Mini Tutorial 2: A Tour of JMP Reliability Platforms and Bayesian Methods for Reliability Data скачать в хорошем качестве

Mini Tutorial 2: A Tour of JMP Reliability Platforms and Bayesian Methods for Reliability Data

Peng Liu is a Principal Research Statistician Developer at JMP Statistical Discovery LLC. He holds a Ph.D. in statistics from NCSU. He has been working at JMP since 2007. He specializes in computational statistics, software engineering, reliability data analysis, reliability engineering, time series analysis, and time series forecasting. He is responsible for developing and maintaining all JMP platforms in the above areas. He has broad interest in statistical analysis research and software product development. JMP is a comprehensive, visual, and interactive statistical discovery software with a carefully curated graphical user interface designed for statistical discovery. The software is packed with traditional and modern statistical analysis capabilities and many unique innovative features. The software hosts several suites of tools that are especially valuable to the DATAWorks’s audience. The software includes suites for Design of Experiments, Quality Control, Process Analysis, and Reliability Analysis. JMP has been building its reliability suite for the past fifteen years. The reliability suite in JMP is a comprehensive and mature collections of JMP platforms. The suite empowers reliability engineers with tools for analyzing time-to-event data, accelerated life test data, observational reliability data, competing cause data, warranty data, cumulative damage data, repeated measures degradation data, destructive degradation data, and recurrence data. For each type of data, there are numerous models and one or more methodologies that are applicable based on the nature of data. In addition to reliability data analysis platforms, the suite also provides capabilities of reliability engineering for system reliability from two distinct perspectives, one for non-repairable systems and the other for repairable systems. The capability of JMP reliability suite is also at the frontier of advanced research on reliability data analysis. Inspired by the research by Prof. William Meeker at Iowa State University, we have implemented Bayesian inference methodologies for analyzing three most important types of reliability data. The tutorial will start with an overall introduction to JMP’s reliability platforms. Then the tutorial will focus on analyzing time-to-event data, accelerated life test data, and repeated measures degradation data. The tutorial will present analyzing these types of reliability data using traditional methods, and highlight when, why, and how to analyze them in JMP using Bayesian methods.