In accordance with an implementation of the present technique, a method for processing data is disclosed. The method involves analyzing consistency of sensor readings obtained from a plurality of sensors monitoring a device, where each sensor reading is indicative of at least one operational parameter of the device. The method also involves assigning a confidence value to each of the sensor readings, where the confidence value is indicative of an operational condition of one of the plurality of sensors that provided the sensor reading. The method also includes weighting each sensor reading based on the confidence value assigned to the sensor reading to determine an acceptance or a rejection of the sensor reading and fusing the sensor readings that are accepted to obtain a fused sensor reading corresponding to the operational parameter measured by the sensors on the device.

The invention relates to a method for an analysis tool for analysis of the sensor performance of a system of sensors, which method comprises analytical calculation of a sensor system's measurement characteristics at each point in a given geographical area. The method comprises obtaining performance parameters from N sensors that are in the system. The method is characterized in that a set of analytical performance parameters for the system is calculated by the performance parameters being fused irrespective of the different measurement characteristics of the sensors in the system with regard to the given performance parameters and in that the analytical parameters are used in the analysis of the performance of the sensor system. The invention also relates to a device for use of the method and to the use of the method and the device.

A multi-sensor data fusion system and method provides adaptive weighting of the contributions from a plurality of sensors in the system using an additive calculation of a sensor reliability function for each sensor. During a predetermined tracking period, data is received from each individual sensor in the system and a sensor reliability function is determined for each sensor based on the SNR (signal-to-noise ratio) for the received data from each sensor. Each sensor reliability function is individually weighted based on the SNR for each sensor and a comparison of predetermined sensor operation characteristics for each sensor and a best performing (most reliable) sensor. Additive calculations are performed on the sensor reliability functions to produce both an absolute and a relative reliability function which provide a confidence level for the multi-sensor system relating to the correct classification (recognition) of targets and decoys.

A system and method for combining conclusions from multiple fault detection techniques to isolate likely faults in a turbine engine is provided. The system and method provide the ability to effectively deal with multiple concurrent faults in the engine. Additionally, the embodiments of the invention provide the ability to correctly characterize multiple conclusions generated from evidence having different levels of interdependence. In one embodiment, the conclusions based on device data with high dependency are aggregated using a high dependency aggregation rule, and the resulting high-dependency sets are then further aggregated using a weak dependency rule. Finally, any conclusions based on independent evidence can be aggregated using an independent combination rule. The resulting aggregation determines which fault(s) are most likely indicated by the plurality of conclusions, taken into account the dependency of the device data used to generate the conclusions.

A method of data fusion comprises the steps of: receiving a plurality of decisions and associated confidences; determining probabilities of a set of the decisions; if the probabilities are not zero, then choosing the decision having a highest likelihood from the plurality of decisions; and if the probabilities are zero, then selecting an algorithm to be used for choosing one of the decisions.