Probabilistic qualitative analysis for fault detection and identification of an on-line phosphate analyzer

On-line, real-time collection of measurements remains a key challenge in water quality monitoring and control due to unknown and varying quality of on-line sensor data. Today’s data quality assessment is typically based on a comparison of sensor-based measurements and grab samples of the sampled solution taken next to the on-line analyzer and analyzed in a laboratory. In this work, internal data is used for fault detection and identification of a phosphate analyzer to inspect the measuring process itself. These internal data is shown to be information-rich with respect to the analyzer’s status. Furthermore, this information is captured well by means of a newly developed method for qualitative analysis of time series. This method was developed with global optimality in mind and therefore lends itself to a probabilistic assessment of the qualitative representation of time series.