Thermal Analysis of Power Transformer

A new approach for predicting the reliability indices based on the numerical analysis of nonuniform temperature fields of power transformer is reported. The failure rates of a power transformer in a real thermal mode of device under natural convection were compared with statistical data. The necessity of unsteady temperature field consideration was shown to enchance the reliability prediction

Product Component Genealogy Modeling and Field‐failure Prediction

Many industrial products consist of multiple components that are necessary for system operation. There is an abundance of literature on modeling the lifetime of such components through competing risks models. During the life‐cycle of a product, it is common for there to be incremental design changes to improve reliability, to reduce costs, or due to changes in availability of certain part numbers. These changes can affect product reliability but are often ignored in system lifetime modeling. By incorporating this information about changes in part numbers over time (information that is readily available in most production databases), better accuracy can be achieved in predicting time to failure, thus yielding more accurate field‐failure predictions. This paper presents methods for estimating parameters and predictions for this generational model and a comparison with existing methods through the use of simulation. Our results indicate that the generational model has important practical advantages and outperforms the existing methods in predicting field failures

A GENERIC RELIABILITY ANALYSIS AND DESIGN FRAMEWORK WITH RANDOM PARAMETER, FIELD, AND PROCESS VARIABLES

This dissertation aims at developing a generic reliability analysis and design framework that enables reliability prediction and design improvement with random parameter, field, and process variables. The capability of this framework is further improved by predicting and managing reliability even with a dearth of data that can be used to characterize random variables. To accomplish the research goal, three research thrusts are set forth. First, advanced techniques are developed to characterize the random field or process. The fundamental idea of these techniques is to model the random field or process with a set of important field signatures and random variables. These techniques enable the use of random parameter, field, and process variables for reliability analysis and design even with a dearth of data. Second, a generic reliability analysis framework is proposed to accurately assess system reliability in the presence of random parameter, field, and process variables. An advanced probability analysis technique, the Eigenvector Dimension Reduction (EDR) method, is developed by integrating the Dimension Reduction (DR) method with three proposed improvements: 1) an eigenvector sampling approach to obtain statistically independent samples over a random space; 2) a Stepwise Moving Least Square (SMLS) method to accurately approximate system responses over a random space; and 3) a Probability Density Function (PDF) generation method to accurately approximate the PDF of system responses for reliability analysis. Third, a generic Reliability-Based Design Optimization (RBDO) framework is developed to solve engineering design problems with random parameter, field, and process variables. This design framework incorporates the EDR method into RBDO. To illustrate the effectiveness of the developed framework, many numerical and engineering examples are employed to conduct the reliability analysis and RBDO with random parameter, field, and process variables. This dissertation demonstrates that the developed framework is very accurate and efficient for the reliability analysis and RBDO of engineering products and processes

Evidential Networks for Evaluating Predictive Reliability of Mechatronics Systems under Epistemic Uncertainties

In reliability predicting field, the probabilistic approaches are based on data relating to the components which can be precisely known and validated by the return of experience REX, but in the case of complex systems with high-reliability precision such as mechatronic systems, uncertainties are inevitable and must be considered in order to predict with a degree of confidence the evaluated reliability. In this paper, firstly we present a brief review of the non-probabilistic approaches. Thereafter we present our methodology for assessing the reliability of the mechatronic system by taking into account the epistemic uncertainties (uncertainties in the reliability model and uncertainties in the reliability parameters) considered as a dynamic hybrid system and characterized by the existence of multi-domain interaction between its failed components. The key point in this study is to use an Evidential Network “EN” based on belief functions and the dynamic Bayesian network. Finally, an application is developed to illustrate the interest of the proposed methodology

Spatial-temporal fractions verification for high-resolution ensemble forecasts

Experiments with two ensemble systems of the resolutions of 10 km (MF10km) and 2 km (MF2km) were designed to examine the value of cloud-resolving ensemble forecast in predicting small spatiotemporal-scale precipitation. Since the verification was performed on short-term precipitation at high resolution, uncertainties from small-scale processes caused the traditional verification methods inconsistent with the subjective evaluation. An extended verification method based on the Fractions Skill Score (FSS) was introduced to account for these uncertainties. The main idea is to extend the concept of spatial neighborhood in FSS to the time and ensemble dimension. The extension was carried out by recognizing that even if ensemble forecast is used, small-scale variability still exists in forecasts and influences verification results. In addition to FSS, the neighborhood concept was also incorporated into reliability diagrams and relative operating characteristics to verify the reliability and resolution of two systems. The extension of FSS in time dimension demonstrates the important role of temporal scales in short-term precipitation verification at small spatial scales. The extension of FSS in ensemble space is called ensemble FSS, which is a good representative of FSS in ensemble forecast in comparison with FSS of ensemble mean. The verification results show that MF2km outperforms MF10km in heavy rain forecasts. In contrast, MF10km was slightly better than MF2km in predicting light rain, suggesting that the horizontal resolution of 2 km is not necessarily enough to completely resolve convective cells

Concurrent Validity and Reliability of the Vertical Jump and Standing Broad Jump Tests in Youth

Muscular power is an important component of fitness with implications for bone health, explosiveness in movements, and predicting long-term health outcomes. However, the literature is scarce concerning commonly used muscular power field tests among youth, including vertical jump (VJ) and standing broad jump (SBJ). PURPOSE: To investigate the relationship between VJ and SBJ. METHODS: Approximately 540 students (9-14 years of age) in grades 4-8 participated in the testing of the VJ and SBJ. Pearson correlations were used to evaluate relationships between jump variables and intra-class correlations (ICC) were used to examine the consistency of the relationship between the VJ and SBJ. RESULTS: VJ had a positive and moderate-to-strong relationship with SBJ (r = 0.74), p \u3c 0.05. ICC analyses demonstrated VJ had poor consistency (ICC = 0.36, p \u3c 0.05) with SBJ. Regression analyses showed an r2 of 0.549 when predicting VJ from SBJ. The r2 was 0.576 when sex, age, and BMI percentile were accounted for, all p \u3c 0.05. CONCLUSIONS: Pearson correlations show the VJ has a positive and strong relationship with SBJ. The VJ displays moderate reliability with SBJ. While each are used as field assessments of lower body power in youth, each contributes unique variance during assessment. Further investigation is needed to better determine this unexplained variance.KEYWORDS: Vertical Jump, Standing Broad Jump, youth, reliability, consistency, validity

Sutte Indicator: an approach to predict the direction of stock market movements

The purpose of this research is to apply technical analysis of Sutte Indicator in stock trading which will assist in the investment decision making process i.e. buying or selling shares. This research takes data of "A" on the Indonesia Stock Exchange(IDX or BEI) 29 November 2006 until 20 September 2016 period. To see the performance of Sutte Indicator, other technical analysis are used as a comparison, Simple Moving Average (SMA) and Moving Average Convergence/Divergence (MACD). To see a comparison of the level of reliability prediction, the stock data were compared using the mean absolute deviation (MAD), mean of square error (MSE), and mean absolute percentage error (MAPE). The result of this research is that Sutte Indicator can be used as a reference in predicting stock movements, and if it is compared to other indicator methods (SMA and MACD) via MAD, MSE, and MAPE, the Sutte Indicator has a better level of reliability