scholarly journals Research on Reliability Evaluation Method of Aerospace Pyrotechnic Devices Based on Energy Measurement

2020 ◽  
Vol 10 (22) ◽  
pp. 8200
Author(s):  
Yubo Zhu ◽  
Jili Rong ◽  
Qianqiang Song ◽  
Zhipei Wu
Keyword(s):  
Evaluation Method ◽  
High Reliability ◽  
Reliability Evaluation ◽  
Small Sample ◽  
Performance Parameters ◽  
Measuring Devices ◽  
Reliability Method ◽  
The Cost ◽  
Sample Method ◽  
T Distribution

High reliability is the basic requirement of aerospace pyrotechnic devices. Traditional reliability evaluation methods require a lot of tests, which become too expensive; therefore, the small-sample evaluation method is needed to reduce the cost. Using energy as a performance parameter can better reflect the essence of the function of the pyrotechnic device compared to using force. Firstly, this article assumes that the strength obeys the normal distribution, and the stress is a constant; therefore, the reliability evaluation formula based on the t distribution is proposed. Then, taking the pin puller as the research object, four sets of energy measuring devices were developed so as to obtain its performance parameters. Finally, the evaluation results show that the pin puller has a high reliability of 0.9999999765 with a confidence level of 0.995. The reliability method proposed in this paper is a small-sample method for evaluating aerospace pyrotechnic devices, which can greatly reduce the cost of reliability evaluation. Moreover, the energy measuring devices developed in this paper can provide a new way of measuring performance parameters for piston-type pyrotechnic devices.

A Study on Wear Reliability of Mechanism Based on Performance Degradation

2012 ◽  
Vol 452-453 ◽  
pp. 1149-1153
Author(s):  
Jing Cai ◽  
Yi Ming Xu ◽  
Hai Bin Lin
Keyword(s):  
High Reliability ◽  
Performance Degradation ◽  
Small Sample ◽  
Performance Parameters ◽  
Degradation Failure ◽  
Long Life ◽  
Failure Probabilities

Because mechanism always has the characteristic of high-reliability, long-life and small-sample, performance degradation theory is adopted to study the wear reliability of mechanism. So, firstly the degradation failure standard of mechanism is analyzed; then the distribution of performance parameters is verified by the K-S test; Furthermore, the failure probabilities are calculated based on degradation failure standard and distribution of performance parameters, in order to confirm the wear reliability distribution of mechanism; finally, an example is given to show the validity of this method.

scholarly journals Reliability Evaluation for the Running State of the Manufacturing System Based on Poor Information

2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Xintao Xia ◽  
Wenhuan Zhu ◽  
Bin Liu
Keyword(s):  
Probability Distribution ◽  
Product Quality ◽  
Manufacturing System ◽  
Evaluation Method ◽  
Probability Distributions ◽  
Bootstrap Method ◽  
Maximum Entropy Principle ◽  
Reliability Evaluation ◽  
Small Sample ◽  
Inspection Data

The output performance of the manufacturing system has a direct impact on the mechanical product quality. For guaranteeing product quality and production cost, many firms try to research the crucial issues on reliability of the manufacturing system with small sample data, to evaluate whether the manufacturing system is capable or not. The existing reliability methods depend on a known probability distribution or vast test data. However, the population performances of complex systems become uncertain as processing time; namely, their probability distributions are unknown, if the existing methods are still taken into account; it is ineffective. This paper proposes a novel evaluation method based on poor information to settle the problems of reliability of the running state of a manufacturing system under the condition of small sample sizes with a known or unknown probability distribution. Via grey bootstrap method, maximum entropy principle, and Poisson process, the experimental investigation on reliability evaluation for the running state of the manufacturing system shows that, under the best confidence levelP=0.95, if the reliability degree of achieving running quality isr>0.65, the intersection area between the inspection data and the intrinsic data isA(T)>0.3and the variation probability of the inspection data isPB(T)≤0.7, and the running state of the manufacturing system is reliable; otherwise, it is not reliable. And the sensitivity analysis regarding the size of the samples can show that the size of the samples has no effect on the evaluation results obtained by the evaluation method. The evaluation method proposed provides the scientific decision and suggestion for judging the running state of the manufacturing system reasonably, which is efficient, profitable, and organized.

