Reliability Estimation of Rotary Lip Seal in Aircraft Utility System Based on Time-Varying Dependence Degradation Model and Its Experimental Validation

With several attractive properties, rotary lip seals are widely used in aircraft utility system, and their reliability estimation has drawn more and more attention. This work proposes a reliability estimation approach based on time-varying dependence analysis. The dependence between the two performance indicators of rotary lip seals, namely leakage rate and friction torque, is modeled by time-varying copula function with polynomial to denote the time-varying parameters, and an efficient copula selection approach is utilized to select the optimal copula function. Parameter estimation is carried out based on a Bayesian method and the reliability during the whole lifetime is calculated based on a Monte Carlo method. Degradation test for rotary lip seal is conducted and the proposed model is validated by test data. The optimal copula function and optimal order of polynomial are determined based on test data. Results show that this model is effective in estimating the reliability of rotary lip seals and can achieve a better goodness of fit

Reliability Analysis of Electrotechnical Devices

This is a book on the practical approaches of reliability to electrotechnical devices and systems. It includes the electromagnetic effect, radiation effect, environmental effect, and the impact of the manufacturing process on electronic materials, devices, and boards

Reliability demonstration of a multi-component Weibull system under zero-failure assumption.

This dissertation is focused on finding lower confidence limits for the reliability of systems consisting of Wei bull components when the reliability demonstration testing (RDT) is conducted with zero failures. The usual methods for the parameter estimation of the underlying reliability functions like maximum likelihood estimator (MLE) or mean squares estimator (MSE) cannot be applied if the test data contains no failures. For single items there exists a methodology to calculate the lower confidence limit (LCL) of reliability for a certain confidence level. But there is no comparable method for systems. This dissertation provides a literature review on specific topics within the wide area of reliability engineering. Based on this and additional research work, a first theorem for the LCL of system reliability of systems with Weibull components is formulated. It can be applied if testing is conducted with zero observed failures. This theorem is unique in that it allows for different Wei bull shape parameters for components in the system. The model can also be applied if each component has been exposed to different test durations. This can result from accelerated life testing (AL T) with test procedures that have different acceleration factors for the various failure modes or components respectively. A second theorem for Ex -lifetime, derived from the first theorem, has been formulated as well. The first theorem on LCL of system reliability is firstly proven for systems with two components only. In the following the proof is extended towards the general case of n components. There is no limitation on the number of components n. The proof of the second theorem on Bx - lifetime is based on the first proof and utilizes the relation between Bx and reliability. The proven theorem is integrated into a model to analyze the sensitivity of the estimation of the Wei bull shape parameter p. This model is also applicable if the Weibull parameter is subject to either total uncertainty or of uncertainty within a defined range. The proven theorems can be utilized as the core of various models to optimize RDT plans in a way that the targets for the validation can be achieved most efficiently. The optimization can be conducted with respect to reliability, Bx -lifetime or validation cost. The respective optimization models are mixed-integer and highly non-linear and therefore very difficult to solve. Within this research work the software package LINGO™ was utilized to solve the models. There is a proposal included of how to implement the optimization models for RDT testing into the reliability process in order to iteratively optimize the RDT program based on failures occurred or changing boundary conditions and premises. The dissertation closes with the presentation of a methodology for the consideration of information about the customer usage for certain segments such as market share, annual mileage or component specific stress level for each segment. This methodology can be combined with the optimization models for RDT plans

