PRINCIPAL COMPONENT ANALYSIS BASED FAULT CLASSIFICATION
First Claim
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;
differentiating a sensor that directly senses the event from a sensor that senses a residual of the event; and
storing the found nearest cluster of bad actors in a storage device.
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Abstract
Principle 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.
26 Citations
20 Claims
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1. A computer implemented 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; finding a nearest cluster of bad actors related to the event to identify the event; differentiating a sensor that directly senses the event from a sensor that senses a residual of the event; and storing the found nearest cluster of bad actors in a storage device. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. 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; means for finding a nearest cluster of bad actors related to the event to identify the event; means for differentiating a sensor that directly senses the event from a sensor that senses a residual of the event; and means for storing the found nearest cluster of bad actors in a storage device. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17)
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18. A computer implemented method of identifying events in a process, the method comprising:
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running a principal component analysis model on a computer on sensor data representative of multiple process parameters in the process; calculating statistics related to the model; determining if an event is occurring in the process; and finding a nearest cluster of bad actors related to the event to identify the event, wherein an event consists of one or more process parameters being out of a normal range in one or more parts of the process; differentiating a sensor that directly senses the event from a sensor that senses a residual of the event; and storing the found nearest cluster of bad actors in a storage device. - View Dependent Claims (19, 20)
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Specification