A method and an apparatus for operating an automation system is provided. The method for operating an automation system includes the method steps of: providing a learning-based prediction model for the automation system trained by process data including context of an automation process, receiving information about current context of the automation process, verifying context change by comparing the current context to the context of said process data, in the case of any context change verifying a concept drift by comparing pre-drift process data and post-drift process data, in the case of any concept drift re-training said model with post-drift process data, in the case of no context change testing for random concept drift not detected by verifying context change, in the case of any random concept drift extend the current context by using data comprising previous context changes, otherwise no further method steps are required.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for operating an automation system comprising the method steps of: a) providing a learning-based prediction model for the automation system trained by process data comprising context of an automation process, b) receiving information about current context of the automation process, c) verifying context change by comparing the current context to the context of said process data, d) in the case of any context change verifying a concept drift by comparing pre-drift process data and post-drift process data, e) in the case of any concept drift re-training said model with post-drift process data, f) in the case of no context change testing for random concept drift not detected by verifying context change. g) in the case of any random concept drift extend the current context by using data comprising previous context changes, h) otherwise no further method steps are required. 2. The method according to claim 1, wherein if such a random concept drift is detected without context change, the current context is extended with adjacent data stored in a context knowledge base. 3. The method according to claim 2, wherein the context knowledge base comprises a context knowledge history. 4. The method according to claim 1, wherein context is represented by features of a piece of product and/or automation process steps and/or in the automation process used apparatus. 5. The method according to claim 1, wherein the current context is automatically extended on the basis of the context's influence on the automation process performance. 6. The method according to claim 1, wherein the model (M) keeps track of a specified subset of a set of context. 7. The method according to the preceding claim 6, wherein the initially tracked, specified subset only contains context instances that are directly associated with process data used for its training. 8. The method according to claim 1, where-in the prediction model represents a fault prediction model. 9. An apparatus for operating an automation system comprising: a) means for obtaining a learning-based prediction model for the automation system trained by process data comprising context of an automation process, b) means for receiving information about current context of the automation process, c) means for verifying context change by comparing the current context to the context of said process data, d) means for verifying a concept drift by comparing pre-drift process data and post-drift process data in the case of any context change, e) means for providing post-drift process data for re-training said model in the case of any concept drift. f) means for testing for random concept drift not detected by verifying context change in the case of no context change, g) means for extending the current context by using data comprising previous context changes in the case of any random concept drift. 10. The apparatus according to claim 9, wherein the apparatus comprises a context knowledge base. 11. The apparatus according to claim 10, wherein said context knowledge base comprises a context knowledge history. 12. The apparatus for training a learning-based prediction model for an automation system comprising: a) means for providing a learning-based prediction model for the automation system trained by process data. comprising context of an automation process, b) means for receiving post-drift process data for re-training said model in the case of any concept drift which is tested by an apparatus according to claim 9. 13. The apparatus according to claim 9, where in the prediction model represents a fault prediction model. 14. A computer program product, comprising a computer readable hardware storage device having computer readable program code stored therein, said program code executable by a processor of a computer system to implement a method directly loadable into the internal memory of a computer, comprising software code portions for performing the steps of claim 1 when said computer program product is running on a computer.