Method of condition monitoring
First Claim
1. A method of monitoring the condition of a fleet of plants, machines, or processes, the method comprising:
- generating and storing a single generic model for all members of the fleet using selected historic data from the fleet taken during normal operation and using novelty detection;
scaling the selected historic data from the individual fleet members by using scaling factors derived from the historic data prior to including them in the single generic model;
monitoring the fleet members during operation by taking test data, scaling the test data using said scaling factors, and comparing the scaled data to the generic model; and
generating a signal on the condition of the fleet members based on the comparison of the measurement data to the generic model;
wherein scaling the input historic data includes subtracting from each data point a mean value and dividing by the standard deviation;
forming clusters of the scaled data;
determining a cluster center for each cluster; and
calculating a cluster threshold for each cluster.
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Abstract
A method for the condition monitoring of a fleet of machines, plants, or processes includes the generation of a single generic network for the fleet using historic data from the members of the fleet and using novelty detection tools for analyzing and reducing the data. Scaling factors relevant to each individual fleet member are stored in a scaling file. The fleet members are monitored during operation by comparing test data to the network and calculating quantization errors. Signals on the condition of the fleet members are given based on the quantization errors. The network is adapted by adding new data representing changes in a plant such as due to degradations of components. Due to the individual scaling of data from the different fleet members, the method achieves a sensitivity of monitoring that is comparable to that of methods of the state of the art.
43 Citations
9 Claims
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1. A method of monitoring the condition of a fleet of plants, machines, or processes, the method comprising:
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generating and storing a single generic model for all members of the fleet using selected historic data from the fleet taken during normal operation and using novelty detection; scaling the selected historic data from the individual fleet members by using scaling factors derived from the historic data prior to including them in the single generic model; monitoring the fleet members during operation by taking test data, scaling the test data using said scaling factors, and comparing the scaled data to the generic model; and generating a signal on the condition of the fleet members based on the comparison of the measurement data to the generic model; wherein scaling the input historic data includes subtracting from each data point a mean value and dividing by the standard deviation; forming clusters of the scaled data; determining a cluster center for each cluster; and calculating a cluster threshold for each cluster. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. A method of monitoring the condition of a fleet of plants, machines, or processes, the method comprising:
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generating and storing a single generic model for all members of the fleet using selected historic data from the fleet taken during normal operation and using novelty detection; scaling the selected historic data from the individual fleet members by using scaling factors derived from the historic data prior to including them in the single generic model; monitoring the fleet members during operation by taking test data, scaling the test data using said scaling factors, and comparing the scaled data to the generic model; generating a signal on the condition of the fleet members based on the comparison of the measurement data to the generic model; adapting the generic model including taking new data; standardizing the new data by subtracting from each data point a mean value and dividing by a standard deviation to generate new, standardized data; adding the new, standardized data to the generic model while maintaining all previous data; and adapting the scaling factors in the scaling file according to the mean values and standard deviations of the new data. - View Dependent Claims (9)
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Specification