Data sorting device and method, and monitoring and diagnosis device
The present invention provides a data sorting device and method and a monitoring and diagnosis device, which are able to create a model conveniently. A data sorting device and a monitoring and diagnosis device of the present invention include an operating data database which stores operating data of the plant equipment in a time-series manner. The devices input the operating data that are highly associated physically and engineeringly among the operating data stored in the operating data database, inputting processing attributes relevant to the operating data that are highly associated physically and engineeringly, creating a model simulating characteristics of the plant equipment, and performing data sorting, monitoring, and diagnosis through use of the model.
1. A computer-implemented method training a learning network to diagnose anomalies in plant equipment, the computer-implemented method comprising:
receiving operating data from sensors that monitor the plant equipment;
storing the operating data and a time when the operating data was collected by the sensors in a first database;
excluding abnormal values from the operating data to form filtered data;
training an electric characteristics learning unit using a first subset of the filtered data to generate first weighting factors;
training a vibratory characteristics learning unit using a second subset of the filtered data to generate second weighting factors;
training a thermal characteristics learning unit using a third subset of the filtered data to generate third weighting factors;
storing the first weighting factors, the second weighting factors and the third weighting factors in a second database;
creating a model for nominal operation using the first weighting factors, the second weighting factors and the third weighting factors that are stored in the second database;
receiving additional data from the sensors that monitor the plant equipment; and
diagnosing the anomalies in the plant equipment by inputting the additional data into the model for nominal operated created.
2. The computer-implemented method according to claim 1 , the method further comprising:
recommending countermeasures based on the anomalies in the plant equipment diagnosed, wherein the countermeasures are stored in a determination/evaluation result database.
3. The computer-implemented method according to claim 1 , wherein the plant equipment is a rotating electric machine.
4. A non-transitory computer readable storage medium that stores instructions for
training a learning network to diagnose anomalies in plant equipment, the instructions when executed by a processor cause the processor to execute a method, the method comprising:
receiving operating data from sensors that monitor the plant equipment
storing the operating data and a time when the operating data was collected by the sensors in a first database;
forming filtered data by excluding abnormal values from the operating data;
training an electric characteristics learning unit using a first subset of the filtered data to generate first weighting factors;
training a vibratory characteristics learning unit using a second subset of the filtered data to generate second weighting factors;
training a thermal characteristics learning unit using a third subset of the filtered data to generate third weighting factors;
storing the first weighting factors, the second weighting factors and the third weighting factors in a second database;
creating a model for nominal operation using the first weighting factors, the second weighting factors and the third weighting factors that are stored in the second database;
receiving additional data from the sensors that monitor the plant equipment; and
diagnosing the anomalies in the plant equipment by inputting the additional data into the model for nominal operated created.
5. The non-transitory computer readable storage medium according to claim 4 , wherein the method further comprises:
recommending countermeasures based on the anomalies in the plant equipment diagnosed, wherein the countermeasures are stored in a determination/evaluation result database.
6. The non-transitory computer readable storage medium according to claim 4 , wherein the plant equipment is a rotating electric machine.
7. A system for
training a learning network to diagnose anomalies in plant equipment, comprising:
sensors that monitor the plant equipment;
an operating data database that stores operating data collected by the sensors and a time when the operating data was collected by the sensors
a normal-value data database; and
a computer that is communicatively coupled to the sensors, the operating data database and the normal-value data database, wherein the computer is configured to:
retrieve the operating data from the operating data database,
form filtered data by excluding abnormal values from the operating data,
trains an electric characteristics learning unit using a first subset of the filtered data to generate first weighting factors,
trains a vibratory characteristics learning unit using a second subset of the filtered data to generate second weighting factors,
trains a thermal characteristics learning unit using a third subset of the filtered data to generate third weighting factors,
stores the first weighting factors, the second weighting factors and the third weighting factors in the normal-value data database,
creates a model for nominal operation using the first weighting factors, the second weighting factors and the third weighting factors that are stored in the normal-value data database;
receives additional data from the sensors that monitor the plant equipment; and
diagnose the anomalies in the plant equipment by inputting the additional data into the model for nominal operated created.
8. The system according to claim 7 further comprising:
a determination/evaluation result database that is communicatively coupled to the computer,
wherein the computer is further configured to:
recommend countermeasures based on the anomalies in the plant equipment diagnosed, wherein the countermeasures are stored in the determination/evaluation result database.
9. The system according to claim 7 , wherein the plant equipment is a rotating electric machine.