Life cycle reliability assessment of new products—A Bayesian model updating approach

https://doi.org/10.1016/j.ress.2012.12.002Get rights and content

Abstract

The rapidly increasing pace and continuously evolving reliability requirements of new products have made life cycle reliability assessment of new products an imperative yet difficult work. While much work has been done to separately estimate reliability of new products in specific stages, a gap exists in carrying out life cycle reliability assessment throughout all life cycle stages. We present a Bayesian model updating approach (BMUA) for life cycle reliability assessment of new products. Novel features of this approach are the development of Bayesian information toolkits by separately including “reliability improvement factor” and “information fusion factor”, which allow the integration of subjective information in a specific life cycle stage and the transition of integrated information between adjacent life cycle stages. They lead to the unique characteristics of the BMUA in which information generated throughout life cycle stages are integrated coherently. To illustrate the approach, an application to the life cycle reliability assessment of a newly developed Gantry Machining Center is shown.

Introduction

Modern industrial societies have been characterized by the ever-increasing pace of new products appearing on the market. There is also an ever-increasing reliability requirement for these newly developed products. To deliver a new product with high reliability, it is necessary for the companies/manufacturers to track and manage its reliability throughout its life cycle. This requires coherent reliability assessment of the new product as life cycle stages move on. Accordingly, a life cycle reliability assessment approach is needed to cope with the rapidly increasing pace and continuously evolving reliability requirements of new products. Generally, the life cycle reliability assessment of a new product requires effective use of different types of data and information available throughout the life cycle. However, as the advancement of modern technology and the aggravation of market competition continue, available data for reliability assessment of a new product are extremely sparse and sometimes contain subjective information. As a result, it is impossible to obtain an accurate estimation of reliability using classical methods, which generally pertain to large sample sizes or abundant reliability data.

Alternatively, the Bayesian method is becoming more accepted in reliability engineering. Numerous articles have discussed the reliability assessment with different data types and reliability information, which form the foundation of life cycle reliability assessment. Walls and Quigley [30], Seth [29], Yadav et al. [37], and Augustine et al. [2] have proposed specific methodologies to deal with subjective information in reliability assessment. Hamada et al. [14], and Graves et al. [13] developed hierarchical Bayesian methods for assessing system reliability with multilevel binomial data. Huang et al. [16], Briand and Huzurbazar [5], Xu and Tang [36], Zhong et al. [39], and Reese et al. [27] have presented models and approaches for reliability assessment with lifetime data for different system structures subjected to various reliability information situations. Furthermore, Ching and Leu [6] developed a framework for estimating time-varying reliabilities with condition-state data sets. Wang et al. [32] presented a Bayesian updating mechanism to deal with reliability assessment with evolving, insufficient, and subjective data sets. Wilson et al. [33] and Anderson-Cook [1] described Bayesian approaches for reliability assessment of complex systems by combining multilevel heterogeneous binomial data, lifetime data, and degradation data.

These models and methodologies have formed a solid foundation for life cycle reliability assessment of new products. Further adopting the Bayesian approach for reliability assessment of new products in different life cycle stages, the following papers have appeared in the literature. Yadav et al. [38] proposed a framework for capturing subjective information from different sources for reliability assessment in the development stage of new products. Johnson et al. [19] applied the hierarchical Bayesian model to assess the reliability of complex products in the early in-service stage. Xing et al. [35] proposed a dynamic Bayesian evaluation method for reliability evaluation of a binomial system throughout the development stage. Quigley and Walls [17] proposed a coherent inference framework for reliability estimation during the product development stage by considering both Bayes and empirical Bayes inferences. Considering the Bayesian method in the study of product life cycle, Ho and Huang [15] presented a Bayesian decision model to assist the optimal life decision for new products during the life cycle.

These papers concentrated exclusively on specific life cycle stages for particular types of products. However, little attention has been given to the life cycle reliability assessment of new products. Traditionally, product reliability assessment in a specific life cycle stage is investigated, e.g., the development stage (Yadav et al., [38]) and the early launch stage (Johnson et al., [19]). Each of these methods is effective in a specific life cycle stage for particular types of products. However, when dealing with life cycle reliability assessment of new products, the applications of these methods face difficulties, and there is no systematic approach reported. Since the existing methods are based on different assumptions for different types of products, they can hardly be combined, and the information obtained in different life cycle stages can hardly be integrated. Moreover, this inconsistency between these models may lead to inaccurate estimations and result in poor design or unnecessary investment for new products.

In this paper, a comprehensive Bayesian model updating approach (BMUA) is proposed to deal with life cycle reliability assessment of new products. The BMUA consists of an information integration framework, two Bayesian information toolkits, and the corresponding Bayesian reliability models. Three critical aspects are highlighted in the proposed BMUA: (1) life cycle stages are investigated as a whole; (2) reliability models developed in different life cycle stages are interrelated; and (3) information generated in different life cycle stages is integrated and transited comprehensively under the information integration framework with the Bayesian models and information toolkits.

The paper is organized as follows. The general Bayesian model updating approach is presented in Section 2 with specific descriptions of the framework and critical steps. Two indispensable information toolkits for the BMUA are developed in Section 3. In Section 4, an application example of the proposed BMUA is illustrated for the life cycle reliability assessment of a newly developed Gantry Machining Center. We then summarize the proposed BMUA in Section 5.

Section snippets

A general Bayesian model updating approach

The life cycle of a new product consists of multiple stages. In particular, the electronic and manufacturing industries have demonstrated the multiple-stage nature of product life cycles. We note that there are many ways to define the multiple stages of a new product's life cycle ([40], [21]). Both marketing literature [11] and production literature have developed relative ways to deal with the multiple life cycle stages for particular problems, e.g., Cooper and Kleinschmidt [9] and Cohen et

Bayesian information toolkits for the BMUA

Clearly, derivation of prior distributions is critical for the information integration framework and Bayesian models in the proposed BMUA. Both Fig. 2 and the steps of the BMUA highlight that the connection between adjacent life cycle stages is constructed through a coherent information transition. This is implemented by derivation of prior distributions for the Bayesian models through quantification, integration, and transition of subjective information throughout the life cycle. Accordingly,

A case study

A newly developed Gantry Machining Center (GMC) by company M is used to demonstrate the application of the BMUA. To launch a newly developed GMC with high reliability, company M needs to track and manage the reliability of the GMC throughout its life cycle. In this section, the BMUA procedure is carried out stage by stage for the life cycle reliability assessment of the newly developed GMC to illustrate its application in detail.

Conclusions

A Bayesian model updating approach is developed to deal with life cycle reliability assessment of new products. The BMUA is constructed with an information integration framework, two information toolkits, and three Bayesian models. The information generated throughout the life cycle stages is integrated and transited under the information integration framework. The Bayesian models and the information toolkits are adopted to implement the information integration and transition throughout the

Acknowledgments

This research was supported by the National Natural Science Foundation of China under contract number 51075061, the Natural Sciences and Engineering Research Council of Canada (NSERC), and the City University of Hong Kong (Project No.9380058). An earlier and much shorter version of this paper has been accepted by PSAM11/ESREL 2012. Comments and suggestions from the reviewers and the Editor are very much appreciated.

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