scholarly journals Reliability assessment of cutting tools based on zero-failure data

2020 ◽  
Author(s):  
Bin Yang ◽  
Kai Guo ◽  
Bin Feng ◽  
Chang'an Zhou ◽  
Chao Sun ◽  
...  
Keyword(s):  
Weibull Distribution ◽  
Titanium Alloys ◽  
Weighted Least Squares ◽  
Cutting Tools ◽  
Reliability Assessment ◽  
Assessment Model ◽  
Machining Process ◽  
Distribution Model ◽  
Failure Data ◽  
Monolithic Components

Abstract Titanium alloys are widely utilized in aeronautical monolithic components due to their excellent mechanical properties. However, the low machinability of titanium alloys results in an uncertain lifetime of cutting tools. Reliability assessment on cutting tools has become more and more significant to the effectiveness and stability of machining systems. Considering that cutting tools are often replaced well before the end of their useful lifetime to avoid failures and thus few tools failure data can be obtained during the machining process of titanium alloys, a reliability assessment model based on zero-failure data is developed to evaluate the reliability of cutting tools. A Weibull distribution model is chosen to describe the life distribution of cutting tools for reliability evaluation. Matching distribution curve method and weighted least squares method are used to estimate the distribution parameters. A novel approach using hierarchical Bayesian method for estimating the prior distribution of failure probability with zero-failure data is proposed by combining the characteristics of Weibull distribution and Incomplete Beta distribution. Reliability analysis for cutting tools with zero-failure data is performed using the proposed method. By comparing with the experience of experts and adopting stochastic simulation, the results of point estimation show plausibility of the proposed approach.

Risk Assessment Model of Automobile Defect Based on Weibull

2011 ◽  
Vol 383-390 ◽  
pp. 7496-7502
Author(s):  
Guo Zhong Huang ◽  
Yan Wang ◽  
Ying Chen ◽  
Jing Wang
Keyword(s):  
Risk Assessment ◽  
Weibull Distribution ◽  
Assessment Model ◽  
Distribution Model ◽  
Risk Assessment Model ◽  
Event Tree ◽  
Event Tree Analysis ◽  
Probability Forecast ◽  
Failure Data ◽  
Risk Forecast

This paper analyzed the automobile defects and its risk characteristics; the Event Tree Analysis (ETA) method was introduced to determine the risk flow route of automobile defects. According to the scattered of automobile, a risk forecast method based on the Weibull distribution is established. Based on the thousand vehicle breakdown number for risk probability forecast, propose a risk assessment model of automobile defect. The results indicate that on gathering actual failure data from after-sales service, the Weibull distribution model has a favorable applicability for forecasting risk possibility.

Engine Failure Data Analysis Method Based on Weibull Distribution Model

2011 ◽  
Vol 128-129 ◽  
pp. 850-854
Author(s):  
Ying Kui Gu ◽  
Jing Li
Keyword(s):  
Weibull Distribution ◽  
Distribution Model ◽  
Parallel Model ◽  
Analysis Method ◽  
Failure Data ◽  
Rod System ◽  
Distribution Parameters ◽  
Engine Failure ◽  
Engine Reliability ◽  
Drawing Method

The failure data of crank rod system was analyzed by using weibull parallel model on the base of the simple weibull method. The distribution parameters of the weibull parallel model were estimated by using drawing method. The solving process of WPP drawing method was given in detial. Results show that the fitting degree of the failure data in the weibull parallel model is higher than that of the simple weibull distribution model, and it can more accurately described the failure distribution curve of the system in life cycle, which can provide necessary information for engine reliability indexes computation.

