Principal component analysis based fault classification
First Claim
1. A 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; and
finding a nearest cluster of bad actors related to the event to identify the event.
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Abstract
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.
36 Citations
29 Claims
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1. A method of identifying events in a process, the method comprising:
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running a principal component analysis model on sensor data from the process;
calculating statistics related to the model;
determining if an event is occurring; and
finding a nearest cluster of bad actors related to the event to identify the event. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. A system for identifying events in a process, the system comprising:
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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; and
means for finding a nearest cluster of bad actors related to the event to identify the event. - View Dependent Claims (17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28)
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29. A system for identifying events in a process, the system comprising:
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a controller coupled to sensors monitoring a process;
a principal component analysis model receiving data from the sensors monitoring the process and reducing a number of variables associated with the data from the sensors, the model further comprising;
a training module that is run on historical data to create a pool of vectors with values for the variables, wherein the training module further creates clusters of bad actors from the values based on statistics and associates the clusters with known events; and
a run time module that receives incoming data from the sensors, calculates statistics, determines if events are occurring, and identifies clusters to identify events.
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Specification