Computational framework for real-time diagnostics and prognostics of aircraft actuation systems

Prognostics and health management (PHM) are emerging approaches to product life cycle that will maintain system safety and improve reliability, while reducing operating and maintenance costs. This is particularly relevant for aerospace systems, where high levels of integrity and high performances are required at the same time. We propose a novel strategy for the nearly real-time fault detection and identification (FDI) of a dynamical assembly, and for the estimation of remaining useful life (RUL) of the system. The availability of a timely estimate of the health status of the system will allow for an informed adaptive planning of maintenance and a dynamical reconfiguration of the mission profile, reducing operating costs and improving reliability. This work addresses the three phases of the prognostic flow – namely (1) signal acquisition, (2) fault detection and identification, and (3) remaining useful life estimation – and introduces a computationally efficient procedure suitable for real-time, on-board execution. To achieve this goal, we propose to combine information from physical models of different fidelity with machine learning techniques to obtain efficient representations (surrogate models) suitable for nearly real-time applications. Additionally, we propose an importance sampling strategy and a novel approach to model damage propagation for dynamical systems. The methodology is assessed for the FDI and RUL estimation of an aircraft electromechanical actuator (EMA) for secondary flight controls. The results show that the proposed method allows for a high precision in the evaluation of the system RUL, while outperforming common model-based techniques in terms of computational time

Computational framework for real-time diagnostics and prognostics of aircraft actuation systems

Prognostics and Health Management (PHM) are emerging approaches to product life cycle that will maintain system safety and improve reliability, while reducing operating and maintenance costs. This is particularly relevant for aerospace systems, where high levels of integrity and high performances are required at the same time. We propose a novel strategy for the nearly real-time Fault Detection and Identification (FDI) of a dynamical assembly, and for the estimation of Remaining Useful Life (RUL) of the system. The availability of a timely estimate of the health status of the system will allow for an informed adaptive planning of maintenance and a dynamical reconfiguration of the mission profile, reducing operating costs and improving reliability. This work addresses the three phases of the prognostic flow - namely (1) signal acquisition, (2) Fault Detection and Identification, and (3) Remaining Useful Life estimation - and introduces a computationally efficient procedure suitable for real-time, on-board execution. To achieve this goal, we propose to combine information from physical models of different fidelity with machine learning techniques to obtain efficient representations (surrogate models) suitable for nearly real-time applications. Additionally, we propose an importance sampling strategy and a novel approach to model damage propagation for dynamical systems. The methodology is assessed for the FDI and RUL estimation of an aircraft electromechanical actuator (EMA) for secondary flight controls. The results show that the proposed method allows for a high precision in the evaluation of the system RUL, while outperforming common model-based techniques in terms of computational time.Comment: 57 page

A Review: Prognostics and Health Management in Automotive and Aerospace

Prognostics and Health Management (PHM) attracts increasing interest of many researchers due to its potentially important applications in diverse disciplines and industries. In general, PHM systems use real-time and historical state information of subsystems and components of the operating systems to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. Every year, a substantial number of papers in this area including theory and practical applications, appear in academic journals, conference proceedings and technical reports. This paper aims to summarize and review researches, developments and recent contributions in PHM for automotive- and aerospace industries. It can also be considered as the starting point for researchers and practitioners in general to assist them through PHM implementation and help them to accomplish their work more easily.Algorithms and the Foundations of Software technolog

