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The k-out-of-n system structure is a very popular type of redundancy in fault-tolerant systems, with wide applications in both industrial and military systems. In this paper, the modeling, application and reliability evaluation of k-out-of-n systems are studied for the case where the components and the system have multiple performance levels. A multi-state k-out-of-n system model is proposed that allows different requirements on the number of components for different state levels, and, very importantly, more practical engineering systems can fit into this model. The multiple states in the model can be interpreted in two ways: (i) multiple levels of capacity; and (ii) multiple failure modes. Application examples of the proposed multi-state k-out-of-n system model are given under each of the interpretations. An approach is presented for efficient reliability evaluation of multi-state k-out-of-n systems with identically and independently distributed components. A recursive algorithm is proposed for reliability evaluation of multi-state k-out-of-n systems with independent components. Efficiency investigations show that both of the reliability evaluation approaches are efficient. The multi-state k-out-of-n system model with a constant k value, which is a special case of the general multi-state k-out-of-n system model, has been studied for a long time, but only on the theoretical stage. A practical application of this model is presented in this paper as well. Keywords: Multi-state system, k-out-of-n, reliability evaluation, performance levels, failure modes, 1. Introduction The k-out-of-n system structure is a very popular type of redundancy in fault-tolerant systems, and finds wide application in both industrial and military systems. Examples of such fault-tolerant [...]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.techfore.2005.05.009 Byline: Jian Cheng Guan (a), Chiu Kam Mok (b), Richard C.M. Yam (b), K.S. Chin (b), Kit Fai Pun (c) Keywords: Technology transfer; Innovation performance; Chinese industrial firms Abstract: This paper explores the relationship between technology transfer activities and innovation performance with special reference to Chinese industrial firms. It is based on a recent nationwide survey covering 2334 Chinese industrial firms. The impact of technology transfer on their innovative performance was analyzed for all the responded firms and similar analyses also addressed the issue from perspectives of general industrial firms versus high-tech firms, and large firms versus small and medium firms. Several critical types of technology transfer activities were identified and both positive and negative impacts were discussed along with the Pearson correlation analysis and stepwise regression analysis. The study revealed that the innovation activities in Chinese manufacturing firms could not be boosted substantially merely through the acquisition of key equipment and apparatus from abroad. The findings also suggested that Chinese firms should develop their technological absorptive capacity and transformative capacity, and foster technology transfer and communications among trading partners. Moreover, it would be crucial for Chinese firms to stress the nurturing of indigenous innovation capabilities so as to sustain their performance improvement. Author Affiliation: (a) School of Management, Beijing University of Aeronautics and Astronautics, Beijing, China (b) Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong (c) Department of Mechanical Engineering, The University of the West Indies, St Augustine Campus, Trinidad, West Indies Article History: Received 27 December 2004; Revised 11 May 2005; Accepted 19 May 2005

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2004.07.054 Byline: Jian Cheng Guan (a), Richard C.M. Yam (b), Chiu Kam Mok (b), Ning Ma (a) Keywords: Data envelopment analysis; Competitiveness; Organizational innovation capability; Controlling projection; Innovative firms Abstract: Researchers and managers have been searching for appropriate methods to explore the relationship between technological innovation capability and competitiveness in recent years. This study attempts to find a systematic quantitative methodology to tackle this problem. In a recent survey covering 182 industrial innovative firms in China, the traditional data envelopment analysis (DEA) model was employed to analyze the data collected. The research results show that only 16% of the enterprises operate on the best-practice frontier and there are some inconsistencies between organizational innovation capability and competitiveness in many enterprises. Decreasing returns to scale were found among about 70% of the inefficient enterprises and increasing returns to scale were found among the remaining 30% of the inefficient enterprises. Thus the internal innovation harmonizing process in these enterprises is considerably inefficient. Based on the restricted ranges of the input/output factors, a multi-objective DEA projection model has also been developed in this study to provide a benchmark for auditing competitiveness. Research results further indicate that there is still much room for enterprises to improve competitiveness in situations of confining score ranges of technological innovation capability and competitiveness. Author Affiliation: (a) School of Management, Beijing University of Aeronautics and Astronautics, Beijing, China (b) Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong Article History: Received 22 September 2003; Accepted 13 July 2004

Three strategies for reliability modelling of continuous state devices are shown. One employs the random process to fit model parameters of a statistical distribution as functions of time. The second method employs the general path model to fit parameters of the model as functions of time. The third techniques utilizes multiple linear regression to fit the distribution of lifetime directly. All three techniques are demonstrated by examples.