Telemetry Parameter Synthesis System to Support Machine Learning Tuning and Validation

Advanced machine learning (ML) prognostics are leading to increasing Return-on-Investment (ROI) for dense-sensor Internet-of-Things (IoT) applications across multiple industries including Utilities, Oil-and-Gas, Manufacturing, Transportation, and for business-critical assets in enterprise and cloud data centers. For all of these IoT prognostic applications, a nontrivial challenge for data scientists is acquiring enough time series data from executing assets with which to evaluate, tune, optimize, and validate important prognostic functional requirements that include false-alarm and missed-alarm probabilities (FAPs, MAPs), time-to-detect (TTD) metrics for early-warning of incipient issues in monitored components and systems, and overhead compute cost (CC) for real-time stream ML prognostics. In this paper we present a new data synthesis methodology called the Telemetry Parameter Synthesis System (TPSS) that can take any limited chunk of real sensor telemetry from monitored assets, decompose the sensor signals into deterministic and stochastic components, and then generate millions of hours of high-fidelity synthesized telemetry signals that possess exactly the same serial correlation structure and statistical idiosyncrasies (resolution, variance, skewness, kurtosis, auto-correlation content, and spikiness) as the real telemetry signals from the IoT monitored critical assets. The synthesized signals bring significant value-add for ML data science researchers for evaluation and tuning of candidate ML algorithmics and for offline validation of important prognostic functional requirements including sensitivity, false alarm avoidance, and overhead compute cost. The TPSS has become an indispensable tool in Oracle's ongoing development of innovative diagnostic/prognostic algorithms for dense-sensor predictive maintenance applications in multiple industries.