Generalized integrated importance measure for system performance evaluation: application to a propeller plane system

The integrated importance measure (IIM) evaluates the rate of system performance change due to a component changing from one state to another. The IIM simply considers the scenarios where the transition rate of a component from one state to another is constant. This may contradict the assumption of the degradation, based on which system performance is degrading and therefore the transition rate may be increasing over time. The Weibull distribution describes the life of a component, which has been used in many different engineering applications to model complex data sets. This paper extends the IIM to a new importance measure that considers the scenarios where the transition rate of a component degrading from one state to another is a time-dependent function under the Weibull distribution. It considers the conditional probability distribution of a component sojourning at a state is the Weibull distribution, given the next state that component will jump to. The research on the new importance measure can identify the most important component during three different time periods of the system lifetime, which is corresponding to the characteristics of Weibull distributions. For illustration, the paper then derives some probabilistic properties and applies the extended importance measure to a real-world example (i.e., a propeller plane system)

Optimization of condition-based maintenance strategy prediction for aging automotive industrial equipment using FMEA

Abstract: Maintenance plays a highly important role in achieving production targets and system performance. Electromechanical equipment and facility infrastructure within motor manufacturing industries are expected to perform at optimal efficiency during the operational phase of production. A major problem in the automotive production plan from motor industry statistics is associated with unexpected downtime, which is largely linked to aging equipment. During production downtime, much time is lost to fault finding, repairs, and replacement of faulty components within production lines. This transforms into low throughput in production, and performance gradually declines during the operational life cycle of the equipment. This paper presents an approach taken to prevent such instances in the automotive manufacturing industry, which considers an optimized condition-based maintenance approach to predict the condition of each component and assembly line using Failure-Mode-and-Effect-Analysis (FMEA). The condition-based performance level prediction is designed to help in formulating maintenance schedules and strategies that eliminate unplanned downtimes

Analysis of the causes and level of maintenance for enterprise systems in construction companies

A construction company without a similar information technology (IT) system in the past has insufficient historical data to use for investment decision-making of IT system. An estimation of maintenance costs is especially more uncertain than the initial investment costs, and the uncertainty is greater when the IT system is used over a long period, such as an enterprise system (ES). This study proposes estimation criteria for the maintenance costs of an ES for an accurate investment decision. First, the causes of maintenance are determined, and the level of maintenance analyzed. Then, the result is compared with a general trend of maintenance incidence (bathtub curve) that is widely used as reference criteria to estimate maintenance cost. The level of maintenance was high during the early stage but steadily decreased in the middle and end stage because high-cost maintenance activities were postponed with the approach of the time in which the ES was being restructured. This trend is different from the bathtub curve that increases again during the end stage. Thus, when a maintenance contract is negotiated, the maintenance level that affects maintenance cost should be considered as well as the incidence of maintenance

On Methods of Estimation for the Type II Discrete Weibull Distribution

In this paper, we describe and analyze several methods of estimation for the type II discrete Weibull distribution, outlining their applicability and properties, assessing and comparing their performance via intensive Monte Carlo simulation experiments. We consider the standard maximum likelihood method, a method of proportion, and two variants of the least-squares method. The type II discrete Weibull distribution can be used in reliability engineering for modeling count data or discrete lifetimes and its use is theoretically motivated by its capability of modeling either bounded or unbounded support, and either increasing or decreasing failure rate. Statistical analyses of real datasets are presented to show the capability of the distribution in fitting reliability data and illustrate the application of the proposed inferential techniques

Modelling of an insurance premium through the application of actuarial methods, failure theory and Black-Scholes in the health in Colombia

La prima de la tarifación en un seguro para el sector salud está influenciada por la siniestralidad de sus suscriptores, lo que genera altos niveles de fluctuación e incertidumbre. El objetivo de esta investigación es la aplicación de los modelos actuariales riesgo individual, riesgo colectivo y modelo de credibilidad, junto con la aplicación del modelo tecnológico de tasa de falla y el modelo de opciones financieras de Black-Scholes como herramientas de estimación de la prima de la tarifación para la industria aseguradora y de la salud en Colombia. A partir de las reclamaciones y de los costos totales de los siniestros históricos se aplican los modelos que permita asegurar primas óptimas para una cobertura a las pérdidas agregadas de los siniestros. Al final, se comparan dichos modelos y se aproxima a una definición de un método óptimo. La importancia de la investigación radica en el alto compromiso, responsabilidad e incidencia financiera de gestionar y mitigar el impacto del riesgo actuarial, planteando nuevas metodologías mediante un nivel de estimación óptima en las primas para certificar un correcto funcionamiento a las entidades del sector en temas de costos, sostenibilidad y cumplimiento al servicio en el sector.The pricing’s premium in an insurance for the health sector is influenced by the claims ratio of its subscribers, which generates high levels of fluctuation and uncertainty. The objective of this research is the application of the actuarial individual risk models, collective risk and credibility model, together with the application of the failure rate technological model and the Black-Scholes financial options model as tools for estimating pricing’s premium for the insurance and health industry in Colombia. Based on the claims and the total costs of the historical claims, the models are applied to ensure optimal premiums for coverage of the aggregate losses of the claims. In the end, comparing these models and approaching a definition of an optimal method. The importance of the research settles in the high commitment, responsibility and financial impact of managing and mitigating the impact of actuarial risk, proposing new methodologies through an optimal estimation level in premiums to certify proper functioning of the sector entities in matters of costs, sustainability and service compliance in the sector.Universidad Pablo de Olavid

