Advanced Control Charts for Effective Monitoring of Time-between-events Data

Project: Research

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Statistical Process Control (SPC) was traditionally established for the manufacturingindustry. In recent years, SPC has gained considerable attention as it has been foundeffective in applications in important areas such as healthcare, education, hospitalityindustry, energy management, and public service.However, for SPC to be effective in non-manufacturing systems, it has to have theability to handle situations associated with multivariate and/or non-normal distribution.In the past, we have investigated time-between-events (TBE) control charts that haveproven to be very effective with reference to the monitoring and control of high qualitymanufacturing processes. In this proposed research, we will develop and investigate someadvanced control charts for TBE and related process characteristics targeted for moregeneralized applications, with a focus on multivariate situations.We will also investigate the modelling and monitoring of extreme-value characteristics,such as those related to peak usage, extreme damage or performance levels in importantareas beyond manufacturing, such as found in service and administrative systems. Animportant application of our research is to develop cost-effective maintenance procedurebased on TBE data monitoring. Almost all existing studies have focused on theunivariate data monitoring system, while most systems/equipment deteriorates withusage and age which will be studied in this project.Usually an event has two dimensions, one is the event magnitude and the other one isthe event frequency and they are traditionally monitored separately. In this proposal, wewill develop and study some monitoring procedures, especially those based on bivariatedistributions that could incorporate the dependence of these two characteristics as well.The PIs are internationally known researchers having extensive experience in statisticalprocess control research. The proposed research is expected to considerably advance thecurrent field of knowledge, providing cutting edge innovation for applications in SPC.The research issues (multivariate monitoring, effects of estimated parameters,monitoring with reliability data, joint monitoring of the frequency and magnitude ofevents, etc.) covered in this proposal are in line with the need for efficient applicationsin the real word as suggested in a very recent review paper by Woodall and Montgomery(2014) in the Journal of Quality Technology.?


Project number9042327
Grant typeGRF
Effective start/end date1/11/1629/10/20

    Research areas

  • Statistical quality control , Weibull distribution , Control charts , time-between-events ,