A review of aircraft environmental control system simulation and diagnostics

The aircraft Environmental Control System (ECS) enables the aircraft to maintain a comfortable and safe environment for its passengers throughout its operating envelope. The Pressurised Air Conditioner (PACK) is the heart of the ECS, and is composed of multiple sub-systems: heat exchangers, valves, compressor, turbine, and a water separator. The PACK’s principle function is to enable conditioning of the hot, high pressure bleed air from the engine or APU, for temperature, pressure and humidity against the cabin requirements. The operation of the PACK is governed by a control system which has the ability to mask degradation in its component during operation until severe degradation or failure results. The required maintenance is then both costly and disruptive. The PACK has been reported as major driver of unscheduled maintenance by the operators. The aviation industry is currently proactively exploring innovative health management solutions that aid the maintenance of aircraft key systems based on predictive based maintenance approaches using online condition monitoring techniques. This paper presents a comprehensive review of the simulation and diagnostic methodologies applicable to fault diagnostics of the ECS PACK. The existing literature suggests that model-based and data-driven methods are effective for conducting fault detection and isolation of the PACK system. The conceived findings indicate that the model-based diagnostic approach have been extensively employed to conduct PACK diagnostics at component level only. Their successful implementation requires robust experimental verification and validation against the actual data under healthy and faulty conditions. Although a substantial amount of work has been reported on developing first principles based simulation models and diagnostic strategies for the ECS, the acquired findings suggest that there is a compelling need for a verified and validated ECS simulation model to enable accurate PACK system-level diagnostics based on single and multiple component level degradation scenarios. It has also been identified that the existing literature lacks the evaluation of humidity regulation and the effect of the control system on the PACK performance characteristics. Finally, a taxonomy of diagnostic techniques and simulation models is compiled based on the available literature

Система мониторинга устройств железнодорожной автоматики на основе промышленного «Интернета вещей»

The features of monitoring systems for railway infrastructure and rolling stock are considered. The main approaches to organisation of monitoring of railway infrastructure and rolling stock objects are described, their advantages and disadvantages are noted. The main objective of this work is to present to the reader a conceptual vision of a system for monitoring devices and systems for ensuring train traffic safety, using technologies for transmitting diagnostic information over a radio channel. The methods of the theory of technical diagnostics and monitoring were used. Attention is focused on the use of wireless data transmission technologies and the use of autonomous industrial automation sensors for monitoring systems for railway automation devices.The architecture of the monitoring system is presented. The description of the system itself and the monitoring technology is given, the main advantages of the presented approach are noted, which, first, are linked to reduction of the volume of design work and of energy consumption of the system as a whole. The disadvantages are associated with the need to replace autonomous power supply sources, ensure security of the data transmission network, to proceed with periodic verification and calibration of measuring instruments. The basic diagrams of connecting sensors for measuring physical quantities to the circuit units of railway automation are presented. A list of parameters necessary for high-quality and effective monitoring of railway automation devices is given. The need is noted for both the control of mechanical and geometric parameters of devices and the accounting of data from interconnected objects of railway infrastructure and rolling stock. The proposed approach can find its application in the field of railway automation and, first of all, at those facilities that are located in premises with limited area (e.g. at subway facilities).Рассматриваются особенности систем мониторинга объектов железнодорожной инфраструктуры и подвижного состава. Описаны основные подходы к организации мониторинга объектов железнодорожной инфраструктуры и подвижного состава, отмечены их достоинства и недостатки. Основной целью настоящей работы является представление читателю концептуального видения системы мониторинга устройств и систем обеспечения безопасности движения поездов, использующей технологии передачи диагностической информации по радиоканалу. Использованы методы теории технической диагностики и мониторинга.Внимание сфокусировано на применении технологий беспроводной передачи данных и использовании автономных датчиков промышленной автоматизации при организации систем мониторинга устройств железнодорожной автоматики. Представлена архитектура системы мониторинга.Дано описание самой системы и технологии мониторинга, отмечаются основные преимущества представленного подхода, заключающиеся, прежде всего, в сокращении объёмов проектных работ и снижении энергопотребления системы в целом. Недостатком же является необходимость замены автономных источников питания, обеспечения защищённости сети передачи данных, периодической поверки и калибровки средств измерения.Приведены основные схемы подключения датчиков измерения физических величин к схемным узлам железнодорожной автоматики. Дан перечень необходимых для качественного и эффективного мониторинга устройств железнодорожной автоматики параметров. Отмечается необходимость как контроля механических и геометрических параметров устройств, так и учёт данных от взаимосвязанных объектов железнодорожной инфраструктуры и подвижного состава.Предложенный подход может найти своё применение в сфере железнодорожной автоматики и, прежде всего, на тех объектах, которые расположены на ограниченных площадях помещений (например, в метрополитенах)

