A Quantitative Performance Index for Model-Based Monitoring Systems

Model-based Monitoring (MBM) systems based on state observer theory are attractive for machine monitoring because practical, inexpensive, and reliable sensors can be located remote to the signal(s) of interest. Then, a model of the machine plus an estimation algorithm convert the output of the remote sensors to signals representing the desired local behavior. While this type of monitoring system has shown much promise in the laboratory, it has not been widely accepted by industry because, in practice, these systems often have poor performance with respect to accuracy, bandwidth, reliability (false alarms), and robustness. In this paper, the limitations of the deterministic state observer are investigated quantitatively from the machine monitoring viewpoint. The limitations in the transient and steady-state observer performance are quantified by the estimation error bounds, and from these error bounds performance indices are selected. Then, based on the relationships between the indices, a main index is determined in order to represent the overall observer performance. This index could form the basis for an observer design methodology that should improve the performance of model-based monitoring systems.