scholarly journals Exploratory comparison of methods for combining failure-rate data from different data sources

1978 ◽  
Author(s):  
H.F. Jr. Martz ◽  
R.A. Waller
Keyword(s):  
Failure Rate ◽  
Data Sources ◽  
Rate Data ◽  
Comparison Of Methods

scholarly journals Research on Dynamic Reliability of a Jet Pipe Servo Valve Based on Generalized Stochastic Petri Nets

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yuanbo Chu ◽  
Zhaohui Yuan ◽  
Jia Chen
Keyword(s):  
Petri Nets ◽  
Failure Rate ◽  
Failure Modes ◽  
Stochastic Petri Nets ◽  
Rate Data ◽  
Repair Rate ◽  
Dynamic Reliability ◽  
Servo Valve ◽  
Reliability Parameters ◽  
Generalized Stochastic Petri Nets

The jet pipe servo valve is widely used in the military fields of aviation and ship, whose reliability has obvious randomness and dynamic. However, existing methods are either having complicated theory or analyzing static reliability. Based on the generalized stochastic petri nets (GSPN) theory and the collected basic failure modes and failure rate data of jet pipe servo valve, this paper proposes a novel modeling and simulating method for system’s dynamic behavior analysis. In this method, the dynamic reliability model considering failure’s random and repair is established and is simulated using GSPN software. Then, the steady state probability of servo valve is calculated, which is compared with the value calculated by Markov method. Finally, the dynamic reliability parameters of jet pipe servo valve are calculated using collected failure rate data and different repair rate data. Results show the probability that the maximum error between methods of GSPN and Markov is 2.07%, the optimal repair rate set is less than 1.71µi, and also the dynamic reliability parameters become better with increasing simulation time because of failure’s recovery. Therefore, research methods and results based on GSPN are concise and realistic, which can be used for failure’s qualitative forecast and dynamic reliability’s quantitative calculation of similar complicated system.

Reliability Prediction for Offshore Renewable Energy: Data Driven Insights

2017 ◽  
Author(s):  
Fraser J. Ewing ◽  
Philipp R. Thies ◽  
Benson Waldron ◽  
Jonathan Shek ◽  
Michael Wilkinson
Keyword(s):  
Renewable Energy ◽  
Failure Rate ◽  
Reliability Assessment ◽  
Surrogate Data ◽  
Data Sources ◽  
Reliability Prediction ◽  
Failure Rates ◽  
Onshore Wind ◽  
Energy Devices ◽  
Frequency Converters

Accurately quantifying and assessing the reliability of Offshore Renewable Energy (ORE) devices is critical for the successful commercialisation of the industry. At present, due to the nascent stage of the industry and commercial sensitivities there is very little available reliability field data. This presents an issue: how can the reliability of ORE’s be accurately assessed and predicted with a lack of specific reliability data? ORE devices largely rely on the assessment of surrogate data sources for their reliability assessment. To date there are very few published studies that empirically assess the failure rates of offshore renewable energy devices [1]. The applicability of surrogate data sources to the ORE environment is critical and needs to be more thoroughly evaluated for a robust ORE device reliability assessment. This paper tests two commonly held assumptions used in the reliability assessment of ORE devices. Firstly, the constant failure rate assumption that underpins ORE component failure rate estimations is addressed. Secondly, a model that is often used to assess the reliability of onshore wind components, the Non-Homogeneous Poisson Power Law Process (PLP) model is empirically assessed and trend tested to determine its suitability for use in ORE reliability prediction. This paper suggests that pitch systems, generators and frequency converters cannot be considered to have constant failure rates when analysed via nonrepairable methods. Thus, when performing a reliability assessment of an ORE device using non-repairable surrogate data it cannot always be assumed that these components will exhibit random failures. Secondly, this paper suggests when using repairable system methods, the PLP model is not always accurate at describing the failure behaviour of onshore wind pitch systems, generators and frequency converters whether they are assessed as groups of turbines or individually. Thus, when performing a reliability assessment of an ORE device using repairable surrogate data both model choice and assumptions should be carefully considered.

scholarly journals Comparison of Tritium Component Failure Rate Data

2005 ◽  
Vol 47 (4) ◽  
pp. 983-988 ◽  
Author(s):  
L. C. Cadwallader
Keyword(s):  
Failure Rate ◽  
Rate Data ◽  
Component Failure

scholarly journals Statistical Properties of a New Bathtub Shaped Failure Rate Model With Applications in Survival and Failure Rate Data

2021 ◽  
Vol 10 (3) ◽  
pp. 49
Author(s):  
Muhammad Z. Arshad ◽  
Muhammad Z. Iqbal ◽  
Alya Al Mutairi
Keyword(s):  
Failure Rate ◽  
Quantile Function ◽  
Data Sets ◽  
Lifetime Data ◽  
Rate Data ◽  
Rate Model ◽  
Unknown Parameters ◽  
Method Of Maximum Likelihood ◽  
Rate Functions ◽  
Lifetime Model

In this study, we proposed a flexible lifetime model identified as the modified exponentiated Kumaraswamy (MEK) distribution. Some distributional and reliability properties were derived and discussed, including explicit expressions for the moments, quantile function, and order statistics. We discussed all the possible shapes of the density and the failure rate functions. We utilized the method of maximum likelihood to estimate the unknown parameters of the MEK distribution and executed a simulation study to assess the asymptotic behavior of the MLEs. Four suitable lifetime data sets we engaged and modeled, to disclose the usefulness and the dominance of the MEK distribution over its participant models.

Estimating the Performance Measure of Exponential-Gamma Distribution with Application to Failure Rate Data

2020 ◽  
Vol 10 (3) ◽  
pp. p9994
Author(s):  
Ayeni Taiwo Michael ◽  
Ogunwale Olukunle Daniel ◽  
Adewusi Oluwasesan Adeoye ◽  
Odukoya Elijah Ayooluwa
Keyword(s):  
Failure Rate ◽  
Gamma Distribution ◽  
Performance Measure ◽  
Rate Data
Sign in / Sign up

Export Citation Format

Share Document