GENERALIZED PATTERN RECOGNITION FOR FAULT DIAGNOSIS IN MACHINE CONDITION MONITORING
First Claim
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1. A method of machine condition monitoring, comprising:
- receiving, by a computer, historic operating data including data from O signals over time;
extracting I patterns x from data from individual signals in the operating data;
clustering the I patterns into K pattern clusters ck based on similarities;
clustering the O signals into R signal clusters based on correlations among the O signals;
receiving an annotated training data sample containing data from N signals selected from the O signals and having at least one marked failure time period;
creating a K×
N confidence vector containing K confidence values for each of the N signals, each confidence value representing a confidence that a pattern x extracted from data in the marked failure time period of a signal belongs to one of the K pattern clusters;
training a classifier using the K×
N confidence vector;
receiving, by a computer, a monitored data sample including data from the O signals; and
classifying, by a computer, the monitored data sample as indicating a failure based on at least one of the O signals not among the I signals being in a same signal cluster as one of the I signals and further based on a determination that the at least one of the O signals has confidence values similar to confidence values of the one of the I signals contained in the K×
N confidence vector.
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Abstract
A generalized pattern recognition is used to identify faults in machine condition monitoring. Pattern clusters are identified in operating data. A classifier is trained using the pattern clusters in addition to annotated training data. The operating data is also used to cluster the signals in the operating data into signal clusters. Monitored data samples are then classified by evaluating confidence vectors that include substitutions of signals contained in the training data by signals in the same signal clusters as the signals contained in the training data.
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Citations
24 Claims
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1. A method of machine condition monitoring, comprising:
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receiving, by a computer, historic operating data including data from O signals over time; extracting I patterns x from data from individual signals in the operating data; clustering the I patterns into K pattern clusters ck based on similarities; clustering the O signals into R signal clusters based on correlations among the O signals; receiving an annotated training data sample containing data from N signals selected from the O signals and having at least one marked failure time period; creating a K×
N confidence vector containing K confidence values for each of the N signals, each confidence value representing a confidence that a pattern x extracted from data in the marked failure time period of a signal belongs to one of the K pattern clusters;training a classifier using the K×
N confidence vector;receiving, by a computer, a monitored data sample including data from the O signals; and classifying, by a computer, the monitored data sample as indicating a failure based on at least one of the O signals not among the I signals being in a same signal cluster as one of the I signals and further based on a determination that the at least one of the O signals has confidence values similar to confidence values of the one of the I signals contained in the K×
N confidence vector. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
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13. A non-transitory computer-usable medium having computer readable instructions stored thereon for execution by a processor to perform a method of machine condition monitoring, the method comprising:
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receiving historic operating data including data from O signals over time; extracting I patterns x from data from individual signals in the operating data; clustering the I patterns into K pattern clusters ck based on similarities; clustering the O signals into R signal clusters based on correlations among the O signals; receiving an annotated training data sample containing data from N signals selected from the O signals and having at least one marked failure time period; creating a K×
N confidence vector containing K confidence values for each of the N signals, each confidence value representing a confidence that a pattern x extracted from data in the marked failure time period of a signal belongs to one of the K pattern clusters;training a classifier using the K×
N confidence vector;receiving a monitored data sample including data from the O signals; and classifying the monitored data sample as indicating a failure based on at least one of the O signals not among the I signals being in a same signal cluster as one of the I signals and further based on a determination that the at least one of the O signals has confidence values similar to confidence values of the one of the I signals contained in the K×
N confidence vector. - View Dependent Claims (14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24)
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Specification