Abstract
In practices, most industrial products are subject to sudden failure and only failure information can be collected, which presents a great challenge for reliability prediction of modern devices. To address this issue, our paper proposes a dynamic reliability estimation and control for industrial products under regular failure trials. The failure trial is performed at different operational time points of the products, which provides sole data source for evaluating the status of industrial products. We use Bayesian approach to dynamically estimate the industrial products when the failure trial is available. The estimated reliability is updated using a point estimate with new available data. To maintain the reliability of products at a desirable status, a reliability control method is presented to monitor the confidence interval of reliability distribution. The lower limit of confidence interval is maintained above a control limit, which indicates that a corresponding quality-assurance action is preferable. The proposed reliability estimation and control approach is demonstrated using a case of light-emitting diodes under failure trials at production process. The obtained results indicate the effectiveness of our estimation and control model.
Highlights
With development of industrial engineering, small factories are becoming large-scale plants, which prompts the applications of prognostics [1]
When we obtained estimated reliability level of industrial products, we rarely have useful policy to improve the reliability during the production process, where the reliability control approach is very much required in quality control process [7]. erefore, this paper aims to propose a reliability estimation and control approach to address this problem existing in modern industries
E consciousness of reliability applications has been noticeably improved in the recent years [8,9,10]. e reliability evaluation system can ensure many critical and heavy-duty machines with a relatively safe operating conditions, and it has become an indispensable part of modern industry [11]. is system is widely used in manufacturing engineering, aviation, and nuclear industries [11, 12]
Summary
With development of industrial engineering, small factories are becoming large-scale plants, which prompts the applications of prognostics [1]. To fully use limited testing data in the production process, in this paper, we use a Bayesian procedure to dynamically estimate the reliability of the industrial products and present a reliability control method to maintain the lower limit of prediction interval above a certain level. E main contributions of this work are as follows: (1) Interpretation of the past and present failure trials using inconstant general distribution, (2) development of a dynamic estimation procedure for updating products reliability, (3) development of a novel reliability control scheme based on confidence interval, and (4) validation of the improved performance using a real case study. To make full use of the failure trials data, we consider the Bayesian approach to use the discrete trial information to dynamically update the reliability of the tested products, and the procedure is presented
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