Statistical Inference for Grouped Field Failure Data

2017 ◽  
pp. 233-247
Author(s):  
Piao Chen ◽  
Zhi-Sheng Ye
Keyword(s):  
Statistical Inference ◽  
Failure Data ◽  
Field Failure

Reliability Prediction Based on Field Failure Data of Guided Missile

2018 ◽  
Vol 18 (3) ◽  
pp. 250-259 ◽  
Author(s):  
Yangwoo Seo ◽  
Kyeshin Lee ◽  
Younho Lee ◽  
Jeyong Kim
Keyword(s):  
Reliability Prediction ◽  
Failure Data ◽  
Field Failure

scholarly journals On analysis of incomplete field failure data

2014 ◽  
Vol 8 (3) ◽  
pp. 1713-1727 ◽  
Author(s):  
Zhisheng Ye ◽  
Hon Keung Tony Ng
Keyword(s):  
Failure Data ◽  
Field Failure

Overview of Wind Turbine Field Failure Databases: A Discussion of the Requirements for an Analysis

2018 ◽  
Author(s):  
Roozbeh Bakhshi ◽  
Peter Sandborn
Keyword(s):  
Statistical Analysis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farms ◽  
Onshore Wind ◽  
Failure Times ◽  
Reliability Parameters ◽  
Failure Data ◽  
Offshore Installations ◽  
Field Failure

With renewable energy and wind energy in particular becoming mainstream means of energy production, the reliability aspect of wind turbines and their sub-assemblies has become a topic of interest for owners and manufacturers of wind turbines. Operation and Maintenance (O&M) costs account for more than 25% of total costs of onshore wind projects and these costs are even higher for offshore installations. Effective management of O&M costs depends on accurate failure prediction for turbine sub-assemblies. There are numerous models that predict failure times and O&M costs of wind farms. All these models have inputs in the form of reliability parameters. These parameters are usually generated by researchers using field failure data. There are several databases that report the failure data of operating wind turbines and researches use these failure data to generate the reliability parameters through various methods of statistical analysis. However, in order to perform the statistical analysis or use the results of the analysis, one must understand the underlying assumptions of the database along with information about the wind turbine population in the database such as their power rating, age, etc. In this work, we analyze the relevant assumptions and discuss what information is required from a database in order to improve the statistical analysis on wind turbines’ failure data.

Integrated reliability and maintainability analysis of Computerized Numerical Control Turning Center considering the effects of human and organizational factors

2019 ◽  
Vol 26 (1) ◽  
pp. 87-103
Author(s):  
Rajkumar Bhimgonda Patil
Keyword(s):  
Organizational Factors ◽  
Numerical Control ◽  
Content Type ◽  
Failure Data ◽  
Turning Center ◽  
Maintainability Analysis ◽  
Human And Organizational Factors ◽  
Mean Time ◽  
Best Fit ◽  
Field Failure

Purpose Reliability, maintainability and availability of modern complex engineered systems are significantly affected by four basic systems or elements: hardware, software, organizational and human. Computerized Numerical Control Turning Center (CNCTC) is one of the complex machine tools used in manufacturing industries. Several research studies have shown that the reliability and maintainability is greatly influenced by human and organizational factors (HOFs). The purpose of this paper is to identify critical HOFs and their effects on the reliability and maintainability of the CNCTC. Design/methodology/approach In this paper, 12 human performance influencing factors (PIFs) and 10 organizational factors (OFs) which affect the reliability and maintainability of the CNCTC are identified and prioritized according to their criticality. The opinions of experts in the fields are used for prioritizing, whereas the field failure and repair data are used for reliability and maintainability modeling. Findings Experience, training, and behavior are the three most critical human PIFs, and safety culture, problem solving resources, corrective action program and training program are the four most critical OFs which significantly affect the reliability and maintainability of the CNCTC. The reliability and maintainability analysis reveals that the Weibull is the best-fit distribution for time-between-failure data, whereas log-normal is the best-fit distribution for Time-To-Repair data. The failure rate of the CNCTC is nearly constant. Nearly 66 percent of the total failures and repairs are typically due to the hardware system. The percentage of failures and repairs influenced by HOFs is nearly only 16 percent; however, the failure and repair impact of HOFs is significant. The HOFs can increase the mean-time-to-repair and mean-time-between-failure of the CNCTC by nearly 65 and 33 percent, respectively. Originality/value The paper uses the field failure data and expert opinions for the analysis. The critical sub-systems of the CNCTC are identified using the judgment of the experts, and the trend of the results is verified with published results.

