UnQuantize: Overcoming Signal Quantization Effects in IoT Time-Series Databases

Low-resolution quantized time-series signals present a challenge to big-data Machine Learning (ML) prognostics in IoT industrial and transportation applications. The challenge for detecting anomalies in monitored sensor signals is compounded by the fact that many industries today use 8-bit sample-and-hold analog-to-digital (A/D) converters for almost all physical transducers throughout the system. This results in the signal values being severely quantized, which adversely affects the predictive power of prognostic algorithms and can elevate empirical false-alarm and missed-alarm probabilities. Quantized signals are dense and indecipherable to the human eye and ML algorithms are challenged to detect the onset of degradation in monitored assets due to the loss of information in the digitization process. This paper presents an autonomous ML framework that detects and classifies quantized signals before instantiates two separate techniques (depending on the levels of quantization) to efficiently unquantize digitized signals, returning high-resolution signals possessing the same accuracy as signals sampled with higher bit A/D chips. This new “UnQuantize” framework works in line with streaming sensor signals, upstream from the core ML anomaly detection algorithm, yielding substantially higher anomaly-detection sensitivity, with much lower false-alarm and missed-alarm probabilities (FAPs/MAPs).