Revolutionary Predictive Maintenance Solutions for Distribution & Logistics
Full-Stack, AI-Powered Solutions to Automate and Enhance Asset Reliability in Fulfillment and Distribution Centers
Al Analytics
Comprehensive Al analytics and dashboard suiteallows users to gather insights to optimizemaintenance and operational processes
Remote Viewing
View live Infrared & visible & acoustic & vibrationdata 4 video foode from Ill camerns and sensors.
Alerts & Alarms
Live alert & alarm notifications notify teams orindividuals via email, SMS, Slack, and other
Archival & Retrieval
Store critical data on the platform to enable trendanalyses, enhanced Al model trainina, and datacompliance
Integrations
Flexible integrations automatically generate systemwork orders or trigger facility responses in EAM,DCS, PLC, and SCADA systems
Software Updates
Ongoing cloud and edge software updates toenable ever-improving functionality, ensuringoptimum performance
MSAI In Distribution & Logistics
- Conveyor Systems
- Motors
- Critical Power
Predictive Maintenance for Conveyor Systems
Utilizing advanced thermal imaging and sensing technologies, MultiSensor AI detects temperature and vibration anomalies within conveyor systems, indicative of potential issues such as abnormal friction, blockages, or overheating components. The solution establishes a baseline profile for a conveyor belt operating under normal conditions and continuously compares real-time temperature and vibration readings against this reference data. Any deviations from the norm can help pinpoint and diagnose problems such as misaligned or damaged rollers, belt slippage, material spillage, overly tight belts, fire risks, or other impending maintenance needs. By identifying these issues early, users can effectively prevent equipment failures, unplanned downtime, and safety hazards throughout the distribution and logistics facility.
Predictive Maintenance for Motors
MultiSensor AI provides distribution and logistics operations with the tools to optimize motor performance in their critical operations operations. By utilizing a continuous stream of temperature and vibration sensing data from thermal cameras and sensors, MultiSensor AI monitors the condition and performance of motors in real-time. The software employs its advanced algorithms to detect temperature anomalies and sequential temperature changes, comparing these readings against normal operating temperatures. This allows MultiSensor AI to streamline predictive maintenance and performance optimization of motors across distribution and logistics operations.
Predictive Reliability for Critical Power
MultiSensor AI optimizes power management in distribution and logistics operations by identifying temperature anomalies and hotspots in critical electrical equipment and systems. These temperature data points are analyzed by MultiSensor AI's AI-powered algorithm to pinpoint potential asset reliability and employee safety issues early on, thus minimizing unexpected failures and safety hazards. Through deep analytics, the solution enables precise, predictive maintenance strategies for electrical assets, extending their lifespan and reducing repair costs. With MultiSensor AI, distribution and logistics operations can ensure a reliable power supply, minimize disruptions, and lower operational costs.
Transform Asset Reliability in Your Distribution Operations
MSAI Connect leverages advanced algorithms to process large volumes of visual and sensory data in real-time, enabling distribution and logistics operations to reach unparalleled levels of efficiency, reliability, quality, and safety. By constantly monitoring critical equipment with various sensor and imaging technologies, MSAI Connect can detect potential defects or irregularities before they become significant, allowing for prompt corrective measures before unplanned downtime occurs. This transformative technology empowers distribution and logistics operations to make data-driven decisions, fostering continuous improvement and optimized reliability processes throughout their operations.
