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STMicroelectronics

From condition monitoring to predictive maintenance

Predictive Maintenance is a maintenance strategy based on Condition Monitoring, abnormality detection and classification algorithms. Integrating predictive models that estimate the remaining machine runtime left and detected abnormalities, Predictive Maintenance increases overall equipment effectiveness (OEE).

Moving from condition monitoring to predictive maintenance systems is challenging and is only possible after the data system has been set up, the smart sensors nodes chosen, and the partitioning process defined (edge or cloud).

Edge processing combines and distributes processing power among smart sensor nodes and gateways with the aim of sending the right data at the right time to enterprise-level systems where more advanced analyses can be performed. The STEVAL-STWINKT1 with High Speed data logging software, SL-PREDMNT-E2C, SL-PREDMNT-S2C are ST’s latest solutions that provide sensor nodes and all the hardware and software tools necessary to monitor motion and environmental data as part of an end-to-end predictive maintenance system based on a cloud application.

Watch the demo video (Italian subtitles available) and download the presentation to know more.

ST products
II3DWB vibration sensor
ISM330DHCX Machine Learning sensor
STM32MP1 MPU
STM32L4, STM32H7 MCUs
BlueNRG-2 wireless Bluetooth LE SoC

ST solutions
STEVAL-STWINKT1 With STSW-STWINKT01: Industrial wireless Sensor Tile with high speed data logging software
SL-PREDMNT-S2C: condition monitoring sensor to clouds solution, using smart sensor nodes and edge processing combined with AWS Cloud services
SL-PREDMNT-E2C: a condition monitoring cloud gateway solution, using smart sensor nodes and edge processing combined with AWS Cloud services

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STMicroelectronics
Indirizzo

VIA OLIVETTI 1

20864Agrate Brianza (MB)

Italia

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Categoria merceologica: Controlli avanzati e Robotica

Settori: Elettronica/Elettrotecnica, Macchine Utensili/Robotica

Area tematica: Automazione Avanzata

Keyword: #maintenance #factory #machinery #monitoring

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