Computationally efficient, real-time, and embeddable prognostic techniques for power electronics

Power electronics are increasingly important in new generation vehicles as critical safety mechanical subsystems are being replaced with more electronic components. Hence, it is vital that the health of these power electronic components is monitored for safety and reliability on a platform. The aim of this paper is to develop a prognostic approach for predicting the remaining useful life of power electronic components. The developed algorithms must also be embeddable and computationally efficient to support on-board real-time decision making. Current state-of-the-art prognostic algorithms, notably those based on Markov models, are computationally intensive and not applicable to real-time embedded applications. In this paper, an isolated-gate bipolar transistor (IGBT) is used as a case study for prognostic development. The proposed approach is developed by analyzing failure mechanisms and statistics of IGBT degradation data obtained from an accelerated aging experiment. The approach explores various probability distributions for modeling discrete degradation profiles of the IGBT component. This allows the stochastic degradation model to be efficiently simulated, in this particular example ~1000 times more efficiently than Markov approaches

Multiobjective design optimization of IGBT power modules considering power cycling and thermal cycling

Insulated-gate bipolar transistor (IGBT) power modules find widespread use in numerous power conversion applications where their reliability is of significant concern. Standard IGBT modules are fabricated for general-purpose applications while little has been designed for bespoke applications. However, conventional design of IGBTs can be improved by the multiobjective optimization technique. This paper proposes a novel design method to consider die-attachment solder failures induced by short power cycling and baseplate solder fatigue induced by the thermal cycling which are among major failure mechanisms of IGBTs. Thermal resistance is calculated analytically and the plastic work design is obtained with a high-fidelity finite-element model, which has been validated experimentally. The objective of minimizing the plastic work and constrain functions is formulated by the surrogate model. The nondominated sorting genetic algorithm-II is used to search for the Pareto-optimal solutions and the best design. The result of this combination generates an effective approach to optimize the physical structure of power electronic modules, taking account of historical environmental and operational conditions in the field

Commercial users panel

The discussions of motives and requirements for telerobotics application demonstrated that, in many cases, lack of progress was a result not of limited opportunities but of inadequate mechanisms and resources for promoting opportunities. Support for this conclusion came from Telerobotics, Inc., one of the few companies devoted primarily to telerobot systems. They have produced units for such diverse applications as nuclear fusion research, particle accelerators, cryogenics, firefighting, marine biology/undersea systems and nuclear mobile robotics. Mr. Flatau offered evidence that telerobotics research is only rarely supported by the private sector and that it often presents a difficult market. Questions on the mechanisms contained within the NASA technology transfer process for promoting commercial opportunities were fielded by Ray Gilbert and Tom Walters. A few points deserve emphasis: (1) NASA/industry technology transfer occurs in both directions and NASA recognizes the opportunity to learn a great deal from industry in the fields of automation and robotics; (2) promotion of technology transfer projects takes a demand side approach, with requests to industry for specific problem identification. NASA then proposes possible solutions; and (3) comittment ofmotivated and technically qualified people on each end of a technology transfer is essential

Multistage quality control using machine learning in the automotive industry

Product dimensional variability is a crucial factor in the quality control of complex multistage manufacturing processes, where undetected defects can easily be propagated downstream. The recent advances in information technologies and consequently the increased volume of data that has become readily available provide an excellent opportunity for the development of automated defect detection approaches that are capable of extracting the implicit complex relationships in these multivariate data-rich environments. In this paper, several machine learning classifiers were trained and evaluated on varied metrics to predict dimensional defects in a real automotive multistage assembly line. The line encompasses two automated inspection stages with several human-operated assembly and pre-alignment stages in between. The results show that non-linear models like XGBoost and Random Forests are capable of modelling the complexity of such an environment, achieving a high true positive rate and showing promise for the improvement of existing quality control approaches, enabling defects and deviations to be addressed earlier and thus assist in reducing scrap and repair costs.info:eu-repo/semantics/publishedVersio

Data mining in manufacturing: a review based on the kind of knowledge

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques

A framework for real-time product quality monitoring system with consideration of process-induced variations