A Reliability Assessment Method of Fiber Optic Gyro

2013 ◽  
Vol 760-762 ◽  
pp. 152-156
Author(s):  
Ping Huang ◽  
Shao Bin Guo
Keyword(s):  
Fiber Optic ◽  
Reliability Assessment ◽  
Temperature Stability ◽  
Assessment Method ◽  
Spectral Lines ◽  
Distribution Model ◽  
Mechanism Analysis ◽  
Fiber Optic Gyro ◽  
Failure Data ◽  
Erbium Doped

Erbium-doped fiber source for Fiber Optic Gyro (FOG) uses doped fiber to produce super fluorescence with laser pumping. It has higher output power, wide spectral lines, lower temporal coherence, good temperature stability and long life, which are perfect source for high precision FOG. To solve the problem of reliability analysis of erbium-doped fiber source for FOG with zero failure data, Weibull distribution is chosen as the life distribution model of erbium-doped fiber source on basis of the failure mechanism analysis in this paper. And Bayesian theory is used to estimate the failure rate in different time with zero failure data, then the parameters of the life model are estimated to get reliability index of erbium-doped fiber source. The method greatly decreases the number of test samples because of Bayesian estimation has take advantage of experience information, and also, it overcomes the shortcoming of relying on failure data when using traditional reliability assessment methods. So it has great value on project application.

scholarly journals Estimation on Reliability Models of Bearing Failure Data

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Xia Xintao ◽  
Chang Zhen ◽  
Zhang Lijun ◽  
Yang Xiaowei
Keyword(s):  
Probability Distribution ◽  
Weibull Distribution ◽  
Normal Distribution ◽  
Distribution Model ◽  
Bearing Failure ◽  
Analysis Model ◽  
Failure Data ◽  
Log Normal ◽  
Log Normal Distribution ◽  
Two Parameter

The failure data of bearing products is random and discrete and shows evident uncertainty. Is it accurate and reliable to use Weibull distribution to represent the failure model of product? The Weibull distribution, log-normal distribution, and an improved maximum entropy probability distribution were compared and analyzed to find an optimum and precise reliability analysis model. By utilizing computer simulation technology and k-s hypothesis testing, the feasibility of three models was verified, and the reliability of different models obtained via practical bearing failure data was compared and analyzed. The research indicates that the reliability model of two-parameter Weibull distribution does not apply to all situations, and sometimes, two-parameter log-normal distribution model is more precise and feasible; compared to three-parameter log-normal distribution model, the three-parameter Weibull distribution manifests better accuracy but still does not apply to all cases, while the novel proposed model of improved maximum entropy probability distribution fits not only all kinds of known distributions but also poor information issues with unknown probability distribution, prior information, or trends, so it is an ideal reliability analysis model with least error at present.

Optimization of Distribution Model for CNC Lathes Early Failures

2009 ◽  
Vol 407-408 ◽  
pp. 107-111
Author(s):  
Dian Sheng Chen ◽  
Tian Shan Lv
Keyword(s):  
Weibull Distribution ◽  
Exponential Distribution ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Distribution Model ◽  
Early Failure ◽  
Index Method ◽  
Failure Data ◽  
Failure Distribution ◽  
Early Failures

To eliminate early failures of CNC lathes, it is necessary to study the early failure distribution to establish accurate early failure distribution model of CNC lathes. During the modeling process, the same group of failure data obeys Weibull distribution, exponential distribution, super-exponential distribution and gamma distribution at the same time. To optimize the failure distribution model, this paper used “relevant index” method by comparing the distribution function curve fitting and determined that this group of failure data fitted Weibull distribution using maximum likelihood estimation method with its shape parameter k 0.8706 and scale parameter b142.6991.

A Reliability Data Analysis Method Using Mixture Weibull Distribution Model

2011 ◽  
Vol 148-149 ◽  
pp. 1449-1453 ◽  
Author(s):  
Ying Kui Gu ◽  
De Liang Ge ◽  
Yao Gang Xiong
Keyword(s):  
Weibull Distribution ◽  
Failure Time ◽  
Likelihood Estimation ◽  
Weibull Model ◽  
Distribution Model ◽  
Time Data ◽  
Failure Rate Function ◽  
Failure Data ◽  
Wide Range ◽  
Rod System

The weibull distribution plays a crucial role in reliability theory and life-testing experiments. Weibull mixtures are widely used to model lifetime and failure time data, since they exhibit a wide range of shapes for the failure rate function. In this paper, the failure data of crank rod system was analyzed by using mixture weibull distribution model. The distribution parameters of the mixture weibull distribution model were estimated by using maximum likelihood estimation and drawing method. The comparison of fitting degree of failure location between standard weibull distribution model and mixture weibull model was given. Results show that the fitting degree of the failure data in the mixture weibull distribution model is higher than that of the simple weibull distribution model, and it can more accurately described the failure distribution curve of the system in life cycle.

