Weibull Analysis: Tableau + R Integration

Weibull reliability analysis predicts the life of products by fitting a distribution to a plot based on a population of units; multiple proprietary software applications are available to perform the analysis. The advent of Tableau + R Integration empowers Data Scientists and Reliability experts to make inferences drawn on populationsäó» failure characteristics by considering the ‘- value of the distribution. With ‘, we plot F(t), or unreliability over time, when leveraging Tableau + R Integration (Rscripts in Tableau calculated fields, pointing to R Server library for row-level execution). The Weibull analysis performed is superior to the Kaplan-Meiers method as it enables the more accurate Maximum Likelihood Estimate (MLE) curve fitting of plotted regression as opposed to Least Squares Estimate (LSE), which excludes R Integration and fails to precisely match parameters (shape, slope) that sophisticated existing reliability software packages produce. Application of Weibull for reliability analysis considers failure for given time in lifespan (t) when t= miles, cycles, hours, etc. The two-parameter distribution performed in this analysis includes beta and eta, or shape and scale parameters, respectively. Mean Time To Failure (MTTF) calculations are derived from these parameters as well. Variable Confidence Interval (CI) bands are used and can be adjusted using the interactive Tableau visualization. Industries utilizing Weibull analysis to plot Bathtub Curve assess the infant mortality, normal useful life, and end of life failures anticipated for a product (e.g. semiconductor chips, automotive parts, medical devices).