SG10201903611RA - Method and system for determining an error threshold value for machine failure prediction - Google Patents
Method and system for determining an error threshold value for machine failure predictionInfo
- Publication number
- SG10201903611RA SG10201903611RA SG10201903611RA SG10201903611RA SG10201903611RA SG 10201903611R A SG10201903611R A SG 10201903611RA SG 10201903611R A SG10201903611R A SG 10201903611RA SG 10201903611R A SG10201903611R A SG 10201903611RA SG 10201903611R A SG10201903611R A SG 10201903611RA
- Authority
- SG
- Singapore
- Prior art keywords
- determining
- threshold value
- error threshold
- failure prediction
- machine failure
- Prior art date
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/0751—Error or fault detection not based on redundancy
- G06F11/0754—Error or fault detection not based on redundancy by exceeding limits
- G06F11/076—Error or fault detection not based on redundancy by exceeding limits by exceeding a count or rate limit, e.g. word- or bit count limit
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/008—Reliability or availability analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/0706—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment
- G06F11/073—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment in a memory management context, e.g. virtual memory or cache management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| IN201911010877 | 2019-03-20 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| SG10201903611RA true SG10201903611RA (en) | 2020-10-29 |
Family
ID=72515808
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| SG10201903611RA SG10201903611RA (en) | 2019-03-20 | 2019-04-23 | Method and system for determining an error threshold value for machine failure prediction |
Country Status (2)
| Country | Link |
|---|---|
| US (1) | US11636001B2 (en) |
| SG (1) | SG10201903611RA (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| IL299752A (en) * | 2020-06-22 | 2023-03-01 | ID Metrics Group Incorporated | Data processing and transaction decisioning system |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110106734A1 (en) * | 2009-04-24 | 2011-05-05 | Terrance Boult | System and appartus for failure prediction and fusion in classification and recognition |
| CA2979193C (en) * | 2015-03-11 | 2021-09-14 | Siemens Industry, Inc. | Diagnostics in building automation |
| US20210002728A1 (en) * | 2018-02-27 | 2021-01-07 | Cornell University | Systems and methods for detection of residual disease |
| US10460235B1 (en) * | 2018-07-06 | 2019-10-29 | Capital One Services, Llc | Data model generation using generative adversarial networks |
-
2019
- 2019-04-23 SG SG10201903611RA patent/SG10201903611RA/en unknown
- 2019-04-24 US US16/392,709 patent/US11636001B2/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| US11636001B2 (en) | 2023-04-25 |
| US20200301769A1 (en) | 2020-09-24 |
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