Methodological framework for implementation of a prediction reliability model of IGBT power modules used in railway applications

The control of critical electronic components reliability is one of the main issues in railway traction applications. Insulated Gate Bipolar Transistors (IGBT) modules are part of these components. They are subjected to high stresses due to severe conditions of use of the train. The increase of requirements in terms of reliability and safety imposes to be able to assess these dependability measures. This paper introduces a methodological framework for predicting reliability of IGBT based on an innovative and structured Bayesian approach

Prognostics of Insulated Gate Bipolar Transistors

Insulated gate bipolar transistors (IGBTs) are the devices of choice for medium and high power, low frequency applications. IGBTs have been reported to fail under excessive electrical and thermal stresses in variable speed drives and are considered as reliability problems in wind turbines, inverters in hybrid electric vehicles and railway traction motors. There is a need to develop methods to detect anomalous behavior and predict the remaining useful life (RUL) of IGBTs to prevent system downtime and costly failures. In this study, a framework for prognostics of IGBTs was developed to provide early warnings of failure and predict the remaining useful life. The prognostic framework was implemented on non punch through (NPT) IGBTs. Power cycling of IGBTs was performed and the gate-emitter voltage, collector-emitter voltage, collector-emitter current and case temperature was monitored in-situ during aging. The on-state collector-emitter current (ICE(ON)) and collector-emitter voltage (VCE(ON)) were identified as precursors to IGBT failure. Electrical characterization and X-ray analysis was performed before and after aging to map degradation in the devices to observed trends in the precursor parameters. A Mahalanobis distance based approach was used for anomaly detection. The initial ICE(ON) and VCE(ON) parameters were used to compute the healthy MD distance. This healthy MD distance was transformed and the mean and standard deviation of the transformed MD data was obtained. The μ+3σ upper bound obtained from the transformed healthy MD was then used as a threshold for anomaly detection. This approach was able to detect anomalous behavior in IGBTs before failure. Upon anomaly detection, a particle filter approach was used for predicting the remaining useful life of the IGBTs. A system model was developed using the degradation trend of the VCE(ON) parameter. This model was obtained by a least squares regression of the IGBT degradation curve. The tracking and prediction performance of the model with the particle filter was demonstrated

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

Towards a More Flexible, Sustainable, Efficient and Reliable Induction Cooking: A Power Semiconductor Device Perspective

Esta tesis tiene como objetivo fundamental la mejora de la flexibilidad, sostenibilidad, eficiencia y fiabilidad de las cocinas de inducción por medio de la utilización de dispositivos semiconductores de potencia: Dentro de este marco, existe una funcionalidad que presenta un amplio rango de mejora. Se trata de la función de multiplexación de potencia, la cual pretende resolverse de una manera más eficaz por medio de la sustitución de los comúnmente utilizados relés electromecánicos por dispositivos de estado sólido. De entre todas las posibles implementaciones, se ha identificado entre las más prometedoras a aquellas basadas en dispositivos de alta movilidad de electrones (HEMT) de Nitruro de Galio (GaN) y de aquellas basadas en Carburo de Silicio (SiC), pues presentan unas características muy superiores a los relés a los que se pretende sustituir. Por el contrario, otras soluciones que inicialmente parecían ser muy prometedoras, como los MOSFETs de Súper-Unión, han presentado una serie de comportamientos anómalos, que han sido estudiados minuciosamente por medio de simulaciones físicas a nivel de chip. Además, se analiza en distintas condiciones la capacidad en cortocircuito de dispositivos convencionalmente empleados en cocinas de inducción, como son los IGBTs, tratándose de encontrar el equilibrio entre un comportamiento robusto al tiempo que se mantienen bajas las pérdidas de potencia. Por otra parte, también se estudia la robustez y fiabilidad de varios GaN HEMT de 600- 650 V tanto de forma experimental como por medio de simulaciones físicas. Finalmente se aborda el cálculo de las pérdidas de potencia en convertidores de potencia resonantes empleando técnicas de termografía infrarroja. Por medio de esta técnica no solo es posible medir de forma precisa las diferentes contribuciones de las pérdidas, sino que también es posible apreciar cómo se distribuye la corriente a nivel de chip cuando, por ejemplo, el componente opera en modo de conmutación dura. Como resultado, se obtiene información relevante relacionada con modos de fallo. Además, también ha sido aprovechar las caracterizaciones realizadas para obtener un modelo térmico de simulación.This thesis is focused on addressing a more flexible, sustainable, efficient and reliable induction cooking approach from a power semiconductor device perspective. In this framework, this PhD Thesis has identified the following activities to cover such demands: In view of the growing interest for an effective power multiplexing in Induction Heating (IH) applications, improved and efficient Solid State Relays (SSRs) as an alternative to the electromechanical relays (EMRs) are deeply investigated. In this context, emerging Gallium Nitride (GaN) High‐Electron‐Mobility Transistors (GaN HEMTs) and Silicon Carbide (SiC) based devices are identified as potential candidates for the mentioned application, featuring several improved characteristics over EMRs. On the contrary, other solutions, which seemed to be very promising, resulted to suffer from anomalous behaviors; i.e. SJ MOSFETs are thoroughly analysed by electro‐thermal physical simulations at the device level. Additionally, the Short Circuit (SC) capability of power semiconductor devices employed or with potential to be used in IH appliances is also analysed. On the one hand, conventional IGBTs SC behavior is evaluated under different test conditions so that to obtain the trade‐off between ruggedness and low power losses. Moreover, ruggedness and reliability of several normally‐off 600‐650 V GaN HEMTs are deeply investigated by experimentation and physics‐based simulation. Finally, power losses calculation at die‐level is performed for resonant power converters by means of using Infrared Thermography (IRT). This method assists to determine, at the die‐level, the power losses and current distribution in IGBTs used in resonant soft‐switching power converters when functioning within or outside the Zero Voltage Switching (ZVS) condition. As a result, relevant information is obtained related to decreasing the power losses during commutation in the final application, and a thermal model is extracted for simulation purposes.<br /

