Adaptive quantization and fault detection in smart sensors

Abstract

This paper presents an adaptive maximum entropy quantizer for fault detection in smart sensors. An unsupervised learning rule adapts the quantization thresholds for obtaining equal-probability quantization intervals, therefore giving a reliable non-parametric density estimation. Hypothesis testing based on the likelihood ratio is then applied for detecting abrupt changes in the signal distribution which reflects faulty sensor operations.