Improved identification of a process anomaly in a technical facility includes training a self-organizing map using the historical process data as good states of the facility. The good states are used to determine a temporal sequence or a path of node hits and the tolerances of neuron hits. Threshold values are determined and stored for a Euclidean distance for the good states. The Euclidean distance of a current state vector to the neuron hit is checked as to whether the threshold value has been exceeded. The path determined is used to determine the neuron to be hit as long as the threshold value was not already exceeded in the check on the relevant neuron. A symptom vector from the current state vector and either the neuron hit or the neuron that is to be hit is determined.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for improved identification of a process anomaly in a technical facility, the method comprising: training a self-organizing map using historical process data as good states of the technical facility in a learning phase; determining and storing reference path regions in a preparation step, wherein the reference path regions are defined with the aid of a temporal sequence of neuron hits in the self-organizing map and tolerances of the neuron hits in the learning phase; storing threshold values for Euclidean distances of the good states to relevant neurons in the self-organizing map in the learning phase; evaluating current process data in the technical facility in the form of a state vector with the aid of the self-organizing map trained in the learning phase; checking the Euclidean distances of the current state vector to the neuron hit as to whether the threshold value determined in the learning phase is exceeded; checking a current path by comparison with the reference path regions determined in the learning phase and definition of a neuron to be hit with the aid of the relevant reference path region as long as the threshold value was not already exceeded in the check with the neuron hit; and determining a symptom vector from the current state vector and either the neuron hit or the neuron that is to be hit, wherein the determining of the symptom vector is performed taking into account the threshold value of the relevant neuron, and wherein a symptom vector different from a zero vector flags the process anomaly, and the symptom vector specifies the process anomaly in more detail. 2. The method of claim 1, further comprising: acquiring and storing the respective duration of the good states in addition to the sequence of neuron hits along a path in the learning phase; and using the duration the good states is additionally used to check the duration of the current states and thereby to identify a process anomaly as long as the threshold value was not exceeded and the path did not deviate during the evaluation of the current process states. 3. The method of claim 1, further comprising: sampling processes of the technical facility at discrete regular time intervals; acquiring and storing the number of hits of a respective neuron instead of the respective duration of the good states; and performing a check with the aid of the respective number of hits with respect to the current states instead of a check on the duration of the current states. 4. The method of claim 1, after the training, further comprising: optimizing the self-organizing map; and automatically calculating and storing the threshold values for the individual nodal points by a re-evaluation of good states. 5. The method of claim 1, further comprising applying a tolerance value to the threshold values. 6. The method of claim 2, further comprising: sampling processes of the technical facility at discrete regular time intervals; acquiring and storing the number of hits of a respective neuron instead of the respective duration of the good states; and performing a check with the aid of the respective number of hits with respect to the current states instead of a check on the duration of the current states. 7. The method of claim 2, after the training, further comprising: optimizing the self-organizing map; and automatically calculating and storing the threshold values for the individual nodal points by a re-evaluation of good states. 8. The method of claim 3, after the training, further comprising: optimizing the self-organizing map; and automatically calculating and storing the threshold values for the individual nodal points by a re-evaluation of good states. 9. The method of claim 2, further comprising applying a tolerance value to the threshold values. 10. The method of claim 3, further comprising applying a tolerance value to the threshold values. 11. The method of claim 4, further comprising applying a tolerance value to the threshold values. 12. A computer program product comprising a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium storing instructions executable by a computer for improved identification of a process anomaly in a technical facility, the instructions comprising: training a self-organizing map using historical process data as good states of the technical facility in a learning phase; determining and storing reference path regions in a preparation step, wherein the reference path regions are defined with the aid of a temporal sequence of neuron hits in the self-organizing map and tolerances of the neuron hits in the learning phase; storing threshold values for Euclidean distances of the good states to relevant neurons in the self-organizing map in the learning phase; evaluating current process data in the technical facility in the form of a state vector with the aid of the self-organizing map trained in the learning phase; checking the Euclidean distances of the current state vector to the neuron hit as to whether the threshold value determined in the learning phase is exceeded; checking a current path by comparison with the reference path regions determined in the learning phase and definition of a neuron to be hit with the aid of the relevant reference path region as long as the threshold value was not already exceeded in the check with the neuron hit; and determining a symptom vector from the current state vector and either the neuron hit or the neuron that is to be hit, wherein the determining of the symptom vector is performed taking into account the threshold value of the relevant neuron, and wherein a symptom vector different from a zero vector flags the process anomaly, and the symptom vector specifies the process anomaly in more detail. 13. The computer program product of claim 12, wherein the instructions further comprise: acquiring and storing the respective duration of the good states in addition to the sequence of neuron hits along a path in the learning phase; and using the duration the good states is additionally used to check the duration of the current states and thereby to identify a process anomaly as long as the threshold value was not exceeded and the path did not deviate during the evaluation of the current process states. 14. The computer program product of claim 12, wherein the instructions further comprise: sampling processes of the technical facility at discrete regular time intervals; acquiring and storing the number of hits of a respective neuron instead of the respective duration of the good states; and performing a check with the aid of the respective number of hits with respect to the current states instead of a check on the duration of the current states. 15. The computer program product of claim 12, wherein, after the training, the instructions further comprise: optimizing the self-organizing map; and automatically calculating and storing the threshold values for the individual nodal points by a re-evaluation of good states. 16. The computer program product of claim 12, wherein the instructions further comprise applying a tolerance value to the threshold values. 17. A diagnostic system comprising: means for training a self-organizing map using historical process data as good states of a technical facility in a learning phase; means for determination and storage of reference path regions in the learning phase, wherein the reference path regions are definable with the aid of a temporal sequence of neuron hits in the self-organizing map and the tolerances for the neuron hits; means for storage of threshold values for Euclidean distances of the good states to the relevant neurons in the self-organizing map in the learning phase; means for evaluation of current process data in the technical facility in the form of a state vector with the aid of the self-organizing map trained in the learning phase; means for checking the Euclidean distance of the current state vector to the neuron hit as to whether the threshold value determined in the learning phase is exceeded; means for checking the current path by comparison with the reference path regions determined in the learning phase and for defining a neuron to be hit with the aid of the relevant reference path region as long as the threshold value was not already exceeded in the check with the neuron hit; and means for determination of a symptom vector from the current state vector and either the neuron hit or the neuron that is to be hit, wherein the determination is performed taking into account the threshold value of the relevant neuron, and wherein a symptom vector different from a zero vector flags the process anomaly, and the symptom vector specifies the process anomaly in more detail.