Name
Abstract | ||
|---|---|---|
Success of manufacturing companies largely depends on reliability of their products. Scheduled maintenance is widely used to ensure that equipment is operating correctly so as to avoid unexpected breakdowns. Such maintenance is often carried out separately for every component, based on its usage or simply on some fixed schedule. However, scheduled maintenance is labor-intensive and ineffective in identifying problems that develop between technician's visits. Unforeseen failures still frequently occur. In contrast, predictive maintenance techniques help determine the condition of in-service equipment in order to predict when and what repairs should be performed. The main goal of predictive maintenance is to enable pro-active scheduling of corrective work, and thus prevent unexpected equipment failures. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1145/2623330.2623340 | KDD |
Keywords | Field | DocType |
predictive maintenance,crisp-dm,industrial automation,log mining,machine learning | Technician,Planned maintenance,Data mining,Operational maintenance,Scheduling (computing),Computer science,Maintenance testing,Proactive maintenance,Corrective maintenance,Predictive maintenance | Conference |
Citations | PageRank | References |
33 | 1.48 | 22 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruben Sipos | 1 | 33 | 1.48 |
| Dmitriy Fradkin | 2 | 344 | 19.25 |
| Fabian Mörchen | 3 | 372 | 17.94 |
| Zhuang Wang | 4 | 246 | 15.35 |