http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-103631145-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_0ee33b0e3e7398cf6fc957eeee59d510 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G05B17-00 |
| filingDate | 2013-12-11-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_942cc18e99bfb2e6372ad9c1c7ff8321 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_82b91ee19831ed4189d3c61c2948d739 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_1d45967919f18d377184bea251f2b1ea |
| publicationDate | 2014-03-12-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-103631145-A |
| titleOfInvention | Monitoring index switching based multi-operating-mode process monitoring method and system |
| abstract | The invention discloses a monitoring index switching based multi-operating-mode process monitoring method and system. The method includes: acquiring normal data in different operating modes to serve as a training sample set; obtaining a hidden Markov model on the basis of the training sample set, and acquiring control limits corresponding to monitoring indexes of the hidden Markov model; respectively establishing statistical pattern analysis models of corresponding operating modes on the basis of training samples of each operating mode, and acquiring control limits corresponding to monitoring indexes of each statistical pattern analysis model; computing operating mode vectors on the basis of process data acquired in real time, and further computing differential operating mode vectors; computing corresponding real-time monitoring indexes according to norms of the differential operating mode vectors, and comparing the real-time monitoring indexes with the control limits corresponding to the monitoring indexes of the corresponding models so as to monitor running states of the operating modes. The method has the advantages that the process data are acquired in real time, reliability in monitoring is guaranteed, data in each operating mode need not to obey Gaussian distribution, and applicability is higher. |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-105718742-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-105573290-B http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-105573290-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-109491338-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-108015665-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-109634240-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-109634240-B http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-107147526-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-105487524-B http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-105487524-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-107707601-A |
| priorityDate | 2013-12-11-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID453509599 http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID54685047 |
Total number of triples: 25.