It provides the information related to preventing, detect, and correct the defects of the reliability design. Quantitative reliability assessment is the task of the reliability data analysis. These data may be in the form of numbers, graphics, symbols, texts and curves. Reliability data refers to the variety of data that describe the reliability of system or component during its operation. The reliability engineering includes the equipment failure data processing, quantitative assessment of system reliability and maintenance, etc. Reliability is an important issue affecting each stage of the life cycle ranging from birth to death of a product or a system. Ma Yingfei Zhang Zhijian Zhang Min Zheng Gangyang International Nuclear Information System (INIS)
RELIASOFT WEIBULL CRACK CRACK
That is, how many times can we repeat that 1g to 1.679 g cycle until a crack will initiate.Weibull distribution in reliability data analysis in nuclear power plant The result is a contour plot of damage or, inversely, life. It then calculates the damage associated with that change in stress by looking at the fatigue curve and then calculates how many of those cycles the part can withstand before a crack is predicted to initiate for every single node in the model. DesignLife uses this information to calculate the change in stress due to the change in acceleration on every single node in the model. Therefore, our cycle is defined as going from 1 g to 1.679 g.
RELIASOFT WEIBULL CRACK PLUS
In this case, we will define it as going from nominal to the nominal plus the additional acceleration the user imparts onto the hinge. In order to perform the fatigue analysis in DesignLife, we need to define the fatigue cycle. In the case of the B50 loading, the average user causes an acceleration of 0.679 g above nominal. Our nominal stress distribution represents the stress of the part under no additional or external user-input loading. Let’s start by analyzing the B50 loading. In this example, recall that we’ve been asked to design a hinge that can survive 1,000,000 cycles at the “average” or B50 loading of 0.679 g, and 75,000 cycles at the high-end user level, or B90 loading of 1.360 g. Once the static stress distribution is obtained, the results file, along with the B50 and B90 usage values obtained from the analysis performed in Weibull++, can be exported to DesignLife to assess the fatigue life of the part. The second step will involve applying those levels within DesignLife to a virtual model of our product to obtain life estimates for both the average user (50 TH percentile) and the high-end user (90 th percentile). The first step will involve fitting a distribution to existing customer usage data to obtain the 50 th and 90 th percentile usage levels. In this article, we will combine the analysis capabilities of ReliaSoft Weibull++ and nCode DesignLife to perform a two-step analysis to estimate product life. In some cases, usage estimation is sufficient, however, collecting actual customer usage data allows for more accurate predictions. In order to meet these requirements, it is important to understand how customers use the product. As cost effectiveness and time to market have become higher priorities during the developmental process, more and more companies are turning to Computer Aided Engineering (CAE) to achieve these goals while ensuring that next generation products continue to meet customer expectations.
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