Statistical Inference of LZL-Type Mass Flowmeter Life Distribution Model

2010 ◽  
Vol 458 ◽  
pp. 173-178
Author(s):  
Zhen Zhou ◽  
L.N. Zhang ◽  
Y. Qin ◽  
D.Z. Ma ◽  
B. Niu
Keyword(s):  
Least Square ◽  
Distribution Model ◽  
Maintenance Policy ◽  
Life Distribution ◽  
Mass Flowmeter ◽  
Failure Data ◽  
Distribution Type ◽  
Two Parameter ◽  
Field Failure ◽  
Type Mass

Characteristics of field failure data are analyzed in this paper. The failure data and sales record of LZL-type mass flowmeter are used to infer life distribution of this conduct. The lines can be fitted in coordinates of six distribution using least square and the residual sum of squares are compared, the minimum correspond is the best distribution type. The results show that the life distribution style of this conduct is the two parameter exponential distribution, which is the base to analyze and predict failure development, research failure mechanism and draw up maintenance policy.

Model Selection Methods for Reliability Assessment Based on Interval-Censored Field Failure Samples

2020 ◽  
Vol 27 (06) ◽  
pp. 2050018
Author(s):  
Tzong-Ru Tsai ◽  
Sih-Hua Wu ◽  
Yan Shen
Keyword(s):  
Model Selection ◽  
High Speed ◽  
Reliability Assessment ◽  
Lifetime Distribution ◽  
Maximum Likelihood Estimates ◽  
Selection Procedures ◽  
Failure Data ◽  
Proposed Model ◽  
Interval Censored ◽  
Field Failure

Incomplete field failure data from automated production are often applied for evaluating the system reliability. But the evaluation could be impacted by the uncertainty of the product’s lifetime distribution, which is usually predetermined but may be misspecified. In this paper, we assume that the system lifetime distribution follows a location-scale family with several candidates instead of a certain distribution. Two model selection procedures are proposed to assign the most likely candidate distribution from a pool of the location-scale distributions based on interval-censored field failure samples. The maximum likelihood estimates (MLE) of parameters of the candidate distribution are estimated by using the Newton–Raphson method and the MLE of a quartile is assigned as the reliability measure for assessing the reliability of systems. To illustrate the applications of the proposed model selection procedures, an example of high-speed motor with interval-censored field failure data is given. Monte Carlo simulations are carried out to evaluate the performance of the proposed model selection procedures. Simulation results show that the proposed methods are efficient for model identification and can provide reliable reliability assessment.

Assessment of operation management for beer packaging line based on field failure data: A case study

2010 ◽  
Vol 98 (1) ◽  
pp. 51-59 ◽  
Author(s):  
Panagiotis H. Tsarouhas ◽  
Ioannis S. Arvanitoyannis
Keyword(s):  
Operation Management ◽  
Failure Data ◽  
Field Failure

Analysis of Field Failure Data on HMWPE- and XLPE-Insulated High-Voltage Distribution Cable

1985 ◽  
Vol PAS-104 (8) ◽  
pp. 1979-1985 ◽  
Author(s):  
L. Stember ◽  
M. Epstein ◽  
G. Gaines ◽  
G. Derringer ◽  
R. Thomas
Keyword(s):  
High Voltage ◽  
Voltage Distribution ◽  
Failure Data ◽  
Field Failure
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