Knowledge Discovery from Complex Event Time Data with Covariates

In particular engineering applications, such as reliability engineering, complex types of data are encountered which require novel methods of statistical analysis. Handling covariates properly while managing the missing values is a challenging task. These type of issues happen frequently in reliability data analysis. Specifically, accelerated life testing (ALT) data are usually conducted by exposing test units of a product to severer-than-normal conditions to expedite the failure process. The resulting lifetime and/or censoring data are often modeled by a probability distribution along with a life-stress relationship. However, if the probability distribution and life-stress relationship selected cannot adequately describe the underlying failure process, the resulting reliability prediction will be misleading. To seek new mathematical and statistical tools to facilitate the modeling of such data, a critical question to be asked is: Can we find a family of versatile probability distributions along with a general life-stress relationship to model complex lifetime data with covariates? In this dissertation, a more general method is proposed for modeling lifetime data with covariates. Reliability estimation based on complete failure-time data or failure-time data with certain types of censoring has been extensively studied in statistics and engineering. However, the actual failure times of individual components are usually unavailable in many applications. Instead, only aggregate failure-time data are collected by actual users due to technical and/or economic reasons. When dealing with such data for reliability estimation, practitioners often face challenges of selecting the underlying failure-time distributions and the corresponding statistical inference methods. So far, only the Exponential, Normal, Gamma and Inverse Gaussian (IG) distributions have been used in analyzing aggregate failure-time data because these distributions have closed-form expressions for such data. However, the limited choices of probability distributions cannot satisfy extensive needs in a variety of engineering applications. Phase-type (PH) distributions are robust and flexible in modeling failure-time data as they can mimic a large collection of probability distributions of nonnegative random variables arbitrarily closely by adjusting the model structures. In this paper, PH distributions are utilized, for the first time, in reliability estimation based on aggregate failure-time data. To this end, a maximum likelihood estimation (MLE) method and a Bayesian alternative are developed. For the MLE method, an expectation-maximization (EM) algorithm is developed to estimate the model parameters, and the corresponding Fisher information is used to construct the confidence intervals for the quantities of interest. For the Bayesian method, a procedure for performing point and interval estimation is also introduced. Several numerical examples show that the proposed PH-based reliability estimation methods are quite flexible and alleviate the burden of selecting a probability distribution when the underlying failure-time distribution is general or even unknown

Modeling multivariate degradation processes with time‐variant covariates and imperfect maintenance effects

International audienceThis article proposes two types of degradation models that are suitable for describing multivariate degrading systems subject to time‐variant covariates and imperfect maintenance activities. A multivariate Wiener process is constructed as a baseline model, on top of which two types of models are developed to meaningfully characterize the time‐variant covariates and imperfect maintenance effects. The underlying difference between the two models lies in the way of capturing the influences of covariates and maintenance: The first model reflects these impacts in the degradation rates/paths directly, whereas the second one describes the impacts by modifying the time scales governing the degradation processes. In each model, two particular imperfect maintenance models are presented, which differ in the extent of reduction in degradation level or virtual age. The two degradation models are then compared in certain special cases. The proposed multivariate degradation models pertain to complex industrial systems whose health deterioration can be characterized by multiple performance characteristics and can be altered or affected by maintenance activities and operating/environmental conditions

Decision-Making for Utility Scale Photovoltaic Systems: Probabilistic Risk Assessment Models for Corrosion of Structural Elements and a Material Selection Approach for Polymeric Components

abstract: The solar energy sector has been growing rapidly over the past decade. Growth in renewable electricity generation using photovoltaic (PV) systems is accompanied by an increased awareness of the fault conditions developing during the operational lifetime of these systems. While the annual energy losses caused by faults in PV systems could reach up to 18.9% of their total capacity, emerging technologies and models are driving for greater efficiency to assure the reliability of a product under its actual application. The objectives of this dissertation consist of (1) reviewing the state of the art and practice of prognostics and health management for the Direct Current (DC) side of photovoltaic systems; (2) assessing the corrosion of the driven posts supporting PV structures in utility scale plants; and (3) assessing the probabilistic risk associated with the failure of polymeric materials that are used in tracker and fixed tilt systems. As photovoltaic systems age under relatively harsh and changing environmental conditions, several potential fault conditions can develop during the operational lifetime including corrosion of supporting structures and failures of polymeric materials. The ability to accurately predict the remaining useful life of photovoltaic systems is critical for plants ‘continuous operation. This research contributes to the body of knowledge of PV systems reliability by: (1) developing a meta-model of the expected service life of mounting structures; (2) creating decision frameworks and tools to support practitioners in mitigating risks; (3) and supporting material selection for fielded and future photovoltaic systems. The newly developed frameworks were validated by a global solar company.Dissertation/ThesisDoctoral Dissertation Civil and Environmental Engineering 201