초록 ▼

Principal Component Analysis (PCA) is used to model a process, and clustering techniques are used to group excursions representative of events based on sensor residuals of the PCA model. The PCA model is trained on normal data, and then run on historical data that includes both normal data, and data

Principal Component Analysis (PCA) is used to model a process, and clustering techniques are used to group excursions representative of events based on sensor residuals of the PCA model. The PCA model is trained on normal data, and then run on historical data that includes both normal data, and data that contains events. Bad actor data for the events is identified by excursions in Q (residual error) and T2 (unusual variance) statistics from the normal model, resulting in a temporal sequence of bad actor vectors. Clusters of bad actor patterns that resemble one another are formed and then associated with events.

대표청구항 ▼

The invention claimed is: 1. A computer implemented method of identifying events in a process, the method comprising: running a principal component analysis model on sensor data from the process; calculating statistics related to the model; determining if an event is occurring; finding a nearest cl

The invention claimed is: 1. A computer implemented method of identifying events in a process, the method comprising: running a principal component analysis model on sensor data from the process; calculating statistics related to the model; determining if an event is occurring; finding a nearest cluster of bad actors related to the event to identify the event; storing the found nearest cluster of bad actors in a storage device; and further comprising for new bad actors: identifying a sequence of cluster matches; correlating the sequence of cluster matches to known events; determining if a cluster needs to be split when new bad actors are added; and splitting the cluster into two clusters using a goodness of fit algorithm. 2. The method of claim 1 and further comprising: determining if a new event category is encountered; and broadening limits for the sequence of clusters. 3. A computer implemented method of identifying events in a process, the method comprising: running a principal component analysis model on sensor data from the process; calculating statistics related to the model; determining if an event is occurring; finding a nearest cluster of bad actors related to the event to identify the event; and storing the found nearest cluster of bad actors in a storage device; wherein a cluster is limited to a predetermined number of bad actors; and wherein the predetermined number of bad actors is ten. 4. A computer implemented method of identifying events in a process, the method comprising: running a principal component analysis model on sensor data from the process; calculating statistics related to the model; determining if an event is occurring; finding a nearest cluster of bad actors related to the event to identify the event; storing the found nearest cluster of bad actors in a storage device; and using a feature scoring scheme to identify top contributors of bad actors; wherein the feature scoring scheme is based on rank, value, and percent of contribution to a Q-residual sensor to identify a relative importance. 5. The method of claim 4, wherein the top-contributors are determined based on a majority percentage of the Q-residual. 6. The method of claim 4, where the top-contributors are determined based on only the contributors with absolute values that are drastically different from values of other contributors. 7. The method of claim 4 wherein the scoring scheme is based on predetermined limits. 8. The method of claim 7 wherein, the limits are computed statistically through change point detections. 9. A system for identifying events in a process, the system comprising: means for running a principal component analysis model on sensor data from the process; means for calculating statistics related to the model; means for determining if an event is occurring; means for finding a nearest cluster of bad actors related to the event to identify the event; means for identifying a sequence of cluster matches; means for correlating the sequence of cluster matches to known events; means for determining if a cluster needs to be split when new bad actor(s) are added; and means for splitting the cluster into two clusters using a goodness of fit algorithm. 10. The system of claim 9 and further comprising: means for determining if a new event category is encountered; and means for broadening limits for the sequence of clusters. 11. A system for identifying events in a process, the system comprising: means for running a principal component analysis model on sensor data from the process; means for calculating statistics related to the model; means for determining if an event is occurring; means for finding a nearest cluster of bad actors related to the event to identify the event; and means for feature scoring to identify top contributors of bad actors in a cluster; wherein the means for feature scoring is based on rank, value, and percent of contribution to a Q-residual sensor to identify a relative importance. 12. The system of claim 11, wherein the top-contributors are determined based on a majority percentage of the Q-residual. 13. The system of claim 11, where the top-contributors are determined based on only the contributors with absolute values that are drastically different from values of other contributors. 14. The system of claim 11 wherein the scoring scheme is based on predetermined limits. 15. The system of claim 14 wherein, the limits are computed statistically through change point detections.