A Methodology to Quantify Cumulative Damage Function (CDF) for Integration Into an Object-Oriented Life Cycle Assessment (LCA)

Life Cycle Assessment (LCA) is one of the most widely used tools to determine the environmental impact of products and processes. One of the main concerns with LCA is the limited comparability of the results due to limitations in defining the functional unit. This affects goal and scope definition of the LCA studies. A result, an object-oriented framework for LCA that integrates functional analysis and systems engineering principles was developed. In this research a cumulative damage function (CDF) to quantify the life of components, subsystems and components was defined. However, the development of the methodology and underlying principles to develop the CDF was left for future work. The purpose of this thesis is to develop a framework to quantify CDF using the concepts of Remaining Useful Life (RUL), reliability analysis and failure analysis so that it can be easily integrated into the object-oriented LCA framework. This thesis will present a 5-step methodology to quantify the CDF and demonstrate its use and effectiveness by implementing it on a manual can opener and a coffee maker as examples of product systems

Prognostics and Health Monitoring for ECU Based on Piezoresistive Sensor Measurements

This dissertation presents a new approach to prognostics and health monitoring for automotive applications using a piezoresistive silicon stress sensor. The stress sensor is a component with promising performance for monitoring the condition of an electronic system, as it is able to measure stress values that can be directly related to the damage sustained by the system. The primary challenge in this study is to apply a stress sensor to system-level monitoring. To achieve this goal, this study firstly evaluates the uncertainties of measurement conducted with the sensor, and then the study develops a reliable solution for gathering data with a large number of sensors. After overcoming these preliminary challenges, the study forms a framework for monitoring an electronic system with a piezoresistive stress sensor. Following this, an approach to prognostics and health monitoring involving this sensor is established. Specifically, the study chooses to use a fusion approach, which includes both model-based and data-driven approaches to prognostics; such an approach minimizes the drawbacks of using these methods separately. As the first step, the physics of failure model for the investigated product is established. The process of physics of failure model development is supported by a detailed numerical analysis of the investigated product under both active and passive thermal loading. Accurate FEM modeling provides valuable insight into the product behavior and enables quantitative evaluation of loads acting in the considered design elements. Then, a real-time monitoring of the investigated product under given loading conditions is realized to enable the system to estimate the remaining useful life based on the existing model. However, the load in the design element may abruptly change when delamination occurs. A developed data-driven approach focuses on delamination detection based on a monitoring signal. The data driven methodology utilizes statistical pattern recognition methods in order to ensure damage detection in an automatic and reliable manner. Finally, a way to combine the developed physics-of-failure and data-driven approaches is proposed, thus creating fusion approach to prognostics and health monitoring based on piezoresistive stress sensor measurements

Prognostics and Health Management of Electronics by Utilizing Environmental and Usage Loads

Prognostics and health management (PHM) is a method that permits the reliability of a system to be evaluated in its actual application conditions. Thus by determining the advent of failure, procedures can be developed to mitigate, manage and maintain the system. Since, electronic systems control most systems today and their reliability is usually critical for system reliability, PHM techniques are needed for electronics. To enable prognostics, a methodology was developed to extract load-parameters required for damage assessment from irregular time-load data. As a part of the methodology an algorithm that extracts cyclic range and means, ramp-rates, dwell-times, dwell-loads and correlation between load parameters was developed. The algorithm enables significant reduction of the time-load data without compromising features that are essential for damage estimation. The load-parameters are stored in bins with a-priori calculated (optimal) bin-width. The binned data is then used with Gaussian kernel function for density estimation of the load-parameter for use in damage assessment and prognostics. The method was shown to accurately extract the desired load-parameters and enable condensed storage of load histories, thus improving resource efficiency of the sensor nodes. An approach was developed to assess the impact of uncertainties in measurement, model-input, and damage-models on prognostics. The approach utilizes sensitivity analysis to identify the dominant input variables that influence the model-output, and uses the distribution of measured load-parameters and input variables in a Monte-Carlo simulation to provide a distribution of accumulated damage. Using regression analysis of the accumulated damage distributions, the remaining life is then predicted with confidence intervals. The proposed method was demonstrated using an experimental setup for predicting interconnect failures on electronic board subjected to field conditions. A failure precursor based approach was developed for remaining life prognostics by analyzing resistance data in conjunction with usage temperature loads. Using the data from the PHM experiment, a model was developed to estimate the resistance based on measured temperature values. The difference between actual and estimated resistance value in time-domain were analyzed to predict the onset and progress of interconnect degradation. Remaining life was predicted by trending several features including mean-peaks, kurtosis, and 95% cumulative-values of the resistance-drift distributions

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

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

A condition monitoring tool based on a FMECA and FMMEA combined approach in Oil&Gas applications

A new approach that combines up both FMECA and FMMEA techniques has been analyzed and apply in the Oil&Gas field. This method guarantees its efficiency along the product/system development cycle and assures a proper maintenance scheduling according to the current condition of the product/system