scholarly journals Research on Reliability Assessment of Mechanical Equipment Based on the Performance–Feature Model

2018 ◽  
Vol 8 (9) ◽  
pp. 1619 ◽  
Author(s):  
Wei Dai ◽  
Yongjiao Chi ◽  
Zhiyuan Lu ◽  
Meiqing Wang ◽  
Yu Zhao
Keyword(s):  
Distribution Function ◽  
Evaluation Method ◽  
High Reliability ◽  
Reliability Assessment ◽  
Feature Model ◽  
Machining Process ◽  
Distribution Model ◽  
Mechanical Equipment ◽  
Time Data ◽  
Degradation Model

There is a growing body of literature which recognizes the importance of mechanical equipment reliability during processing, and reliability assessment is important in guaranteeing the precision, function, and use life span of mechanical equipment. For products with a long lifetime and high reliability, it is difficult to assess lifetime and reliability using traditional statistical inference based on a large sample of data from the lifetime test. Therefore, this study contributed to this growing area of research, through a reliability evaluation method based on degradation path distribution related to signal characteristics. In this research, an effective method for reliability assessment was constructed, in which the signal features of the machining process were used to replace traditional time data and fit equipment degradation model. The pseudo failure characteristic (PFC) was obtained according to the failure threshold and the reliability curve was plotted by a PFC distribution model. Experimental investigation on tool reliability assessment was used to verify the effectiveness of this method, in which the trend that tool wear changes with the features was fitted by a Gaussian distribution function and Logarithmic distribution function, to obtain a better tool degradation model. The results illustrated the model could evaluate reliability of mechanical equipment effectively.

Modeling and Analysis of Machining Center Reliability Based on MATLAB

2014 ◽  
Vol 945-949 ◽  
pp. 3102-3106 ◽  
Author(s):  
Xue Li ◽  
Chuan Gui Yang ◽  
Xiao Cui Zhu ◽  
Xin Fang Yuan
Keyword(s):  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Theoretical Basis ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Distribution Model ◽  
Modeling And Analysis ◽  
Failure Data ◽  
Time Between Failures ◽  
Machining Center

A method is introduced to process the failure data of 12 sets of machining centers for modelling and analyzing their reliability. In this method, Matlab is applied to fit the failure data for getting a hypothesis that their time between failures obey weibull distribution. Then, MLE (maximum likelihood method) is used to estimate parameters of distribution model of the time between failure, and the K-S test is employed to test the above hypothesis for proving that time between failures of machining center obey weibull distribution. Finally, the analysis of their common failures and corresponding improvements are done, which provide the theoretical basis for improvement of reliability of machining center and are greatly valuable in engineering.

scholarly journals Mechanical Damage Assessment for Pneumatic Control Valves Based on a Statistical Reliability Model

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3307
Author(s):  
Nirbhay Mathur ◽  
Vijanth Sagayan Asirvadam ◽  
Azrina Abd Aziz
Keyword(s):  
Weibull Distribution ◽  
Reliability Assessment ◽  
Mechanical Damage ◽  
Control Valve ◽  
Reliability Models ◽  
Statistical Reliability ◽  
Processing Plants ◽  
Simulated Process ◽  
Pneumatic Control Valves ◽  
Log Normal

A reliability assessment is an important tool used for processing plants, since the facility consists of many loops and instruments attached and operated based on other availability; thus, a statistical model is needed to visualize the reliability of its operation. The paper focuses on the reliability assessment and prediction based on the existing statistical models, such as normal, log-normal, exponential, and Weibull distribution. This paper evaluates and visualizes the statistical reliability models optimized using MLE and considers the failure mode caused during a simulated process control operation. We simulated the failure of the control valve caused by stiction running with various flow rates using a pilot plant, which depicted the Weibull distribution as the best model to estimate the simulated process failure.

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