursts to identify the most likely burst location in the pipe network. A new data algorithm for estimating the ‘normal’ DMA demand and burst flow during the burst period is developed and used for localisation. A new data algorithm for statistical analysis of flow and pressure data was also developed and used to determine the approximate burst area by producing a list of top ten suspected burst location nodes. The above novel methodologies for burst detection and localisation were applied to two real-life District Metred Areas in the United Kingdom (UK) with artificially generated flow and pressure observations and assumed bursts. The results obtained this way show that the developed methodology detects pipe bursts in a reliable and timely fashion, provides good estimate of a burst flow and accurately approximately locates the burst within a DMA. In addition, the results obtained show the potential of the methodology described here for online burst detection and localisation in assisting Water Companies (WCs) to conserve water, save energy and money. It can also enhance the UK WCs’ profile customer satisfaction, improve operational efficiency and improve the OFWAT’s Service Incentive Mechanism (SIM) scores.This STREAM project is funded by the Engineering and Physical Sciences Research Council and Industrial Collaborator, United Utilities

Modelling and estimation of vanadium redox flow batteries: a review

Redox flow batteries are one of the most promising technologies for large-scale energy storage, especially in applications based on renewable energies. In this context, considerable efforts have been made in the last few years to overcome the limitations and optimise the performance of this technology, aiming to make it commercially competitive. From the monitoring point of view, one of the biggest challenges is the estimation of the system internal states, such as the state of charge and the state of health, given the complexity of obtaining such information directly from experimental measures. Therefore, many proposals have been recently developed to get rid of such inconvenient measurements and, instead, utilise an algorithm that makes use of a mathematical model in order to rely only on easily measurable variables such as the system’s voltage and current. This review provides a comprehensive study of the different types of dynamic models available in the literature, together with an analysis of the existing model-based estimation strategies. Finally, a discussion about the remaining challenges and possible future research lines on this field is presented.The research that gave rise to these results received support from “la Caixa” Foundation (ID 100010434. Fellowship code LCF/BQ/DI21/11860023) , the CSIC program for the Spanish Recovery, Transformation and Resilience Plan funded by the Recovery and Resilience Facility of the European Union, established by the Regulation (EU) 2020/2094, CSIC Interdisciplinary Thematic Platform (PTI+) Transición Energética Sostenible+ (PTI-TRANSENER+ project TRE2103000), the Spanish Ministry of Science and Innovation (project PID2021-126001OB-C31 funded by MCIN/AEI/10.13039/501100011033 / ERDF,EU) and the Spanish Ministry of Economy and Competitiveness under Project DOVELAR (ref. RTI2018-096001-B-C32).Peer ReviewedPostprint (published version

Applications in Electronics Pervading Industry, Environment and Society

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs