Distributed fault detection for large-scale dynamic systems

Abstract

As the number of aircraft and satellites grows, continuous tracking has become increasingly critical. Monitoring these vehicles in flight could prevent catastrophic failures and reduce maintenance and downtime costs. One possible solution is to use lightweight, low-power, and rugged sensors to check thousands, or even millions, of measurement points.1 Such detection might reveal failures early enough to prevent disasters, and could provide truly predictive awareness data on the state of a vehicle. Increasing demands for robustness, cost-efficiency, and reliability have fueled interest in fault detection for dynamic systems.2, 3 Most of the existing strategies are centralized, which means that all sensing data is collected and processed at one unit. This not only causes excessive communication and computational burdens, but also creates a single point of failure. To get around these problems, we have developed large-scale distributed detection and data fusion methods. Our approach provides localized information processing at the sensor level, reducing network bandwidth, validating sensor data and integrity, and standardizing formatting and reporting. We considered the distributed fault detection problem, in which multiple sensors monitor the dynamic state of the system (see Figure 11). Normal and faulty behaviors can be modeled as two hypotheses. Due to communication constraints, our model assumes that sensors can only send binary data to the fusion center. We designed local detector and decision fusion rules to minimize the probabilities of missed fault detection and false alarms. We chose a quantitative analytical modeling approach because it is more amenable to performance analysis. With the dynamic system state-space model, we can predict the state under both normal and faulty hypotheses using knowledge of past observations. For linear and Gaussian systems, the conventional Kalman filter (KF) is optimal for prediction. Models capturing observational noise and the evolution of the system, however, may have complex nonlinearity and non-Gaussian distributions, precludFigure 1. System diagram for distributed fault detection. xt: System dynamic state at time t. yM t : Local observation at time t and sensor M. ut: Local decisions at time t and sensor M. ut: Global decision at time t.