An Assessment of a Sensor Network using Bayesian Analysis Demonstrated on an Inlet Manifold 2019-01-0121
Modern control strategies for internal combustion engines use increasingly complex networks of sensors and actuators to measure different physical parameters. Often indirect measurements and estimation of variables, based off sensor data, are used in the closed loop control of the engine and its subsystems. Thus, sensor fusion techniques and virtual instrumentation have become more significant to the control strategy. With the large volumes of data produced by the increasing number of sensors, the analysis of sensor networks has become more important. Understanding how valuable the information they contain and how well it is extracted through uncertainty quantification will also become essential to the development of control architecture. This paper proposes a methodology to quantify how valuable a sensor is relative to the architecture. By modelling the sensor network as a Bayesian network, we used Bayesian analysis and control metrics to assess the value of the sensor. This was demonstrated on charge mass flow estimation in the inlet manifold. The results suggested that an individual sensor does not add any significant useful information about the variable we are estimating. It is only when we combine sensors that a significant reduction in the uncertainty of the charge mass flow is observed. These results were then used to quantifiably assess how each sensor and variable affects the charge mass flow estimation.
Rhys Comissiong, Thomas Steffen, Leo Shead