Optimal Constant-Stress Accelerated Degradation Test Plans Using Nonlinear Generalized Wiener Process

Accelerated degradation test (ADT) has been widely used to assess highly reliable products’ lifetime. To conduct an ADT, an appropriate degradation model and test plan should be determined in advance. Although many historical studies have proposed quite a few models, there is still room for improvement. Hence we propose a Nonlinear Generalized Wiener Process (NGWP) model with consideration of the effects of stress level, product-to-product variability, and measurement errors for a higher estimation accuracy and a wider range of use. Then under the constraints of sample size, test duration, and test cost, the plans of constant-stress ADT (CSADT) with multiple stress levels based on the NGWP are designed by minimizing the asymptotic variance of the reliability estimation of the products under normal operation conditions. An optimization algorithm is developed to determine the optimal stress levels, the number of units allocated to each level, inspection frequency, and measurement times simultaneously. In addition, a comparison based on degradation data of LEDs is made to show better goodness-of-fit of the NGWP than that of other models. Finally, optimal two-level and three-level CSADT plans under various constraints and a detailed sensitivity analysis are demonstrated through examples in this paper

Some contributions to maintenance and accelerated degradation test under complex failure process

Ph.DDOCTOR OF PHILOSOPH

NASA/ASEE Summer Faculty Fellowship Program, 1990, Volume 1

The 1990 Johnson Space Center (JSC) NASA/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program was conducted by the University of Houston-University Park and JSC. A compilation of the final reports on the research projects are presented. The topics covered include: the Space Station; the Space Shuttle; exobiology; cell biology; culture techniques; control systems design; laser induced fluorescence; spacecraft reliability analysis; reduced gravity; biotechnology; microgravity applications; regenerative life support systems; imaging techniques; cardiovascular system; physiological effects; extravehicular mobility units; mathematical models; bioreactors; computerized simulation; microgravity simulation; and dynamic structural analysis

VI Workshop on Computational Data Analysis and Numerical Methods: Book of Abstracts

The VI Workshop on Computational Data Analysis and Numerical Methods (WCDANM) is going to be held on June 27-29, 2019, in the Department of Mathematics of the University of Beira Interior (UBI), Covilhã, Portugal and it is a unique opportunity to disseminate scientific research related to the areas of Mathematics in general, with particular relevance to the areas of Computational Data Analysis and Numerical Methods in theoretical and/or practical field, using new techniques, giving especial emphasis to applications in Medicine, Biology, Biotechnology, Engineering, Industry, Environmental Sciences, Finance, Insurance, Management and Administration. The meeting will provide a forum for discussion and debate of ideas with interest to the scientific community in general. With this meeting new scientific collaborations among colleagues, namely new collaborations in Masters and PhD projects are expected. The event is open to the entire scientific community (with or without communication/poster)

Precision Medicine: Viable Pathways to Address Existing Research Gaps

Precision Medicine (PM) seeks to customize medical treatments for patients based on measurable and identifiable characteristics. Unlike personalized medicine, this effort is not intended to result in tailored care for each patient. Instead, this effort seeks to improve overall care within the medical domain by shifting the focus from one-size-fits-all care to optimized care for specified subgroups. In order for the benefits of PM to be expeditiously realized, the diverse skills sets of the scientific community must be brought to bear on the problem. This research effort explores the intersection of quality engineering (QE) and healthcare to outline how existing methodologies within the QE field could support existing PM research goals. Specifically this work examines how to determine the value of patient characteristics for use in disease prediction models with select machine learning algorithms, proposes a method to incorporate patient risk into treatment decisions through the development of performance functions, and investigates the potential impact of incorrect assumptions on estimation methods used in optimization models

Degradation Modeling and Remaining Useful Life Estimation: From Statistical Signal Processing to Deep Learning Models

