Using statistically designed experiments to improve reliability and to achieve robust reliability

This tutorial explains statistically designed experiments which provide a proactive means to improve reliability as advocated by Genichi Taguchi. That is, by systematic experimentation, the important parameters (factors) affecting reliability can be identified along with parameter values that yield reliability gains. In addition to improving reliability, Taguchi's robust design can be used to achieve robust reliability; that is, to make a process or product reliability insensitive to factors which are hard or impossible to control. Robust design is also implemented using statistically designed experiments. This paper presents classes of experimental plans for reliability improvement and robust reliability. An important feature of the reliability data collected from such experiments is censoring which occurs when some of the experimental units have not failed by the end of the experiment. Consequently, the analysis methodology must account for these censored data which are likely to occur in light of the ever increasing reliability of today's products. Several appropriate methods are discussed briefly. These experimental plans and analysis methods are illustrated using three documented experiments which improved fluorescent lamp and industrial thermostat reliability and which achieved robust reliability for night-vision goggles