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The paper describes a two parameter model and its relationship to the widely used Weibull model. Mathematical properties of the distribution like survival and hazard functions, moments, harmonic and geometric means, Shannon entropy and mean residual life are derived. Different methods of estimation are discussed and a simulation study is performed to verify the efficiency of estimation methods. Applications of our distribution in different scenarios observed in real life are illustrated. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There are many state-of-the-art algorithms presented in the literature that perform very well on some evaluation data but are not studied with the data properties on which they are applied; therefore, they could have low performance on data with other characteristics. In this paper, the results of comprehensive research regarding the prediction with the frequently applied AdaBoost algorithm on real-world sensor data are presented. The chosen dataset has some specific characteristics, and it contains error and failure data of several machines and their components. The research aims to investigate whether the AdaBoost algorithm has the capability of predicting failures, thus providing the necessary information for monitoring and condition-based maintenance (CBM). The dataset is analyzed, and the principal characteristics are presented. Performance evaluations of the AdaBoost algorithm that we present show a prediction capability below expectations for this algorithm. The specificity of this study is that it indicates the limitation of the AdaBoost algorithm, which could perform very well on some data, but not so well on others. Based on this research and some others that we performed, and actual research from worldwide studies, we must outline that the mathematical analysis of the data is especially important to develop or adapt algorithms to be very efficient. [ABSTRACT FROM AUTHOR]

A detailed analysis is performed on a dataset of failure and maintenance records from various onshore wind farms located in different geographical areas for the safety, risk, reliability, availability, and maintainability characterization of wind turbines. Specifically, characteristics related to failures, including the criticality of failure modes, failure frequencies, failure rates, and lifetime distributions of components, are analyzed to support the failure identification and failure prevention of wind turbines. Additionally, characteristics of maintenance, including typical maintenance measures of failures, policies for spare components, delayed maintenance, as well as related times such as reaction time, travelling time, and mean time to repair, are provided to support the maintenance management of wind farms. Based on the operational data analysis results, a reliability influence factor-based failure data correction approach is presented to transfer the onshore data to floating offshore turbines by modeling the differences in failure occurrences based on experts' judgment. A comprehensive comparison with existing studies validates the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

In the past, several software reliability growth models (SRGMs) have been proposed to predict various aspects of software reliability, and non-homogeneous Poisson process (NHPP)-based SRGMs have drawn much attention. However, it should be noted that, in practice, blindly applying SRGMs might not lead to meaningful results when the characteristics of the data (i.e., data type) do not fit the characteristics of selected SRGMs. Moreover, there currently exist no guidelines for project managers or developers to follow in selecting the most appropriate SRGMs for a particular failure dataset. On the other hand, waiting to collect a substantial amount of failure data before fitting SRGMs might be unfeasible in many cases. In this paper, we propose useing statistical data analysis methods to investigate the characteristics of software failure data and to improve the accuracy of software reliability growth modeling. Here, SRGMs will be broadly partitioned into two categories: “NHPP models” and “non-NHPP models.” Similarly, software failure data can be classified into two categories: “NHPP-type data” and “non-NHPP-type data.” We propose a two-phase method to verify the hypothesis that failure data follow an NHPP. Through the proposed two-phase and Laplace trend tests, we will be able to infer whether a dataset is the NHPP type and whether it is properly used by NHPP-based SRGMs. In this case, the final choice for the most suitable NHPP model for the software failure process would be made, and the software reliability prediction result can be deemed trustworthy and helpful after correctly judging whether the dataset can be classified or not as being NHPP based. Experiments based on 25 real software failure data were presented and analyzed. Using our proposed method, project managers and/or developers will be able to analyze and use collected failure data to help improve both planning accuracy and software quality.