A systematic mapping of the advancing use of machine learning techniques for predictive maintenance in the manufacturing sector

The increasing availability of data, gathered by sensors and intelligent machines, is chang-ing the way decisions are made in the manufacturing sector. In particular, based on predictive approach and facilitated by the nowadays growing capabilities of hardware, cloud-based solutions, and new learning approaches, maintenance can be scheduled—over cell engagement and resource monitoring—when required, for minimizing (or managing) unexpected equipment failures, improving uptime through less aggressive maintenance schedules, shortening unplanned downtime, reducing excess (direct and indirect) cost, reducing long-term damage to machines and processes, and improve safety plans. With access to increased levels of data (and over learning mechanisms), companies have the capability to conduct statistical tests using machine learning algorithms, in order to uncover root causes of problems previously unknown. This study analyses the maturity level and contributions of machine learning methods for predictive maintenance. An upward trend in publications for predictive maintenance using machine learning techniques was identified with the USA and China leading. A mapping study—steady set until early 2019 data—was employed as a formal and well-structured method to synthesize material and to report on pervasive areas of research. Type of equipment, sensors, and data are mapped to properly assist new researchers in positioning new research activities in the domain of smart maintenance. Hence, in this paper, we focus on data-driven methods for predictive maintenance (PdM) with a comprehensive survey on applications and methods until, for the sake of commenting on stable proposal, 2019 (early included). An equal repartition between evaluation and validation studies was identified, this being a symptom of an immature but growing research area. In addition, the type of contribution is mainly in the form of models and methodologies. Vibrational signal was marked as the most used data set for diagnosis in manufacturing machinery monitoring; furthermore, supervised learning is reported as the most used predictive approach (ensemble learning is growing fast). Neural networks, followed by random forests and support vector machines, were identified as the most applied methods encompassing 40% of publications, of which 67% related to deep neural network with long short-term memory predominance. Notwithstanding, there is no robust approach (no one reported optimal performance over different case tests) that works best for every problem. We finally conclude the research in this area is moving fast to gather a separate focused analysis over the last two years (whenever stable implementations will appear)

Platform health management for aircraft maintenance – a review

Aircraft health management has been researched at both component and system levels. In instances of certain aircraft faults, like the Boeing 777 fuel icing problem, there is evidence suggesting that a platform approach using an Integrated Vehicle Health Management (IVHM) system could have helped detect faults and their interaction effects earlier, before they became catastrophic. This paper reviews aircraft health management from the aircraft maintenance point of view. It emphasizes the potential of a platform solution to diagnose faults, and their interaction effects, at an early stage. The paper conducts a thorough analysis of existing literature concerning maintenance and its evolution, delves into the application of Artificial Intelligence (AI) techniques in maintenance, explains the rationale behind their employment, and illustrates how AI implementation can enhance fault detection using platform sensor data. Further, it discusses how computational severity and criticality indexes (health indexes) can potentially be complementary to the use of AI for the provision of maintenance information on aircraft components, for assisting operational decisions

Predictive Maintenance of Wind Generators based on AI Techniques

As global warming is slowly becoming a dangerous reality, governments and private institutions are introducing policies to minimize it. Those policies have led to the development and deployment of Renewable Energy Sources (RESs), which introduces new challenges, among which the minimization of downtime and Levelised Cost of Energy (LCOE) by optimizing maintenance strategy where early detection of incipient faults is of significant intent. Hence, this is the focus of this thesis. While there are several maintenance approaches, predictive maintenance can utilize SCADA readings from large scale power plants to detect early signs of failures, which can be characterized by abnormal patterns in the measurements. There exists several approaches to detect these patterns such as model-based or hybrid techniques, but these require the detailed knowledge of the analyzed system. As SCADA system collects large amounts of data, machine learning techniques can be used to detect the underlying failure patterns and notify customers of the abnormal behaviour. In this work, a novel framework based on machine learning techniques for fault prediction of wind farm generators is developed for an actual customer. The proposed fault prognosis methodology addresses data limitation such as class imbalance and missing data, performs statistical tests on time series to test for its stationarity, selects the features with the most predictive power, and applies machine learning models to predict a fault with 1 hour horizon. The proposed techniques are tested and validated using historical data for a wind farm in Summerside, Prince Edward Island (PEI), Canada, and models are evaluated based on appropriate evaluation metrics. The results demonstrate the ability of the proposed methodology to predict wind generator failures, and the viability of the proposed methodology for optimizing preventive maintenance strategies

