Name
Abstract | ||
|---|---|---|
In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures tracking, downtime reduction, and assets preservation. In the era of big machinery data, data-driven MHMS have achieved remarkable results in the detection of faults after the occurrence of certain failures (diagnosis) and prediction of the fu... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIE.2017.2733438 | IEEE Transactions on Industrial Electronics |
Keywords | Field | DocType |
Feature extraction,Logic gates,Monitoring,Sensors,Data mining,Fault diagnosis,Computational modeling | Data mining,Raw data,Supervised learning,Feature extraction,Tool wear,Artificial intelligence,Engineering,Deep learning,Predictive maintenance,Downtime,Machine learning,Feature learning | Journal |
Volume | Issue | ISSN |
65 | 2 | 0278-0046 |
Citations | PageRank | References |
21 | 0.80 | 12 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rui Zhao | 1 | 145 | 9.73 |
| Dongzhe Wang | 2 | 21 | 0.80 |
| Ruqiang Yan | 3 | 532 | 55.59 |
| K. Z. Mao | 4 | 848 | 74.71 |
| Fei Shen | 5 | 31 | 9.29 |
| jinjiang wang | 6 | 89 | 7.64 |