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Cloud-based predictive maintenance platform combining AI and human insights for asset health.
Senseye Predictive Maintenance, a Siemens product, leverages AI and human expertise to generate machine behavior models and direct attention to critical assets. The cloud platform processes large volumes of data for thousands of assets, provides real-time monitoring, and delivers actionable insights to reduce maintenance costs and unplanned downtime. Senseye integrates with any asset or system, supports rapid deployment, and includes robust security features for enterprise use.
Integrates with any asset or system
Uses existing or new sensors
Monitors thousands of assets
Cloud-based global access
AI-generated behavior models
Real-time risk assessment
Connects with ERP and CMMS
Data flows across platforms
Top-tier encryption
Enterprise-grade security protocols
Unlimited amount of users
AI-driven predictive maintenance analytics software
Automatic generation of predictive models
Asset health visualization and real-time monitoring
Alert notification service
Prioritized list of assets requiring attention
Real user experiences from across different platforms
Senseye PdM provided real-time health information for our use in the program's predictive maintenance component. Looking at this aspect, one can identify probabilities of equipment failure hence arranging for their repair in advance.
TB. Takacs B.
2024-08-09
looking for a scalable, machine-agnostic predictive maintenance solution.
ors and integrate AI without requiring data science expertise.
on in data visualization and trend analysis for minute details.
ce tool to have remote access or control capabilities.
Significantly reduce unplanned downtime (up to 50%) and maintenance costs (up to 40%) while increasing maintenance staff productivity and extending asset life through scalable, AI-driven, and easy-to-use predictive maintenance for all assets, not just the critical few.
It is an advisory tool only, not a control system, and the customer remains solely responsible for the assets. No reverse control/remote access to customer's assets. AI-generated output may not be entirely accurate, complete, or reliable, requiring user verification. If an asset is in a bad condition during the initial 120-hour learning phase, the system may learn the 'low state' as usual. Some users noted difficulty in tweaking/individualizing settings for specific used machinery.