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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for condition monitoring of a distributed drive-train comprising measurement of physical signals obtained from sensors attached to the components being chosen from the drive-train, said signals are delivered to the computer means for processing the measured data and performing data fusion processes, using a data from an information database containing at least one information system, characterized in that it comprises two stages of data fusion processes, the first stage consists of the determination of the local posterior probability of fault pc(ti,sc(ti)) of the individual components of the of the drive-train, using Bayesian Inference according to a formula: p c  ( t i , s c  ( t i ) ) = 1 A  L c  ( y c  ( t i )  s c  ( t i ) )  p c -  ( t i , s c  ( t i ) ) where: ti is a time instant, sc(ti) is a component condition s at a time instant ti, c: 1,2,3, . . . , C, and C is the number of components, pc−(ti,sc(ti)) is the prior probability of fault for the individual component c, Lc(yc(ti)|sc(ti)) is a component likelihood function CLF for the individual component c, yc(ti) is the vector of indicator values of the component c at the time instant ti, A is a normalizing constant, and the second stage which consists of on the determination of a global posterior probability of fault p(ti,S(ti)) of the drive-train, using Bayesian Inference according to a formula: p  ( t i , S  ( t i ) ) = 1 B  L  ( Y  ( t i )  S  ( t i ) )  p -  ( t i , S  ( t i ) ) where: ti is a time instant, S(ti) is a global condition S of the drive-train in the time instant ti, p−(ti,S(ti)) is the prior probability of fault for the drive-train, Y(ti) is the vector of local posterior probabilities of fault for all the components in the drive-train, at the time instant ti, L(Y(ti)|S(ti)) is the global likelihood function GLF for the drive-train, this GLF is obtained by multiplying a different set of overall likelihood functions OLF of the different components Πc=1C Lc(Yc(ti)|S(ti)), where Yc(ti) is equal to the local posterior probability of fault for the component c defined as pc(ti,sc(ti)), B is a normalizing constant, and next on a base of determined values of global posterior probability of fault p(ti,S(ti)) assessing the condition of the drive-train by choosing the maximum value from the global posterior probability of fault p(ti,S(ti)) which maximum value serves as an indicator for the most likely fault present in the drive-train and triggers an alarm for drive train. 2. A method according to claim 1, characterized in that the prior probability of fault for the individual component (c), pc−(ti,sc(ti)) is calculated using the component last known condition and a component fault development model. 3. A method according to claim 1, characterized in that the prior probability of fault for the drive-train, p−(ti,S(ti)) is calculated using a global last known condition and a global fault development model. 4. A method according to claim 1, characterized in that the information system, contained in the information data base is an Expert Knowledge System EKS, a Component Life Time Estimation System CLTES, an Equipment Operating Condition Information System EOCS, or a combination between them. 5. A method according to claim 1, characterized in that the global likelihood function GLF is calculated in a global data fusion module for at least two components from the drive-train. 6. A method according to claim 5, characterized in that the data for the global likelihood function GLF is calculated from signals measured and processed for a motor and a gearbox or a load or their combination. 7. A computer program for condition monitoring of distributed drive-trains, which is loadable into and executable in the computer means having a processor, characterized in that during program run it executes the method according to claim 1.