Department of Human and Systems EngineeringAs industrial technologies develop, the manufacturing industry is globally changing in more automated and complex manners, and the prediction of real-time product quality has become an essential issue. Although many of the physical manufacturing activities are getting more automated than ever, there still exist many uncovered parameters that, either directly or indirectly, affect the product quality. In many manufacturing sites, the quality tests in their processes still rely on few skilled operators and quality experts, which requires a lot of time and human efforts to manage the product quality issues. In this thesis, thus, a real-time/in-process quality monitoring system for small and medium size manufacturing environments is proposed to provide the data-driven product quality monitoring system framework. The proposed framework consists of a product quality ontology model for complex manufacturing supply chain environments, and a real-time quality prediction tool using the support vector machine (SVM) algorithm that enables the quality monitoring system to classify the product quality patterns from the in-process production data. Additionally, we propose a framework for analysis of the quality inspection results from the monitoring system with respect to quality costs, including inspection and warranty costs. In addition, this thesis establishes a relationship between the warranty cost and the severity of customer-perceived quality. Finally, we suggest a future work that a prescriptive product quality assessment concept using the Hidden Markov Models (HMM) that analyze and forecast possible future product quality problems using production data from manufacturing processes based on data flow analysis. Also, a door trim production data in an automotive company is illustrated to verify the proposed quality monitoring/prediction model.ope

Supply chain challenges in the semiconductor industry

Abstract. This thesis focuses on the challenges of semiconductor supply chain management from the perspective of industrial engineering. The aim of this thesis is to form an overview of the situation, reviewing the factors by which the current events can mostly be explained, and certain remedies presented. The work includes a literature review that discusses the semiconductor manufacturing process, the nature of cyclical markets, the characteristics of demand and supply shocks, and the concept of the bullwhip effect. The results of the literature and market analysis are combined with expert interviews to form a framework on which conclusions and recommendations will be built. The findings of this study point to divergent incentives and patterns of action, the combined effect of which exacerbates the shortage, partly consciously but also unknowingly. Cooperation and communication between retailers, manufacturers, logistics companies, and raw material producers in certain high-risk sectors of the supply chain will continue to play a key role in minimizing similar shocks in the future. The most effective methods are expanding information flows, as well as direct cooperation between different parts of the supply chain. This development is likely to be further supported in the future by continuous progress in information technology, the expansion of data management, and the improvement of analytics tools. The study provides an extensive and current overview of a historically unique situation, the long-term effects of which can only be speculated for the time being. Based on the findings, it is possible to make recommendations for organizations operating in the current or somewhat similar circumstances, as well as to create a sort of period piece from the thinking and choices of decision-makers during a crisis for subsequent research.Tilaus-toimitusketjun haasteet puolijohdeteollisuudessa. Tiivistelmä. Tämä diplomityö käsittelee puolijohteiden tilaus-toimitusketjujen haasteita tuotantotalouden näkökulmasta. Tavoitteena on muodostaa yleiskuva puolijohdepulasta kerraten tapahtumat, joilla ainakin suurin osa taustatekijöistä voidaan selittää ja esittää niiden varalta torjuntakeinoja. Työhön kuuluu kirjallisuuskatsaus, jossa käydään läpi puolijohteiden valmistusprosessi pääpiirteineen, syklisten markkinoiden luonne, kysyntä- ja tarjontashokkien piirteitä, sekä esitellään Piiskanisku-ilmiön käsite. Kirjallisuus- ja markkinakatsauksen tulokset yhdistetään lopulta asiantuntijahaastatteluihin ja muodostetaan viitekehys, jonka ympärille johtopäätökset ja suositukset rakennetaan. Työn tulokset osoittavat erisuuntaisiin toimintamalleihin, joiden yhteisvaikutus pahentaa pulaa osittain tietoisesti, mutta myös osapuolten tiedostamatta. Keskeisimmässä roolissa vastaavien kysyntä- ja tarjontashokkien minimoinnissa tulee jatkossa olemaan tietyillä toimitusketjujen kannalta riskipitoisilla aloilla toimivien tilaajien, tuottajien ja raaka-ainevalmistajien välinen yhteistyö. Menetelmistä tehokkaimpia on suora piiskaniskuilmiöön puuttuminen informaatiovirtoja tehostamalla ja ketjun osien välinen nopeampi kommunikaatio. Tätä kehityskulkua tulevat todennäköisesti jatkossa tukemaan myös tietotekninen kehitys, datanhallinnan laajentuminen ja analytiikan parantuminen. Tutkimus tarjoaa laajan ja ajankohtaisen yleiskatsauksen historiallisesti ainutlaatuiseen tilanteeseen, jonka pitkäaikaisia vaikutuksia voidaan toistaiseksi vain arvailla. Tulosten pohjalta voidaan laatia toimintasuosituksia nykyisissä tai nykyisiä joltakin osin vastaavissa olosuhteissa toimiville organisaatioille, sekä luoda myöhemmin ajankuvaa kriisin keskellä päätöksiä tekevien tahojen ajattelusta ja valinnoista