Safety-critical systems (SCSs) of nuclear power plants (NPPs) are being designed and developed to meet high dependability requirements. Fault tree analysis (FTA) is widely used for risk and reliability analysis of NPPs. However, fault trees (FTs) are static and have only limited capability to represent dynamic systems. FTA is also not capable of modeling non-binary logic and or modelling the system's evolution in time. Dynamic reliability methods are being developed to deal with such limitations. Time series Markov chains and dynamic flowgraph methodology are the dynamic reliability methods alternate to traditional FTA, which can be used for the system performance analysis. In this article, Time series Markov chains and DFM methods, for the system reliability predictions for the SCS of NPP are compared. The benefits of the proposed method are brought out to the traditional methods. The approach is applied on passive residual heat removal system of pressurized heavy water reactor under the station blackout scenario. [ABSTRACT FROM AUTHOR]

Copyright of Natural Gas & Oil is the property of Editorial Department of Natural Gas & Oil and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Since the accuracy of defect detection is strongly influenced by the data validity of pipeline magnetic flux leakage(MFL) inline detection, processing approaches for MFL failure data have been designed, based on the MFL failure signal characteristic analysis, to avoid, as far as possible, the influence of failure data. The approaches, such as threshold over-limit method, neighborhood difference threshold method, signal area, local zero-crossing rate, etc., were used for five types of failure data including over-limit failure, peak failure, continuous oversmoothing, single-channel data drift and sensor jitter, with the purpose of detection and rejection of failure data. With K-Nearest Neighbor(KNN) algorithm, the redundancy of the training set can be reduced by means of Support Vector Regression(SVR) algorithm, and the missing data interpolation method based on KNN and SVR was thus designed for missing data interpolation.The results demonstrate that, the data processing method for pipeline MFL failure is capable of accurately interpolating the missing MFL data in terms of different degrees of data missing, and considered to be of great reference to the failure data processing that frequently occurs in practical engineering.

A detailed analysis is performed on a dataset of failure and maintenance records from various onshore wind farms located in different geographical areas for the safety, risk, reliability, availability, and maintainability characterization of wind turbines. Specifically, characteristics related to failures, including the criticality of failure modes, failure frequencies, failure rates, and lifetime distributions of components, are analyzed to support the failure identification and failure prevention of wind turbines. Additionally, characteristics of maintenance, including typical maintenance measures of failures, policies for spare components, delayed maintenance, as well as related times such as reaction time, travelling time, and mean time to repair, are provided to support the maintenance management of wind farms. Based on the operational data analysis results, a reliability influence factor-based failure data correction approach is presented to transfer the onshore data to floating offshore turbines by modeling the differences in failure occurrences based on experts’ judgment. A comprehensive comparison with existing studies validates the performance of the proposed approach.

This paper presents an imprecise predictive inference method for accelerated life testing. The method is largely nonparametric, with a basic parametric function to link different stress levels. The log-rank test is used to provide imprecision for the link function parameter, which in turn provides robustness in the resulting lower and upper survival functions for a future observation at the normal stress level. An application using data from the literature is presented, and simulations show the performance and robustness of the method. In case of model misspecification, robustness may be achieved at the price of large imprecision, which would emphasize the need for more data or further model assumptions. [ABSTRACT FROM AUTHOR]

Much attention has been given to measure the reliability of safety critical systems (SCSs), assuming that components fail independently. However, in practice, dependent failures of components due to come common cause play a significant role in determining system reliabilities and hence can measure the risk more accurately. In this article, we propose a method to measure such dependent failures systematically, known as common cause failures (CCFs). We propose a framework to model and quantify CCF that contains two phases: screening analysis and detailed analysis. Screening analysis involves qualitative and quantitative screening; detailed analysis involves estimation of common cause basic event probabilities by developing a probability model. The proposed approach is applied on a case study of nuclear power plant (NPP). The result proves that risk is highly sensitive to the probability of a CCF. [ABSTRACT FROM AUTHOR]

In order to achieve a longer life cycle, higher reliability is necessary for products. Generally, the traditional reliability analysis methods can be performed based on sufficient failure data. However, it is difficult to get such amount of failure data in practical engineering, which brings challenges to the traditional reliability analysis methods. Consequently, the traditional reliability analysis methods are not suitable for the case of no failure data or less failure data. In order to tackle the above challenges, an uncertainty analysis strategy using accelerated performance degradation information is given. While, in this method, the utilization of acceleration factor increases the number of model parameters, which lead to the loss of the accuracy of the model parameter estimation under the finite degradation data. To enhance the above strategy, a reliability analysis method of accelerated performance degradation based on Bayesian strategy is proposed in this study. The accelerated performance degradation analysis method combining historical degradation data and empirical information is introduced here. An engineering example of CNC machine tool function milling head is also used to illustrate the effectiveness of the given method.

