International Journal of Reliability, Quality and Safety Engineering

In the process of power dispatching and control, dispatching small network is an effective means to realize the remote number allocation in power dispatching and switching network. However, due to the scattered site, a wide range, and some unmanned site remote locations, there is a need for a small site terminal equipment to find the fault. To solve these problems, a terminal graphics management system is established. First, the real-time status monitoring mechanism of terminal is established based on IPoE technology to realize the status awareness of remote terminal. The heartbeat mechanism is segmented in link layer to meet the real-time performance of terminal state presentation. Then, the message class method with special characters is used to replace the original dial method to test the detection mode of manual answering. Automatic monitoring using telemetry technology can effectively monitor in unattended situations. Finally, considering the performance of different devices and various fault scenarios, according to the inherent hardware characteristics of segmented devices, the unified packet detection technology is used to realize the fault diagnosis of network sub-nodes, and finally realize the fault diagnosis of the terminal. After experimental verification and testing, the platform can realize remote fault detection and management of each plant terminal through topological graphical management system. The system can improve the scale and management ability of intelligent power dispatching of small stations.

In many practical situations, more than one failure mechanism may contribute to product failure. Many studies assume independence between the different competing risks of failure. Nevertheless, the assumption of independence is not always justified in various practical applications. When the competing risks are assumed dependent, it is important to identify models that describe their dependence structure. Copulas are considered a powerful tool to model such dependence structures. This paper addresses the problem of developing Bayesian life test acceptance criteria through two-sample prediction of future observations based on another independent Weibull progressively Type-II censored sample with binomial random removals. It is assumed that unit failure occurs due to only one of two competing risks. Dependence among the competing risks of failure is modeled using Archimedean copulas with nonconjugate prior distributions. A Metropolis–Hastings Markov chain Monte Carlo algorithm is implemented to derive the prediction intervals that define the proposed acceptance criteria. The derived acceptance criteria enable manufacturers to conform to the required quality specifications and help their clients to properly set their quality expectations. A real data example is provided to illustrate the proposed life test acceptance criteria.

In the progression of innovation, software plays a meaningful role in society. Hence, the need for reliable software increases day by day. But the development process of software has become very complex. In this paper, a nonlinear transfer function model has been developed to predict the remaining faults present in the software. A stepwise procedure is discussed for developing a nonlinear transfer function model. A comparison with the linear transfer function model has been carried out. Release time modeling has also been carried out. Some real data sets have been used for illustration.

The method of six-sigma and the index of process capability are both commonly used tools in the industrial community. Process engineers can follow five improvement steps of the six-sigma method, including “define”, “measure”, “analyze”, “improve”, and “control” (DMAIC), aiming to improve and enhance the process quality. However, none of these five improvement steps have a clear corresponding approach. This paper considered process capability indices not only a process quality evaluation tool widely used in the industrial community but also a process quality evaluation and analysis tool adopted by internal engineers. Therefore, this paper applied the method integrating process capability indices and statistical testing to develop execution models for the five improvement steps, DMAIC, of the six-sigma method. First, this paper, based on the concept of yield, not only deduced the relationship between the required value of the process capability index for the product and the process capability index value of the individual quality characteristic but also discussed the definition of the quality level of six-sigma as well as its relationship with the process capability index. Next, according to these results, five improvement execution models of the six-sigma method were developed and served as a reference for the process engineers in the industry to promote the performance of the six-sigma project. The proposed method in this paper applied various normal processes and combined the six-sigma method and process capability indices, both of which are tools commonly used in the industrial community. It also has taken into account the advantages of theoretical contribution and industrial acceptance.