Prognostics and health management of power electronics

Prognostics and health management (PHM) is a major tool enabling systems to evaluate their reliability in real-time operation. Despite ground-breaking advances in most engineering and scientific disciplines during the past decades, reliability engineering has not seen significant breakthroughs or noticeable advances. Therefore, self-awareness of the embedded system is also often required in the sense that the system should be able to assess its own health state and failure records, and those of its main components, and take action appropriately. This thesis presents a radically new prognostics approach to reliable system design that will revolutionise complex power electronic systems with robust prognostics capability enhanced Insulated Gate Bipolar Transistors (IGBT) in applications where reliability is significantly challenging and critical. The IGBT is considered as one of the components that is mainly damaged in converters and experiences a number of failure mechanisms, such as bond wire lift off, die attached solder crack, loose gate control voltage, etc. The resulting effects mentioned are complex. For instance, solder crack growth results in increasing the IGBT’s thermal junction which becomes a source of heat turns to wire bond lift off. As a result, the indication of this failure can be seen often in increasing on-state resistance relating to the voltage drop between on-state collector-emitter. On the other hand, hot carrier injection is increased due to electrical stress. Additionally, IGBTs are components that mainly work under high stress, temperature and power consumptions due to the higher range of load that these devices need to switch. This accelerates the degradation mechanism in the power switches in discrete fashion till reaches failure state which fail after several hundred cycles. To this end, exploiting failure mechanism knowledge of IGBTs and identifying failure parameter indication are background information of developing failure model and prognostics algorithm to calculate remaining useful life (RUL) along with ±10% confidence bounds. A number of various prognostics models have been developed for forecasting time to failure of IGBTs and the performance of the presented estimation models has been evaluated based on two different evaluation metrics. The results show significant improvement in health monitoring capability for power switches.Furthermore, the reliability of the power switch was calculated and conducted to fully describe health state of the converter and reconfigure the control parameter using adaptive algorithm under degradation and load mission limitation. As a result, the life expectancy of devices has been increased. These all allow condition-monitoring facilities to minimise stress levels and predict future failure which greatly reduces the likelihood of power switch failures in the first place

NOVELTY DETECTION FOR PREDICTIVE MAINTENANCE

Since the advent of Industry 4. 0 significant research has been conducted to apply machine learning to the vast array of Internet of Things (IoT) data produced by Industrial Machines. One such topic is to Predictive Maintenance. Unlike some other machine learning domains such as NLP and computer vision, Predictive Maintenance is a relatively new area of focus. Most of the published work demonstrates the effectiveness of supervised classification for predictive maintenance. Some of the challenges highlighted in the literature are the cost and difficulty of obtaining labelled samples for training. Novelty detection is a branch of machine learning that after being trained on normal operations detects if new data comes from the same process or is different, eliminating the requirement to label data points. This thesis applies novelty detection to both a public data set and one that was specifically collected to demonstrate a its application to predictive maintenance. The Local Optimization Factor showed better performance than a One-Class SVM on the public data. It was then applied to data from a 3-D printer and was able to detect faults it had not been trained on showing a slight lift from a random classifier