Role of hidden-Markov models for autonomous diagnostics of cutting tools

Despite considerable advances in sensing instrumentation and IT infrastructure, monitoring and diagnostics technology has not yet found its place in health management of mainstream machinery and equipment. The fundamental reason for this being the mismatch between the growing diversity and complexity of machinery and equipment employed in industry and the historical reliance on “point-solution” diagnostic systems that necessitate extensive characterization of the failure modes and mechanisms. While these point solutions have a role to play, in particular for monitoring highly-critical assets, generic yet adaptive solutions, could facilitate large-scale deployment of diagnostic and prognostic technology. We present the role of hidden-Markov models for autonomous diagnostics. The proposed methods have been tested on a CNC machining test-bed outfitted with thrust-force and torque sensors for monitoring drill-bits.