Abstract: A method for condition monitoring of distributed drive-trains using Bayesian data fusion approach for measured data includes measurement of physical signals obtained from sensors attached to the components being chosen from the drive-train which are delivered to the computer means for processing the measured data and performing data fusion processes, using a data from information database containing at least one information system. The method is characterized by comprising two stages for data fusion processes performed by using Bayesian Inference, the first one for local data fusion process and the second one for global data fusion process, and on the basis of the second stage the assessment process of the condition of the drive-train is performed by choosing the maximum value of the received data, which maximum value serves as an indicator for the most likely fault present in the drive-train.

Abstract: A method for condition monitoring of distributed drive-trains using Bayesian data fusion approach for measured data includes measurement of physical signals obtained from sensors attached to the components being chosen from the drive-train which are delivered to the computer means for processing the measured data and performing data fusion processes, using a data from information database containing at least one information system. The method is characterized by comprising two stages for data fusion processes performed by using Bayesian Inference, the first one for local data fusion process and the second one for global data fusion process, and on the basis of the second stage the assessment process of the condition of the drive-train is performed by choosing the maximum value of the received data, which maximum value serves as an indicator for the most likely fault present in the drive-train.