CA2843004C - Diagnostic adaptatif de defaillance - Google Patents
Diagnostic adaptatif de defaillance Download PDFInfo
- Publication number
- CA2843004C CA2843004C CA2843004A CA2843004A CA2843004C CA 2843004 C CA2843004 C CA 2843004C CA 2843004 A CA2843004 A CA 2843004A CA 2843004 A CA2843004 A CA 2843004A CA 2843004 C CA2843004 C CA 2843004C
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- CA
- Canada
- Prior art keywords
- fault
- events
- metrics
- metric
- event
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
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- 238000003745 diagnosis Methods 0.000 title claims abstract description 81
- 230000003044 adaptive effect Effects 0.000 title claims abstract description 47
- 238000001514 detection method Methods 0.000 claims abstract description 103
- 238000006467 substitution reaction Methods 0.000 claims abstract description 61
- 230000002596 correlated effect Effects 0.000 claims abstract description 41
- 238000000034 method Methods 0.000 claims description 57
- 238000012549 training Methods 0.000 claims description 24
- 230000008569 process Effects 0.000 claims description 18
- 230000006870 function Effects 0.000 claims description 9
- 238000002347 injection Methods 0.000 claims description 6
- 239000007924 injection Substances 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 description 21
- 239000011159 matrix material Substances 0.000 description 9
- 238000011084 recovery Methods 0.000 description 7
- 230000004044 response Effects 0.000 description 6
- 230000006399 behavior Effects 0.000 description 5
- 230000007246 mechanism Effects 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 3
- 238000013500 data storage Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 238000005067 remediation Methods 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 230000007935 neutral effect Effects 0.000 description 2
- 230000001960 triggered effect Effects 0.000 description 2
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000002085 persistent effect Effects 0.000 description 1
- 230000007261 regionalization Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000011524 similarity measure Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
- H04L41/0636—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis based on a decision tree analysis
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
- H04L41/065—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis involving logical or physical relationship, e.g. grouping and hierarchies
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/142—Network analysis or design using statistical or mathematical methods
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Debugging And Monitoring (AREA)
Abstract
Conformément à un exemple, un système de diagnostic adaptatif de défaillance peut comprendre des instructions lisibles par une machine stockant en mémoire pour recevoir des indicatifs et des événements dun système dentreprise et utiliser un graphique de substitution pour déterminer si un indicatif reçu ou un événement reçu appartient à un groupe qui comprend un ou plusieurs indicatifs ou événements corrélés groupés selon la similarité. Si lindicatif reçu ou l'événement reçu appartient au groupe, la mémoire peut ensuite stocker les instructions lisibles par la machine afin dutiliser le graphique de détection pour déterminer si lindicatif reçu ou l'événement reçu est identifiable pour former un motif de défaillance en traversant un parcours de défaillance du graphique de détection. De plus, la mémoire peut également stocker les instructions lisibles par la machine pour diagnostiquer une défaillance fondée sur la traversée du parcours de défaillance du graphique de détection. Le système peut également comprendre un processeur servant à implanter les instructions lisibles par la machine.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CA2843004A CA2843004C (fr) | 2014-02-14 | 2014-02-14 | Diagnostic adaptatif de defaillance |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CA2843004A CA2843004C (fr) | 2014-02-14 | 2014-02-14 | Diagnostic adaptatif de defaillance |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CA2843004A1 CA2843004A1 (fr) | 2015-08-14 |
| CA2843004C true CA2843004C (fr) | 2017-01-24 |
Family
ID=53836985
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA2843004A Active CA2843004C (fr) | 2014-02-14 | 2014-02-14 | Diagnostic adaptatif de defaillance |
Country Status (1)
| Country | Link |
|---|---|
| CA (1) | CA2843004C (fr) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11570038B2 (en) * | 2020-03-31 | 2023-01-31 | Juniper Networks, Inc. | Network system fault resolution via a machine learning model |
-
2014
- 2014-02-14 CA CA2843004A patent/CA2843004C/fr active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CA2843004A1 (fr) | 2015-08-14 |
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