A study on the reliability of equipment system through case-study on the manufacture of machinery/electronic equipment using practical QRM (quality, reliability, maintenance) process and evaluation index

Abstract

Reliability evaluation approaches to equipment systems consisting of hundreds of parts and dozens of modules have limitations with respect to environmental testing, life testing, and accelerated life testing. Equipment systems cannot be tested with a statistically verifiable sample number. Therefore, screening methods used by manufacturers involve short operational tests after production; failures occurring in the field are dealt with after delivery. The frequency of maintenance checks is closely related to the company's reputation and life cycle costs (LCCs). The aim of this study is to investigate ways to reduce maintenance cost and high LCCs by the company's reliability related processor. First, we classified equipment industries into three categories: machine processing, semiconductor processing, and inspection. Then, we investigated user requirements for equipment systems. We found that there were different demands for each industry. For equipment used in the machine processing industry, capability (productivity, adaptability, flexibility, and precision) and reliability (reliability of drive system, bearing failure rate) are considered (Jo-Peng et al., 2010 [1]). Equipment used in the semiconductor process is required to be employed for immediate use; further, they are required to be highly accurate. Inspection equipment is usually manufactured by small companies; therefore, they have problems associated with quality and initial failures. Next, we proposed the practical quality, reliability, and maintenance (QRM) processes to reduce high LCCs for four years. Before implementing the processes, we developed and applied an evaluation index that evaluated the quality management status, reliability, and maintenance aspects of the company. The index was designed by the behaviorally anchored rating scale (BARS) technique, which is a quantitative calculation method (Martin-Raugh et al., 2016). The results showed that companies could reduce their LCCs and improve their reliability.