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Predictive maintenance meets predictive analytics : gathering and analyzing IoT data for manufacturing /
"In a talk aimed at data scientists, students, researchers, and nontechnical professionals, Danielle Dean introduces the landscape and challenges of predictive maintenance applications in the manufacturing industry. Predictive maintenance, a technique to predict when an in-service machine will...
| Clasificación: | Libro Electrónico |
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| Otros Autores: | |
| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
O'Reilly,
2016.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | "In a talk aimed at data scientists, students, researchers, and nontechnical professionals, Danielle Dean introduces the landscape and challenges of predictive maintenance applications in the manufacturing industry. Predictive maintenance, a technique to predict when an in-service machine will fail so that maintenance can be planned in advance, encompasses failure prediction, failure diagnosis, failure type classification, and recommendation of maintenance actions after failure. Danielle reviews predictive maintenance problems from the perspectives of both the traditional, reliability-centered maintenance field and IoT applications, discussing problem coverage, applicable predictive models based on data available, and what data must be collected to perform predictive maintenance tasks. You'll learn how to bridge the data-driven approach and the problem-driven approach by articulating what types of data are needed for different predictive maintenance applications."--Resource description page. |
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| Notas: | Title from title screen (viewed September 1, 2016). |
| Descripción Física: | 1 online resource (1 streaming video file (50 min., 58 sec.)) : digital, sound, color |