In order to ensure the protection of important data in the face of natural and man-made disasters in data centers in cloud computing services, sufficient backups of the data are required. This paper briefly introduced the security encryption measures for disaster backup of data under cloud computing. The transmission performance during disaster backup was improved by the intelligent ant colony algorithm. Simulation experiments were performed to compare the encryption performance of two encryption algorithms, the Rivest, Shamir, and Adleman (RSA) algorithm and the Advanced Encryption Standard (AES) algorithm, in disaster backup, and the transmission performance of the intelligent ant colony algorithm and the maximum network flow algorithm. The results showed that the asymmetric encryption algorithm and the RSA algorithm had better encryption performance than the AES algorithm in terms of disaster backup; with the increase of backup data volume, the transmission performance of both route assignment algorithms decreased, but the intelligent ant colony algorithm always outperformed the maximum network flow algorithm.

Nowadays, in competitive market, in order to increase confidence in the quality of products, dealers set a warranty for selling them. This issue seems to have a tremendous effect on attracting more customers, especially when it comes to purchasing second-hand products. In this regard, this paper tends to present a two-dimensional warranty policy for second-hand products containing age and usage in two cases, L-shaped and Rectangular. Since maximizing customer repurchase probability is directly linked with dealer’s profit as well as minimizing costs, dealers ought to consider customer satisfaction. In fact, customer satisfaction depends on warranty length and the expected numbers of failures in the post-warranty period. To demonstrate the characteristics of the model, two comparative studies are conducted to present the effect of bi-objective function on warranty models. Finally, in order to extend insights into the matter, a sensitivity analysis is carried out on the varying parameters.

In this paper, we shall discuss some statistical properties of the estimator of $C_p$ when sample observations are autocorrelated and affected by measurement errors. The presence of autocorrelation in production units is very common in many industries like chemical, food processing, pharmaceutical, paper, and mineral. At the same time some amount of measurement error is invariably present in the sample observations due to inaccurate measurement process. In this paper, we discuss the case of a first-order stationary autoregressive process where measurement error follows a Gaussian distribution. The comparison of the statistical properties of the estimator in this case with the error-free case is the subject matter of this paper.

To minimize waste and scrap, reworking becomes an integral part of many manufacturing companies. In this study, the integrated production-quality planning in the tactical horizon considering the customer demand uncertainty is presented. A mixed-integer programming model is developed to find optimal dynamic planning production which minimizes costs and reduces the scrap. After each production cycle, the quality indicators and relative costs for good and poor quality are performed. These indicators are introduced into the developed model to determine the optimal production orders and stock levels considering the reworked and scrapped products. A real case study is presented to illustrate the applicability of the proposed model. By comparing the obtained results with real results from studied company, we proved that this new model gives an optimal production plan.

In this paper, we study renewals in a system with two components connected in series. Both components can undergo corrective maintenance (i.e., either full replacement/perfect repair or minimal repair) when a failure occurs. When one of the units fails and is correctively maintained, the other one is either preventively replaced (if cost-feasible) or simply left working as is. When a component is minimally repaired or left working as is, its remaining lifetime is reevaluated (“memory effect”) and is taken into consideration in calculating the next renewal. A “coupled” lifetime that combines the lifetimes of the two serially connected components is proposed to represent the joint lifetime of the system. We develop renewal functions based on the coupled lifetimes and show that they follow the classical or generalized renewal theory depending on whether the components work without memory or not. Approximation formulas for the new renewal functions are also obtained and validated by Monte Carlo simulations for various combinations of distributions, and a comparative cost analysis is conducted.

This paper presents a software reliability growth model framework modeled from a non-homogenous poisson process (NHPP). Invariably, software testing continues to be a paramount measure for validating the standard of software. Test coverage measures appraise and estimate the proportion and gradation of testing in software. Therefore, presenting a correct picture of the test coverage becomes a prime requisite to guarantee software reliability. As an enhancement over the existing models, the proposed model integrates testing coverage (TC), error propagation, and fault withdrawal efficiency while keeping the number of parameters restrained to make the framework more reliable for parameter estimation. A relative analysis to assess the efficacy of the proposed model and some existing models has been carried out on the failure data obtained from three real-world software applications using six comparison criteria. Finally, the weighted criteria rank method has been used to rank the models and assess their performance. In addition, sensitivity analysis has been carried out to demonstrate the effect of the parameters of the proposed model on the mean value function.