Aging critical infrastructures and valuable machineries together with recent catastrophic incidents such as the collapse of Morandi bridge, or the Gulf of Mexico oil spill disaster, call for an urgent quest to design advanced and innovative prognostic solutions, and efficiently incorporate multi-sensor streaming data sources for industrial development. Prognostic health management (PHM) is among the most critical disciplines that employs the advancement of the great interdependency between signal processing and machine learning techniques to form a key enabling technology to cope with maintenance development tasks of complex industrial and safety-critical systems. Recent advancements in predictive analytics have empowered the PHM paradigm to move from the traditional condition-based monitoring solutions and preventive maintenance programs to predictive maintenance to provide an early warning of failure, in several domains ranging from manufacturing and industrial systems to transportation and aerospace. The focus of the PHM is centered on two core dimensions; the first is taking into account the behavior and the evolution over time of a fault once it occurs, while the second one aims at estimating/predicting the remaining useful life (RUL) during which a device can perform its intended function. The first dimension is the degradation that is usually determined by a degradation model derived from measurements of critical parameters of relevance to the system. Developing an accurate model for the degradation process is a primary objective in prognosis and health management. Extensive research has been conducted to develop new theories and methodologies for degradation modeling and to accurately capture the degradation dynamics of a system. However, a unified degradation framework has yet not been developed due to: (i) structural uncertainties in the state dynamics of the system and (ii) the complex nature of the degradation process that is often non-linear and difficult to model statistically. Thus even for a single system, there is no consensus on the best degradation model. In this regard, this thesis tries to bridge this gap by proposing a general model that able to model the true degradation path without having any prior knowledge of the true degradation model of the system. Modeling and analysis of degradation behavior lead us to RUL estimation, which is the second dimension of the PHM and the second part of the thesis. The RUL is the main pillar of preventive maintenance, which is the time a machine is expected to work before requiring repair or replacement. Effective and accurate RUL estimation can avoid catastrophic failures, maximize operational availability, and consequently reduce maintenance costs. The RUL estimation is, therefore, of paramount importance and has gained significant attention for its importance to improve systems health management in complex fields including automotive, nuclear, chemical, and aerospace industries to name but a few. A vast number of researches related to different approaches to the concept of remaining useful life have been proposed, and they can be divided into three broad categories: (i) Physics-based; (ii) Data-driven, and; (iii) Hybrid approaches (multiple-model). Each category has its own limitations and issues, such as, hardly adapt to different prognostic applications, in the first one, and accuracy degradation issues, in the second one, because of the deviation of the learned models from the real behavior of the system. In addition to hardly sustain good generalization. Our thesis belongs to the third category, as it is the most promising category, in particular, the new hybrid models, on basis of two different architectures of deep neural networks, which have great potentials to tackle complex prognostic issues associated with systems with complex and unknown degradation processes

A Stochastic Approach to Measurement-Driven Damage Detection And Prognosis in Structural Health Monitoring

Damage detection and prognosis are integral to asset management of critical mechanical and civil engineering infrastructure. In practice, these two aspects are often decoupled, where the former is carried out independently using sensor data (e.g., vibrations), while the latter is undertaken based on reliability principles using life time failure data of the system or the component of interest. Only in a few studies damage detection results are extended to remaining useful life estimation, which is achieved by modeling the underlying degradation process using a surrogate measure of degradation. However, an integrated framework which undertakes damage detection, prognosis, and maintenance planning in a systematic way is lacking in the literature. Furthermore, the parameters of degradation model which are utilized for prognosis are often solely estimated using the degradation data obtained from the monitored unit, which represents the degradation of a specific unit, but ignores the general population trend. The main objectives of this thesis are three-fold: first, a mathematical framework using surrogate measure of degradation is developed to undertake the damage detection and prognosis in a single framework; next, the prior knowledge obtained from the historical failed units are integrated in model parameter estimation and residual useful life (RUL) updating of a monitored unit using a Bayesian approach; finally, the proposed degradation modeling framework is applied for maintenance planning of civil and industrial systems, specifically, for reinforced concrete beams and rolling element bearings. The initiation of a fault in these applications is often followed by a sudden change in the degradation path. The location of a change-point can be associated with a sudden loss of stiffness in the case of structural members, or fault initiation in the case of bearings. Hence, in this thesis, the task of change point location identification is thought of as being synonymous with damage or fault detection in the context of structural health monitoring. Furthermore, the change point results are used for two-phase degradation modeling, future degradation level prediction and subsequent RUL estimation. The model parameters are updated using a Bayesian approach, which systematically integrates the prior knowledge obtained from historical failure-time data with monitored data obtained from an in-situ unit. Once such a model is established, it is projected to a failure threshold, thereby allowing for RUL estimation and maintenance planning. Results from the numerical as well as actual field data shows that the proposed degradation modeling framework is good in performing these two tasks. It was also found that as more degradation data is utilized from the monitoring unit, the progressing fault is detected in a timely manner and the model parameters estimates and the end life predictions become more accurate