Risk-Based Reliability Engineering Enables Improved Rotary-Steerable-System Performance and Defines New Industry Performance Metric

Author(s):  
Paul Anthony Wand ◽  
Matt Bible ◽  
Ian Silvester
2020 ◽  
Vol 20 (203) ◽  
Author(s):  
Martino Pelli ◽  
Jeanne Tschopp ◽  
Natalia Bezmaternykh ◽  
Kodjovi Eklou

This paper examines the response of firms to capital destruction, using a new measure of firm exposure to tropical storms as a negative exogenous shock on firms’ capital stock. Drawing on a panel of Indian manufacturing firms between 1995 and 2006, we establish that, depending on their strength, storms destroy up to 75.3% of the fixed assets of the median firm (in terms of its productivity and industry performance). We quantify the response of firm sales within and across industries and find effects akin to Schumpeterian creative destruction, where surviving firms build back better. Within an industry, the sales of less productive firms decrease disproportionately more, while across industries capital destruction leads to a shift in sales towards more performing industries. This build-back better effect is driven by firms active in multiple industries and, to a large extent, by shifts in the firm-level production mix within a firm’s active set of industries. Finally, while there is no evidence that firms adjust by investing in new industry lines, firms tend to abandon production in industries that exhibit lower comparative advantage.


Author(s):  
Miwako Tsuji ◽  
William T. C. Kramer ◽  
Jean-Christophe Weill ◽  
Jean-Philippe Nominé ◽  
Mitsuhisa Sato

AbstractBecause of the increasing complexities of systems and applications, the performance of many traditional HPC benchmarks, such as HPL or HPCG, no longer correlates strongly with the actual performance of real applications. To address the discrepancy between simple benchmarks and real applications, and to better understand the application performance of systems, some metrics use a set of either real applications or mini applications. In particular, the Sustained System Performance (SSP) metric Kramer et al. (The NERSC sustained system performance (SSP) metric. Tech Rep LBNL-58868, 2005), which indicates the expected throughput of different applications executing with different datasets, is widely used. Whereas such a metric should lead to direct insights on the actual performance of real applications, sometimes more effort is necessary to port and evaluate complex applications. In this study, to obtain the approximate performance of SSP representing real applications, without running real applications, we propose a metric called the Simplified Sustained System Performance (SSSP) metric, which is computed based on several benchmark scores and their respective weighting factors, and we construct a method evaluating the SSSP metric of a system. The weighting factors are obtained by minimizing the gap between the SSP and SSSP scores based on a small set of reference systems. We evaluated the applicability of the SSSP method using eight systems and demonstrated that our proposed SSSP metrics produce appropriate performance projections of the SSP metrics of these systems, even when we adopted a simple method for computing the weighting factors. Additionally, the robustness of our SSSP metric was confirmed via computation of the weighting factors based on a smaller set of reference systems and computation of the SSSP metrics of other systems.


2013 ◽  
Vol 718-720 ◽  
pp. 2125-2130 ◽  
Author(s):  
Xiao Qun Sun ◽  
Qiang Wu ◽  
Xu Wen Li ◽  
Xin Zheng

This paper introduces the performance metric of DSP parallel processing system and presents a model of coarse-grained speedup of DSP parallel processing structure. Quantitative research is done according to the system performance index and target program features. This study simulates and analyzes different communication protocols and different influences of different degrees of parallelism on the parallel processing structure performances. Optimization direction of parallel processing system is put forward.


2009 ◽  
Vol 84 (4) ◽  
pp. 1119-1143 ◽  
Author(s):  
Michael S. Cichello ◽  
C. Edward Fee ◽  
Charles J. Hadlock ◽  
Ramana Sonti

ABSTRACT: We study turnover and promotions of division managers in multidivisional firms. Turnover is negatively related to divisional accounting performance, positively related to industry performance, but not significantly related to firm performance or the performance of other divisions. Consistent with tournament theory, promotions are significantly related to whether one division is performing better than others, but are not significantly related to the magnitude of any performance difference. A simple performance metric, divisional ROA, appears more closely related to job allocation decisions than several alternatives. Our evidence is consistent with the hypothesis that accounting information is used by firms when evaluating managerial personnel.


Author(s):  
J Reeves ◽  
R Remenyte-Prescott ◽  
J Andrews

As technology advances, modern systems are becoming increasingly complex, consisting of large numbers of components, and therefore large numbers of potential component failures. These component failures can result in reduced system performance, or even system failure. The system performance can be monitored using sensors, which can help to detect faults and diagnose failures present in the system. However, sensors increase the weight and cost of the system, and therefore, the number of sensors may be limited, and only the sensors that provide the most useful system information should be selected. In this article, a novel sensor performance metric is introduced. This performance metric is used in a sensor selection process, where the sensors are chosen based on their ability to detect faults and diagnose failures of components, as well as the effect the component failures have on system performance. The proposed performance metric is a suitable solution for the selection of sensors for fault diagnostics. In order to model the outputs that would be measured by the sensors, a Bayesian Belief Network is developed. Sensors are selected using the performance metric, and sensor readings can be introduced in the Bayesian Belief Network. The results of the Bayesian Belief Network can then be used to rank the component failures in order of likelihood of causing the sensor readings. To illustrate the proposed approach, a simple flow system is used in this article.


Author(s):  
Fangyu Liu ◽  
Hongyan Dui ◽  
Ziyue Li

With the introduction of reliability engineering, electrical power system reliability has become an important basis for decision-making in the power industry. Two operation cases of electrical power systems are considered in this article. When the system is in an ordinary way, the influence between two system components will affect the importance measure of one component. When some component is in maintenance, preventive maintenance for working components and corrective maintenance for failed components can be executed simultaneously to enhance electrical power system performance. In view of the above two cases, two importance measures are proposed to effectively guide the preventive maintenance, aiming to improve the system performance within a limited budget. Reliability analysis procedure and methods applied toward the two importance measures are then developed and illustrated with the analysis on a Dual Element Spot Network system with double power supplies and double loads. Finally, a strategy for preventive maintenance is proposed by ranking the importance of these components.


1960 ◽  
Author(s):  
S. Seidenstein ◽  
R. Chernikoff ◽  
F. V. Taylor

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