Reliability Assessment Methods of Complicated Mechanical Product Based on Statistical Learning Theory

2008 ◽  
Vol 44-46 ◽  
pp. 575-580 ◽  
Author(s):  
X.Y. Shao ◽  
Jun Wu ◽  
Ya Qiong Lv ◽  
Chao Deng
Keyword(s):  
Statistical Learning ◽  
Learning Theory ◽  
Evaluation Method ◽  
Statistical Learning Theory ◽  
Time Series Prediction ◽  
Reliability Evaluation ◽  
Small Sample ◽  
Accelerated Life Test ◽  
System Level ◽  
Support Vector

As the reliability test data of complicated mechanical products is rare in quantity on the system-level and difficult to determine the accurate composition of the life distribution unit as well, the traditional reliability evaluation method based on evolutionary theory has been of little use. And the Statistical Learning Theory begins to be widely focused on as a novel small sample statistic method, which has been mostly applied to pattern recognition, fault detection, time series prediction and so on. This paper creates a new method for reliability evaluation derived from Statistical Learning Theory. By constructing Support Vector Machine with analog reasoning, and solving linear operator equation, the probability density of product can be evaluated directly and then the product reliability index can be obtained. Compared with the traditional way, this method can apparently increase the accuracy and generalization ability of reliability evaluation within limited samples. Finally, this paper presents the bridge of a certain heavy special vehicle as an example to testify the efficiency of this method, and uses the accelerated life test of the vehicle bridge to estimate its reliability.

Studies on Broad-Sense Sample Method in Data Mining

2014 ◽  
Vol 989-994 ◽  
pp. 1453-1455
Author(s):  
Yan Tang
Keyword(s):  
Residual Life ◽  
High Reliability ◽  
Small Sample ◽  
Life Assessment ◽  
Batch Production ◽  
Long Life ◽  
Residual Life Assessment ◽  
Sample Method ◽  
Whole Life Cycle ◽  
Existing Data

With the development of science and technology, people pay more and more attention to the reliability of the products, especially in some special field, such as aerospace, military products, and some products of high reliability and long life. As a part that runs through the whole life cycle of products, reliability test provides an important source of data for the design, batch production and residual life assessment of the product development. For some expensive, new products put into use, they are not quite little in amount, having the characteristics of small sample. In this case, how to use the existing data to predict product life, reliability of calculating the reliability of a product more accurately and other related parameters is particularly important.

scholarly journals State Evaluation Method of Robot Lubricating Oil Based on Support Vector Regression

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Dongdong Guo ◽  
Xiangqun Chen ◽  
Haitao Ma ◽  
Zimei Sun ◽  
Zongrui Jiang
Keyword(s):  
Support Vector Regression ◽  
Evaluation Method ◽  
Prediction Models ◽  
Sample Volume ◽  
Spare Parts ◽  
Small Sample ◽  
Industrial Robots ◽  
Lubricating Oil ◽  
Support Vector ◽  
The Cost

Recently, the development of the Industrial Internet of Things (IIoT) has led enterprises to re-examine the research of the equipment-state-prediction models and intelligent manufacturing applications. Take industrial robots as typical example. Under the effect of scale, robot maintenance decision seriously affects the cost of spare parts and labor deployment. In this paper, an evaluation method is proposed to predict the state of robot lubricating oil based on support vector regression (SVR). It would be the proper model to avoid the structural risks and minimize the effect of small sample volume. IIoT technology is used to collect and store the valuable robot running data. The key features of the running state of the robot are extracted, and the machine learning model is applied according to the measured element contents of the lubricating oil. As a result, the cost of spare parts consumption can be saved for more than two million CNY per year.

Estimation of Overall Reliability for Zero-Failure Batch Production Monitoring Instrument Based on E-Bayes Method

2014 ◽  
Vol 578-579 ◽  
pp. 1177-1182
Author(s):  
Wei Hua Fang ◽  
Na Zhang
Keyword(s):  
Evaluation Method ◽  
High Reliability ◽  
Interval Estimation ◽  
Reliability Evaluation ◽  
Point Estimation ◽  
Batch Production ◽  
Monitoring Instrument ◽  
Failure Data ◽  
Bayes Theory ◽  
Overall Reliability

To solve the problem of the overall reliability evaluation for the specific batch high-reliability monitoring instrument, for the practical problems for zero-failure data of the high-reliability instrument, we propose the batch production instrument overall reliability evaluation method based on E-Bayes theory, and give the point estimation and interval estimation of instrument overall reliability. Case study demonstrates the rationality and feasibility of the proposed method, which provides a reference for the dam safety monitoring management.

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