Structural integrity assessment of cracked composite plate under aeroelastic loading by means of XFEM

In this paper, a novel procedure to analyse dynamic crack under aeroelastic loading by means of XFEM and Doublet Lattice Method (DLM) - FEM coupling is presented. The present work performed a new investigation on the crack propagation on the composite plate exerted by the unsteady aerodynamic loading during the cruise speed. The study on whether the failure due to instability, i.e., flutter, comes first or the crack propagates first until the structural failure is focused. The flutter speed is determined, and the maximum cruise speed is assessed based on the regulation guided by the FAR 23. The proposed method is used to solve the limitation in XFEM within Abaqus that only general static and implicit dynamic analysis can be performed. As the propagation of crack should be analyzed, the composite plate is assigned based on the energy release rate (ERR) of that type of material. The influence of a crack growth on a composite plate at cruise speed is shown in some details. Despite the fact that the crack is propagated at this speed, with initial crack ratio of 0.25, the crack has just grown slightly

Data-driven prognostics for critical electronic assemblies and electromechanical components

The industrial digitalisation enables the adoption of robust, data-driven maintenance strategies that increase safety and reliability of critical assets such as electronics. And yet, an implementation of data-driven methods which primarily address the industrialisation of diagnostic and prognostic strategies is opposed by various, application specific challenges. This thesis collates such restricting factors encountered within the oil and gas industry, in particular for the critical electrical systems and components in upstream deep drilling tools. A fleet-level, tuned machine learning approach is presented that classifies the operational state (no-failure/ failure) of downhole tool printed circuit board assemblies. It supports maintenance decision making under varying levels of failure costs and fleet reliability scenarios. Applied within a maintenance scheme it has the potential to minimise non-productive time while increasing operational reliability. Likewise, a tailored and efficient deep learning data pipeline is proposed for a component-level forecast of the end of life of electromagnetic relays. It is evaluated using high resolution life-cycle data which has been collected as a part of this thesis. In combination with a failure analysis, the proposed method improves the prognostics capabilities compared to traditional methods which have been proposed so far in order to assess the operational health of electromagnetic relays. Two case studies underpin the need for tailored prognostic methods in order to provide viable solutions that can de-risk deep drilling operations. In consequence, the proposed approaches alleviate the pressure on current maintenance strategies which can no longer meet the stringent reliability requirements of upstream assets

Advances and Technologies in High Voltage Power Systems Operation, Control, Protection and Security

The electrical demands in several countries around the world are increasing due to the huge energy requirements of prosperous economies and the human activities of modern life. In order to economically transfer electrical powers from the generation side to the demand side, these powers need to be transferred at high-voltage levels through suitable transmission systems and power substations. To this end, high-voltage transmission systems and power substations are in demand. Actually, they are at the heart of interconnected power systems, in which any faults might lead to unsuitable consequences, abnormal operation situations, security issues, and even power cuts and blackouts. In order to cope with the ever-increasing operation and control complexity and security in interconnected high-voltage power systems, new architectures, concepts, algorithms, and procedures are essential. This book aims to encourage researchers to address the technical issues and research gaps in high-voltage transmission systems and power substations in modern energy systems

Ship Lifecycle

In an effort to contribute to global efforts by addressing the marine pollution from various emission types, this Special Issue of Ship Lifecyle for Journal of Marine Science and Engineering was inspired to provide a comprehensive insight for naval architects, marine engineers, designers, shipyards, and ship-owners who strive to find optimal ways to survive in competitive markets by improving cycle time and the capacity to reduce design, production, and operation costs while pursuing zero emission. In this context, this Special Issue is devoted to providing insights into the latest research and technical developments on ship systems and operation with a life cycle point of view. The goal of this Special Issue is to bring together researchers from the whole marine and maritime community into a common forum to share cutting-edge research on cleaner shipping. It is strongly believed that such a joint effort will contribute to enhancing the sustainability of the marine and maritime activities. This Special Issue features six novel publications dedicated to this endeavor. First of all, as a proactive response to transitioning to cleaner marine fuel sources, numerous aspects of the excellence of fuel-cell based hybrid ships were demonstrated through four publications. In addition, two publications demonstrated the effectiveness of life cycle assessment (LCA) applicable to marine vessels

Maintenance Management of Wind Turbines

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements