Hidden-Markov model based sequential clustering for autonomous diagnostics

Despite considerable advances over the last few decades in sensing instrumentation and information technology 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 ldquopoint-solutionrdquo diagnostic systems that necessitate extensive characterization of the failure modes and mechanisms (something very expensive and tedious). While these point solutions have a role to play, in particular for monitoring highly-critical assets, generic yet adaptive solutions, meaning solutions that are flexible and able to learn on-line, could facilitate large-scale deployment of diagnostic and prognostic technology. We present a novel approach for autonomous diagnostics that employs model-based sequential clustering with hidden-Markov models as a means for measuring similarity of time-series sensor signals. The proposed method has been tested on a CNC machining test-bed outfitted with thrust-force and torque sensors for monitoring drill-bits. Preliminary results revealed the competitive performance of the method.