SimSPRT-II: Monte Carlo Simulation of Sequential Probability Ratio Test Algorithms for Optimal Prognostic Performance

摘要

New prognostic AI innovations are being developed, optimized, and productized for enhancing the reliability, availability, and serviceability of enterprise servers and data centers, known as Electronic Prognostics (EP). EP prognostic innovations are now being spun off for prognostic cyber-security applications, and for Internet-of-Things (IoT) prognostic applications in the industrial sectors of manufacturing, transportation, and utilities. For these applications, the function of prognostic anomaly detection is achieved by predicting what each monitored signal "should be" via highly accurate empirical nonlinear nonparametric (NLNP) regression algorithms, and then differencing the optimal signal estimates from the real measured signals to produce "residuals". The residuals are then monitored with a Sequential Probability Ratio Test (SPRT). The advantage of the SPRT, when tuned properly, is that it provides the earliest mathematically possible annunciation of anomalies growing into time series signals for a wide range of complex engineering applications. SimSPRT-II is a comprehensive parametric monte-carlo simulation framework for tuning, optimization, and performance evaluation of SPRT algorithms for any types of digitized time-series signals. SimSPRT-II enables users to systematically optimize SPRT performance as a multivariate function of Type-I and Type-II errors, Variance, Sampling Density, and System Disturbance Magnitude, and then quickly evaluate what we believe to be the most important overall prognostic performance metrics for real-time applications: Empirical False and Missed-alarm Probabilities (FAPs and MAPs), SPRT Tripping Frequency as a function of anomaly severity, and Overhead Compute Cost as a function of sampling density. SimSPRT-II has become a vital tool for tuning, optimization, and formal validation of SPRT based AI algorithms for applications in a broad range of engineering and security prognostic applications.