Measuring Level of Degradation in Power Semiconductor Devices using Emerging Techniques

Title from PDF of title page viewed May 24, 2021Dissertation advisor: Faisal KhanVitaIncludes bibliographical references (page 124-154)Thesis (Ph.D.)--School of Computing and Engineering and Department of Mathematics and Statistics, University of Missouri--Kansas City, 2021High thermal and electrical stress, over a period of time tends to deteriorate the health of power electronic switches. Being a key element in any high-power converter systems, power switches such as insulated-gate bipolar junction transistors (IGBTs) and metal-oxide semiconductor field-effect transistors (MOSFETs) are constantly monitored to predict when and how they might fail. A huge fraction of research efforts involves the study of power electronic device reliability and development of novel techniques with higher accuracy in health estimation of such devices. Until today, no other existing techniques can determine the number of lifted bond wires and their locations in a live IGBT module, although this information is extremely helpful to understand the overall state of health (SOH) of an IGBT power module. Through this research work, two emerging methods for online condition monitoring of power IGBTs and MOSFETs have been proposed. First method is based on reflectometry, more specifically, spread spectrum time domain reflectometry (SSTDR) and second method is based on ultrasound based non-destructive evaluation (NDE). Unlike traditional methods, the proposed methods do not require measuring any electrical parameters (such as voltage or current), therefore, minimizes the measurement error. In addition, both of these methods are independent of the operating points of the converter which makes the application of these methods more feasible for any field application. As part of the research, the RL-equivalent circuit to represent the bond wires of an IGBT module has been developed for the device under test. In addition, an analytical model of ultrasound interaction with the bond wires has been derived in order to efficiently detect the bond wire lift offs within the IGBT power module. Both of these methods are equally applicable to the wide band gap (WBG) power devices and power converters. The successful implementation of these methods creates a provision for condition monitoring (CM) hardware embedded gate driver module which will significantly reduce the overall health monitoring cost.Introduction -- Failure mechanisms of modern power electronic devices -- Existing degradation detection & lifetime prediction techniques -- Accelerated aging methods -- SSTDR based degradation detection -- Ultrasound based degradation -- Degradation detection of wide band gap power devices -- Conclusions and future researc

Quality Assessment Methods for Textual Conversational Interfaces: A Multivocal Literature Review

The evaluation and assessment of conversational interfaces is a complex task since such software products are challenging to validate through traditional testing approaches. We conducted a systematic Multivocal Literature Review (MLR), on five different literature sources, to provide a view on quality attributes, evaluation frameworks, and evaluation datasets proposed to provide aid to the researchers and practitioners of the field. We came up with a final pool of 118 contributions, including grey (35) and white literature (83). We categorized 123 different quality attributes and metrics under ten different categories and four macro-categories: Relational, Conversational, User-Centered and Quantitative attributes. While Relational and Conversational attributes are most commonly explored by the scientific literature, we testified a predominance of User-Centered Attributes in industrial literature. We also identified five different academic frameworks/tools to automatically compute sets of metrics, and 28 datasets (subdivided into seven different categories based on the type of data contained) that can produce conversations for the evaluation of conversational interfaces. Our analysis of literature highlights that a high number of qualitative and quantitative attributes are available in the literature to evaluate the performance of conversational interfaces. Our categorization can serve as a valid entry point for researchers and practitioners to select the proper functional and non-functional aspects to be evaluated for their products