Resources

On-Demand Webinar: Why Failures Feel Sudden, and What Top Operators Do Differently

MultiSensor AI — Tue, 07 Apr 2026 16:54:48 GMT

Originally aired on Tuesday, March 31, 2026

Stop reacting to “unexpected” failures. Start seeing what’s actually happening before they occur.

Most industrial and automated operations don’t fail suddenly—but it often feels that way. In reality, failures develop over time, with early signals that are missed, ignored, or simply invisible with traditional monitoring approaches. In this live session, James Newman, Senior Director of Product Enablement at MultiSensor AI, and Luke Grice-Lowe, Director of Reliability & Maintenance Programs (ex-Amazon), break down what’s really happening across motors, drives, conveyors, and electrical systems—and why so many teams only detect issues when it’s already too late.

Factors Affecting Radiometric Temperature Measurements

MultiSensor AI — Sun, 29 Mar 2026 23:37:04 GMT

Improve accuracy. Reduce false readings. Make thermal data you can trust.

Most teams using thermal imaging assume they’re getting accurate temperature data—but in reality, radiometric measurements are highly sensitive to surface conditions, environment, and how the system is deployed. Small miscalculations in emissivity, reflectivity, or distance can lead to misleading readings that impact maintenance decisions and asset reliability. In this whitepaper, MultiSensor AI breaks down the key factors that influence radiometric accuracy - and how to account for them in real-world industrial environments.

On-Demand Webinar: How to Reduce Equipment Downtime with Multi-Sensor Condition-Based Monitoring

MultiSensor AI — Sun, 29 Mar 2026 21:36:12 GMT

Stop guessing. Detect earlier. Plan maintenance before downtime hits.

Most industrial teams know reactive maintenance isn’t sustainable - but moving to condition-based monitoring can feel complex, fragmented, and hard to scale. In this on-demand webinar, Taimen Taylor, Senior Director of Implementations at MultiSensor AI, and Marouane Lahmidi, Founder of IndustrAI SARL and former Amazon (RME) predictive maintenance leader, break down a practical, real-world approach to making that transition - without overwhelming your team with disconnected tools or “dashboard chaos.”

Welcome to MultiSensor AI - Smarter Monitoring, Stronger Reliability

MultiSensor AI — Thu, 05 Mar 2026 18:04:49 GMT

This video introduces MultiSensor AI – a sensor-agnostic platform that helps industrial operations catch problems early, prevent costly downtime, and extend asset life. We combine hardware, software, and AI-driven insights into one solution that keeps critical equipment running longer, safer, and stronger.

Solution Brief - MSAI Connect - Thermal Imaging Platform for Critical Asset Monitoring

MultiSensor AI — Mon, 02 Mar 2026 11:51:23 GMT

Solution Brief - MSAI Connect - Thermal Imaging Platform for Critical Asset Monitoring (ft. Global Distribution Warehouse case study)

This case study shows how a global online retailer used MultiSensor AI to continuously monitor high-speed conveyor and gapper bed systems - catching belt tension and overheating issues early, before they caused downtime. With fixed thermal monitoring and real-time alerts, the team reduced unplanned downtime by 30%, increased throughput more than 11×, cut maintenance costs by 40%, and achieved up to a 20× ROI. A clear example of how condition-based monitoring turns early signals into measurable uptime, safety, and operational gains.

Case Study: Conveyor Belt in Multinational Retailer’s Fulfillment Center Tray router discharge identified by MSAI Connect

MultiSensor AI — Thu, 05 Feb 2026 14:48:18 GMT

This case study shows how MultiSensor AI detected an imminent tray router discharge conveyor failure in a multinational retailer’s fulfillment center. MSAI Connect identified a steady thermal rise caused by a failed tracking roller that forced the belt against side guarding and the tracking unit. The issue was diagnosed and corrected within an agreed inspection window, limiting downtime to one hour instead of a four-hour belt failure. The outcome: three additional hours of operational uptime, $18,000 in avoided labor downtime, and $6,500 in avoided belt replacement costs. A clear example of how continuous thermal monitoring prevents catastrophic conveyor failures before throughput is impacted.

Solution Brief -MSAI Connect - Thermal Imaging Platform for Critical Asset Monitoring

MultiSensor AI — Thu, 05 Feb 2026 04:58:42 GMT

This solution brief explains how MSAI Connect delivers 24/7 thermal monitoring for conveyor belts, motors, bearings, and other critical assets where downtime is costly. By replacing calendar-based handheld inspections with fixed thermal cameras and real-time analytics, teams catch overheating, misalignment, and wear early - before failure occurs. MSAI Connect integrates directly with maintenance and operational systems to trigger alerts, generate work orders, and support predictive maintenance at scale. Proven in Fortune 50 distribution centers, the solution delivers rapid ROI, reduced emergency repairs, and more reliable, high-throughput operations.

Case Study: Conveyor Belt Misalignment in Multinational Retailer’s Fulfillment Center - Belt Misalignment

MultiSensor AI — Thu, 05 Feb 2026 04:52:43 GMT

This case study shows how MultiSensor AI identified conveyor belt misalignment and sideguard friction in a multinational retailer’s fulfillment center before it caused a high-severity failure. MSAI Connect detected an abnormal thermal rise as the belt tracked against the sideguard, triggering an early inspection. The issue was corrected during scheduled downtime, avoiding an estimated three hours of unplanned downtime on a single-point-of-failure conveyor. The result: $18,000 in avoided downtime costs, $6,500 in avoided belt replacement, and uninterrupted flow to the main sorter. A strong example of how continuous thermal monitoring protects conveyor reliability and keeps fulfillment moving.

MSAI Successfully Upgrades Condition Monitoring and Predictive Maintenance Processes in Distribution Center for Fortune 50 Manufacturer

MultiSensor AI — Thu, 05 Feb 2026 04:49:22 GMT

This case study shows how a Fortune 50 online retailer used MultiSensor AI to eliminate recurring unplanned downtime across its distribution facilities. Despite regular handheld inspections, critical conveyors and motors continued to fail. By deploying continuous thermal monitoring with the MSAI Connect platform, the retailer detected overheating belts, misaligned conveyors, and failing motors before breakdowns occurred. The result was immediate cost avoidance across multiple assets, a positive ROI in approximately one month, and a scalable predictive maintenance model now rolling out across global facilities. A clear example of how 24/7 condition monitoring outperforms manual inspections in high-throughput distribution operations.

Upgrading Fire Prevention and Maintenance for a Top Pulp and Paper Manufacturer

MultiSensor AI — Thu, 05 Feb 2026 04:48:36 GMT

This case study shows how a leading U.S. pulp and paper manufacturer used MultiSensor AI to prevent fires and reduce unplanned downtime in a high-risk sawmill environment. After a costly fire event, the facility deployed fixed thermal cameras and AI-driven monitoring across critical assets and ignition-prone zones. The result: a 62% reduction in asset failures, zero thermal incidents since implementation, and more than $550,000 saved in avoided downtime within the first year. By shifting from reactive inspections to continuous early fire detection, the operation improved safety, increased throughput, and strengthened long-term asset reliability.