International Journal of Reliability, Quality and Safety Engineering

This paper describes how MEADEP, a system level dependability prediction tool, and CASRE, a software reliability growth prediction tool can be used together to predict system reliability (probability of failure in a given time interval), availability (proportion of time service is available), and performability (reward-weighted availability). The system includes COTS hardware, COTS software, radar, and communication gateways. The performability metric also accounts for capacity changes as processors in a cluster fail and recover. The Littlewood Verall and Geometric model is used to predict reliability growth from software test data this prediction is integrated into a system level Markov model that incorporates hardware failures and recoveries, redundancy, coverage failures, and capacity. The results of the combined model can be used to predict the contribution of additional testing upon availability and a variety of other figures of merit that support management decisions.

In modern techniques and science one meets design problems for multi-factor experiments in a large experimental region where the underlying model is unknown. These problems needs space filling designs. The uniform experimental design seeks its design points to be uniformly scattered on the experimental domain and is one kind of space filling designs that can be used for computer experiments and also for industrial experiments when the underlying model is unknown. In this paper we shall introduce the theory and method of the uniform design and related data analysis and modelling methods. Applications of the uniform design to industry and other areas are discussed.

This paper focuses on utilizing CAE models, DOE, and RSM to conduct a virtual experimental analysis and design on glass guidance system. The methodologies and critical design parameters are presented.

Generally, a software development manager is performing the testing-progress management by using various CASE tools. These CASE tools support testing activities of Windows applications or embedded software systems. Then, by using various testing coverage-measures, the testing-progress management can be recognized visually. However, in the system-testing phase, it is impossible to measure them of software systems which have been becoming large-scale and complicated recently. Therefore, the manager has to determine the testing termination time in consideration of the convergence situation of the cumulative number of detected faults and the prespecified delivery time to the users. These determination method have ambiguity based on the manager's experience or intuition. In this paper, we investigate the relationship between the testing-domain rate derived from a testing-domain dependent software reliability growth model and the testing-path coverage. Then, we show that the testing-domain rate, which is defined as the increasing ratio of the testing-paths in the modules and functions in the software system to be influenced by executed test-cases, is useful as an alternative measure of testing-coverage metrics. Further, this model is applied to the fault data observed in an actual software project, and the comparisons of goodness-of-fit with the conventional software reliability growth models are performed. Finally, in numerical illustrations, we discuss the software reliability analysis and the usefulness of the testing-domain rate, and propose a new testing-path coverage rate.

Vehicle high mileage warranty issues associated with powertrain mounts are one of the major concerns in vehicle design for long-term customer satisfaction. The present paper is concerned with a root cause understanding of high mileage degradation mechanisms of hydraulic engine mounts. Engine mount stiffness degradation characteristics are studied using laboratory test data. The correlation between the number of powertrain mount related warranty claims and mean temperatures of different geographic areas in the United States is presented. The effects of underhood thermal management design on mount temperature and strategies for improving high mileage mount degradation are discussed.

Developing and delivering quality software in a timely fashion has proven to be a difficult goal to obtain. Defining a thorough and effective software development process lays the foundation for successful projects. The Software Engineering Institute's (SEI) Capability Maturity Model (CMM) defines the framework for such a process. This paper examines the implementation of an SEI Level 5 process within the Communication Systems Division at General Dynamics Decision Systems. A brief review of the CMM is presented followed by an overview of the organization. Examples of the organizations software development process are cited.

In this paper, a new variable selection procedure is introduced for the analysis of uniform design and computer experiment. The new procedure is distinguished from the traditional ones in such a way that it deletes insignificant variables and estimates the coefficients of significant variables simultaneously. The new procedure has an oracle property (Fan and Li⁸). It is better than the best subset variable selection in terms of computational cost and model stability. It is superior to the stepwise regression because it does not ignore stochastic errors during the course of selecting variables. The proposed procedure is illustrated by two examples, one is a typical example of uniform design, and the other one is a classical example for computer experiment.

This paper describes an innovative technique for the quality control of gear pumps based on the frequency analysis of the oscillations of the discharge pressure using a new mathematical tool called "the spectrum of the power cepstrum." It consists in applying Fourier transform of the pressure data sampled in the time domain three times consecutively. The innovative technique makes it possible to obtain an acceptance mask for a set of gear pumps in the same series, in which the class and entity of a defect can be detected.

The vehicle body-in-prime static stiffness can significantly affect vehicle NVH, ride and handling, and durability performance. Stiffness target setting is a crucial step in a vehicle design and development process. Traditionally, benchmarking data and CAE models are used to help identify and set the target, yet they have proven to be time consuming and very subjective. On the other hand, statistical models obtained through data mining or meta-modeling have been tested and applied to various problems, including design optimization. In the present paper, we attempt to utilize this technology for providing more objective vehicle static stiffness targets. Stiffness data characteristics of various vehicle classes are benchmarked. Different statistical modeling techniques are examined using the data to set stiffness targets. The validity and limitations of these techniques are also discussed.