An Autonomous Diagnostics and Prognostics Framework for Condition-Based Maintenance

This paper presents an innovative on-line approach for autonomous diagnostics and prognostics. It overcomes limitations of current diagnostics and prognostics technology by developing a "generic" framework that is relatively independent of the type of physical equipment under consideration. Proposed diagnostics and prognostics framework (DPF) is based on unsupervised learning methods (reducing the need for human intervention). The procedures used in DPF are designed to temporally evolve the critical parameters with monitoring experience for enhanced diagnostic/prognostic accuracy (a critical ability for mass deployment of the technology on a variety of equipment/ hardware without needing extensive initial tune-up). This framework is currently under deployment in a major automotive manufacturing plant in Michigan, USA. Results from this pilot program to date are very satisfactory.