Predictive Maintenance
Predictive maintenance uses data analytics and machine learning to detect issues early—reducing downtime and repair costs while improving reliability and productivity.
Background & Challenges
Industrial and electrical equipment is often deployed globally at customer sites, and uptime is critical to operations. While OEMs are turning to predictive maintenance, they commonly face four hurdles: fragmented data acquisition (mixed protocols and formats, limited collectors), poor data quality (noise, invalid records, inconsistent units), complex and costly connectivity (especially wired networks), and data security and privacy risks.
Solution
InHand Networks offers a “cloud+edge” solution for preventive maintenance. With support for multiple industrial protocols and powerful edge computing capabilities, the IG edge gateways and EC edge computers easily acquire data from various onsite devices, validate data and then upload them to the cloud.
Capabilities
Constant Monitoring
Continuous online status monitoring and data collection of devices.
Protocol Conversion
Connection to different types of industrial devices, convert between hundreds of industrial protocols.
AI on the Edge
AI capabilities deployed on the device edge to improve data analysis efficientcy.
Clouds Ready
Support for multiple IoT clouds, AWS, Microsoft Azure, etc.
SaaS Integration
Flexible deployment of manufacturers’ own SaaS platforms, allowing for fault diagnosis and trend prediction.
Flexible Solutions
Multiple edge computing gateways cater to varying needs with interfaces, computational capabilities, and development environments.
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