ERROR DETECTION METHOD AND SYSTEM
First Claim
1. An error detection method for early detection of errors in a plant or facilities, comprising of steps of:
- acquiring data from a plurality of sensors to divide a trace in a data space into a plurality of clusters on the basis of a temporal change of the data;
modeling the divided clusters by a sub space method; and
calculating an discrepancy value as an error candidate.
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Abstract
Provided are a method which permits complete training data and data with added errors, and enables the early and accurate discovery of errors in facilities such as a plant, and a system thereof. To achieve the objectives, (1) the behavior of temporal data is observed over time, and the trace is divided into clusters; (2) the divided cluster groups are modeled in sub spaces, and the discrepancy values are calculated as errors candidates; (3) the training data are used (compare, reference, etc.) for reference to determine the state transitions caused by the changes over time, the environmental changes, the maintenance (parts replacement), and the operation states; and (4) the modeling is a sub space method such as regression analysis or projection distance method of every N data removing N data items, (N=0, 1, 2, . . . ) (for example, when N=1, one error data item is considered to have been added, this data is removed, then the modeling is performed), or a local sub space method. Linear fitting in regression analysis is equivalent to the lowest order regression analysis.
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Citations
14 Claims
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1. An error detection method for early detection of errors in a plant or facilities, comprising of steps of:
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acquiring data from a plurality of sensors to divide a trace in a data space into a plurality of clusters on the basis of a temporal change of the data; modeling the divided clusters by a sub space method; and calculating an discrepancy value as an error candidate. - View Dependent Claims (2, 3, 4, 5, 7)
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6. An error detection method for early detection of errors in a plant or facilities, comprising of steps of:
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acquiring data from a plurality of sensors; dividing a trace in a data space into a plurality of clusters on the basis of a temporal change of temporal data; and displaying the data to make the divided clusters evident to visualize a state.
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8. An error detection system for early detection of errors in a plant or facilities, comprising:
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a data acquiring unit that acquires data from a plurality of sensors; a trace division unit that divides a trace in a data space into a plurality of clusters on the basis of a temporal change of the data; a modeling unit that performs modeling in a sub space on the divided clusters; and an error detection unit that calculates an discrepancy value as an error candidate, wherein, in the trace division unit, the divided clusters are modeled in the sub space and the discrepancy value is calculated as an error candidate. - View Dependent Claims (9, 10, 11, 12)
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13. An error system for early detection of errors in a plant or facilities, comprising:
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a data acquiring unit that acquires data from a plurality of sensors; a trace division unit that divides a trace in a data space into a plurality of clusters on the basis of a temporal change of the data; a modeling unit that performs modeling in a sub space on the divided clusters; an error detection unit that calculates an discrepancy value as an error candidate; and a data display unit, wherein the data is displayed to make the divided clusters evident to visualize a state. - View Dependent Claims (14)
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Specification