I-Corps: AI-enabled automation intelligence software that can detect micro-anomalies in machine and robotic operations

The broader impact/commercial potential of this I-Corps projects is the development of an Artificial Intelligence (AI)-enabled automation software that detects micro-anomalies in machine and robotic operations and pinpoints defective components before catastrophic equipment failure. Defective components cause unscheduled downtime and are a significant challenge for manufacturers.

In a typical factory, an engineer may spend 30-60 percent of their time collecting information related to defective parts. Eighty-two percent of companies have experienced unplanned downtime that costs an average of $2 million annually. However, 70% of manufacturers still lack awareness of when equipment assets are due for maintenance or upgrade, and 72% of manufacturers identify unplanned downtime as their top priority or a high priority.

The high penetration level of advanced automation and sensor technologies in industrial operations also has increased demand for methods to effectively monitor and manage complex operations in a systematic and timely manner. The proposed technology addresses this need by delivering capabilities to maintenance and industrial engineers to achieve near zero Mean Time-to-Resolution (MTTR) of costly manufacturing equipment failures.

In addition, the technology eliminates the machine failures from occurring by advancing approaches that analyze data and provide insights after failure occurs.

This I-Corps project is based on the development of 1) a proprietary Programmable Logic Controller (PLC) driver that captures machine component micro degradation trends; 2) digital twins of the machine ecosystem that mirror the physical system performance and interactions between machines; and 3) an Automation Intelligence network. The proposed technology’s unique machine performance datasets, created from its PLC-driver and sensor feedback, captures the “normal” operation of the machines.

These datasets are then used to create causal relationship maps between different elements of the system. Causal maps accelerate the detection of root causes of potential machine failure and anticipate the potential risks and weaknesses within a system. In addition, digital twins provide a cyber-physical platform that integrates physics based models of machine components, spatial relationships from Computer Aided Engineering (CAE) models, and data-driven correlations inferred from self-adapting machine learning methods.

The AI platform runs on a distributed system/edge analytics network to ensure real-time monitoring and diagnosis on the local node (e.g., individual machines and its components) and system (e.g., production lines) levels. Integration of these three elements establishes a scalable digital twin that leverages element interactions to continuously discover and refine causal relationships that rapidly and reliably detect, identify, and pinpoint issues.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.