Upper and lower reasonability bounds for instrument failure rates can be established using a large set of FMEDA analysis results. These numbers can be used as a benchmark to help validate any failure data collection process. For any set of collected field failure data for a device, a λ of the device is calculated and compared to the benchmark. It is not uncommon for the benchmark λ and estimated λ to differ considerably. Predictive Analytics (PA) is a procedure for exploring these differences and assessing the accuracy of the estimated device λ with respect to the benchmark λ of the device. The PA process often results in improved data collection methods and more accurate field failure data.
This web seminar describes the PA method and the procedures used to validate field failure data collection processes.
Dr. Goble has over 40 years of professional experience. He is widely recognized as an expert in programmable electronic systems analysis, safety and high availability automation systems, automation systems new product development and market analysis. He developed many of the techniques used for probabilistic evaluation of safety and high availability automation systems. He has published many papers and magazine articles. Dr. Goble has a BSEE from Penn State, a MSEE from Villanova and a PhD from Eindhoven University of Technology in Eindhoven, Netherlands.