Optimal fault detection for coarsely quantized systems

In this work an optimal algorithm is presented for bounding dependencies in coarsely quantized systems for the purposes of fault detection. Coarse quantization implies the need to analyze signals for which the standard Δ²/12 approximation for quantization noise cannot be applied. This creates significant difficulties when it comes to retrofitting analytic fault detection and identification (FDI) approaches to existing large-scale engineering plants that have coarsely quantized status data, particularly in situations when no system models exist and/or it is cost-prohibitive to replace the exist status data sampling sub-systems. No methodology has been reported in the literature for optimal fault detection for the case of coarsely quantized signals. This work presents such an optimal approach. The theoretical results are operational validated by applying the algorithm to perform fault detection on one year´s data obtained from a real-world large-scale engineering plant.