Based on the analysis of traditional calculation method of RPN (Risk Priority Number), a risk assessment method for CNC lathes based on failure data and fuzzy set theory combining with grey theory was proposed in this paper. Severity, occurrence and detection were treated as linguistic variables. Meanwhile, failure data was applied and maintenance time was introduced as another variable. Risk prioritization of all the failure modes was obtained through steps such as expert rating, solving α level sets, defuzzification and so on. Grey theory was applied to verify the accuracy of the proposed method. The method was used in a real case of risk analysis of spindle system of a CNC lathe, in which consistency and practicability of the results were illustrated. It can be concluded that the proposed method can take advantage of failure data and control subjectivity in analysis. In addition, importance weights of the four variables were taken into consideration. So that the drawbacks of traditional RPN analysis can be avoided and improvement measures could be made by designers and technologists according to the results so as to improve reliability of CNC lathes. [ABSTRACT FROM AUTHOR]

A power function having two constants is being studied here as a basic probability distribution with two parameters, some mathematical and statistical properties of this new distribution have been studied. Its utility for failure data has been shown by fitting a failure data on this distribution. [ABSTRACT FROM AUTHOR]

High failure rate and maintenance cost of wind turbines are one of the key issues in wind energy. In view of this, reliability centred maintenance (RCM) has been introduced to distinctively treat equipment failures according to their criticality. Criticality analysis (CA) is a pivotal step of RCM, derived from the failure frequency and failure modes effects of equipment, which determines when and how to maintain the wind turbines. However, in traditional CA, failure data is roughly considered, e.g. failure frequency is categorised as different ranks instead of accurate numerical value. In addition, the method of calculating criticality by multiplying variables is sensitive to variable ranges and values, which is unable to distinctly distinguish the contributions of different variables. Aimed at these deficiencies, an improved CA method with expansibility is proposed to assess the criticality of equipment failures in wind turbines based on Euclidean distance of failure vectors. To assess disparities among criticality ranks from diverse CA methods, an inverse number based method is presented. Several existing CA methods and the proposed CA in this study are compared using the data of wind turbines in literature, and the results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract By introducing the effects of the failure data and corrective maintenance, a novel methodology has been established to update the reliability of the corroding natural gas pipeline. Firstly, the methodology is able to calculate the failure probabilities for the small leak and burst based on the inspection data. The rank regression method is then applied to determine the fittest failure distribution function. Moreover, the failure distribution functions are updated by considering the failure data and the corrective maintenance. The Bayesian method and Markov Chain Monte Carlo (MCMC) simulation techniques are employed in the update process. Furthermore, the actual data from a natural gas pipeline located in China is used to illustrate this methodology, and the assessment indicates that the failure data and the corresponding corrective maintenance exert great impact on failure probability. [ABSTRACT FROM AUTHOR]

The traditional reliability evaluation is based on the failure data,which needs a large amount of statistical data and samples,and comprises the characteristics of long test cycle and high cost. To solve this problem,without failure data,a method of reliability evaluation was proposed which was based on the performance degradation data. The performance degradation model was built in the method which was based on the least square support machine. With the help of NSGA-II,the parameters optimization of the method above had been done,and the process of parameters optimization and reliability evaluation was given.Last but not least,the availability of the method is validated.

Design of experiments has received much attention in the context of reliability improvement. Many engineers and researchers are considering how to use designed experiments to improve the reliability of products and processes. This work illustrates a 12-run Plackett-Burman design and its analysis to improve the reliability of a diamond drill bit. A detailed description and execution of the design are provided including how to choose the factors and determine the number of experimental factors. In addition, the results of the real-world experiment are presented and the predicted performance from statistical model fitting is verified by part of a confirmation experiment. The experimental data show that the recommended factor settings substantially improved the reliability of the diamond drill bits. [ABSTRACT FROM AUTHOR]