Derivation of extreme non-Gaussian stochastic offshore structural responses using finite memory nonlinear system

For offshore structural design, the load due to wind-generated random waves is usually the most important source of loading. A nonlinear wave analysis is recommended to represent a realistic ocean wave for an accurate prediction of extreme offshore structural response. Nevertheless, the contribution of nonlinearity especially due to the wave-wave interaction leads to a complex solution. In fact, the random wave load itself experienced a nonlinearity due to the drag component of Morison’s load, the effect of load intermittency around the member in the splash zone, and the presence of current; which result in a non-Gaussian offshore structural response. The most accurate and versatile method for predicting the statistical properties of extreme responses on a subjected load is the Monte Carlo time simulation method, which can account for all sorts of nonlinearities without introducing any approximations. However, it is computationally very demanding due to its complex procedure in simulating the structural response as reliable results require a very large number of simulations. Therefore, a simple method using finite-memory nonlinear system (FMNS) has been introduced by previous researchers and is proven to improve the efficiency of evaluating offshore structural responses without sacrificing its accuracy. The method is, however, only applicable based on the linear wave analysis. Hence, by taking advantage of the efficiency of FMNS method, a new model needs to be developed by integrating the FMNS method with a nonlinear wave analysis for a more reliable result. It is the derivation of non-Gaussian stochastic offshore structural response using finite-memory nonlinear system, known as FMNSNL (subscript NL indicates nonlinear). In the model development process, the surface elevation is generated first according to a nonlinear wave analysis with at least second-order wave. Then, two components of system are introduced, in which the first component enabled the transformation from a reference surface elevation to a second-order linearized quasi-static responses, while the second component involved the development of nonlinear function based on the relationship of second-order nonlinear and linearized quasi-static responses. Four models have been developed, in which the best model can produce an output of approximate values of second-order nonlinear quasi-static response that is very close to its corresponding values obtained using Monte Carlo time simulation method and will then be used for further examination. Based on the correlation coefficient between those two methods, the best relationship with value of 0.9783 was obtained by model 4 on the drag-induced quasi-static base shear for high significant wave height. The procedure of model development based on those two components is examined for all sea state conditions with Hs = 5, 10 and 15 m, and with the presence of current, U̅= 0 m/sec and ±0.90 m/sec. As a result, the relationship of model 4 fits the data better for all cases. It should be noted that this investigation of in-service analysis is carried out only for quasi-static structure by neglecting the dynamic effect. Based on the result of the short-term analysis, FMNSNL method provided a good accuracy of prediction of 100-year responses compared with the corresponding prediction using Monte Carlo time simulation method for all cases. A comparison has been made according to the ratio of prediction between FMNSNL and Monte Carlo time simulation methods. Overall, the accuracy level achieved by FMNSNL method is in the range of 82% to 99.8%, in which the accuracy level improved with the presence of positive current and vice versa with negative current. The same conclusion is valid for long-term analysis since the accuracy performance of FMNSNL followed exactly as previous analysis for short-term distribution. Without the presence of current along the wave propagation, the accuracy level of FMNSNL method is in the range of 80% to 96%. If there exist a current with the same direction of the wave (positive current), the accuracy improved with an increment of 1% to 7%. However, the opposite direction of current (negative current) provided a severe impact on its prediction with a reduction of 1% to 18% of accuracy. Hence, the method of FMNSNL can then be used with an excellent efficiency and accuracy to determine the extreme offshore structural response. With that, the offshore structure is towards optimization that leads to cost reduction and preservation of safety

Mathematical Modeling and Simulation in Mechanics and Dynamic Systems

The present book contains the 16 papers accepted and published in the Special Issue “Mathematical Modeling and Simulation in Mechanics and Dynamic Systems” of the MDPI “Mathematics” journal, which cover a wide range of topics connected to the theory and applications of Modeling and Simulation of Dynamic Systems in different field. These topics include, among others, methods to model and simulate mechanical system in real engineering. It is hopped that the book will find interest and be useful for those working in the area of Modeling and Simulation of the Dynamic Systems, as well as for those with the proper mathematical background and willing to become familiar with recent advances in Dynamic Systems, which has nowadays entered almost all sectors of human life and activity