TECHNOLOGY LICENSING OPPORTUNITY: Vibration Intelligence System for Industrial Operation and Networks (VISION)

VISION uses higher-order spectral densities (HOSD) to gain deeper insights into vibration data than traditional power spectral density (PSD) techniques. HOSD captures more complex statistical features—like skewness and kurtosis—in the frequency domain, which reflect how well or poorly a machine is functioning. Response Date: July 15, 2026 LANL is seeking a licensing partner to support product development, validation, and market deployment of VISION. Interested organizations should contact Michael C. Erickson by July 15. How it Works: Over the lifetime of operating an industrial machine, plant managers will typically spend many times the purchase price of the machine on other activities like maintenance, electricity, and downtime for when the machine breaks. It is an ever-constant challenge and often a difficult choice to make equipment run smoothly (and thus reduce lifetime costs for operating the machine) or to meet short-term production targets. The consequence is that machinery is regularly pushed beyond its operational limits, which causes failures, downtime, chemical release, and fires. Worst, operating limits can purposefully be exceeded in the event of an industrial cyberattack. VISION solves this problem by using vibration to measure machine operating conditions, where plant managers can use this data to optimize costs of production and detect cyber threats—without requiring integration into existing control systems. Built on advanced signal processing mathematics, VISION enables high-accuracy machine learning predictions with minimal computational overhead. Scalable and independent, it provides a holistic view of facility performance, helping industries like oil and gas, power generation, and chemical processing to reduce costs, prevent downtime, and enhance security. Key Advantages: Comprehensive Facility Monitoring: Provides a holistic view of all machinery used in manufacturing, not just critical machines. Plant managers can better manage ‘forgotten’ machines that are manually controlled and not instrumented. Independent Cybersecurity Check: Monitoring is independent from industrial control systems, providing a means to check for cyberattacks on the machine. Maximizing Insights with Minimal Hardware: Extracts valuable operational data using vibration sensors, reducing reliance on costly infrastructure upgrades for the same knowledge. Superior Machine Learning Performance: Uses higher-order spectral densities (HOSD) to improve AI-based predictions, thereby reducing training time and increasing accuracy. Scalable & Non-Intrusive: Provides insights across the plant without modifying existing infrastructure, making it easy to implement and expand. Problems Solved: Operations Monitoring: Accurately monitors machine fluid characteristics (pressure / flow), which corresponds to a measure of efficiency, reliability, and failure mode. Cyberattack Detection: Identifies operation anomalies indicative of system compromise, independent of the control system and does not use internet monitoring like modern security solutions. Edge AI Processing: Performs on-device analysis with minimal computational burden, enabling faster and more accurate diagnostics. Integration with Condition Monitoring: Vibration data can be supplied to maintenance staff to detect equipment failure. Market Applications: LANL is looking for a collaboration partner with related skills, expertise, and resources for further developing this LANL invention into a commercial prototype. Examples of possible applications are: Oil & Gas: Extraction, pipelines, and refinery pumps and compressors, monitoring for poor operations that cause leaks and fires. Power Generation: Monitor cooling systems for optimal performance and safety. Chemical & Pharmaceutical Manufacturing: Ensure precise fluid control in sensitive production environments. Water & Wastewater Treatment: Maximize efficiency and reduce energy consumption from the facility to the city scale. Industrial Cyber...