July 19, 2018 – Collaboration Employs Advanced Analytics to Improve Manufacturing Efficiency

Control Station Teams with QCIC to Automate Plant-Wide Manufacturing Diagnostics


Manchester, CT – July 19, 2018 – Control Station and the University of Connecticut School of Engineering today announced an applied science initiative focused on the development of an advanced forensic utility. The collaboration targets the need among process manufacturers for plant-wide monitoring utilities with fully automated root-cause analysis capabilities. It combines specialized resources from the University’s Quite Corner Innovation Cluster with process control experts from Control Station.

The market for advanced monitoring and analytics technologies is growing rapidly as process manufacturers continually look for new and innovative methods for improving both production performance and asset reliability. In its report from 2015 ABI Research estimated the size of that segment of the industrial automation market at $5.7 Billion. ABI Research further suggested that the market would be dominated by small innovative firms with disruptive solutions. QCIC, located in the Innovation Partnership Building on the Storrs campus, was formed in 2016 for the purpose of collaborating with small and mid-sized technology firms like Control Station and fueling such disruptive innovations.

“We’re excited to join Control Station in their pursuit of unique capabilities that empower manufacturers to both avoid costly unplanned downtime and sustain optimal productivity,” shared Dr. Hadi Bozorgmanesh, director of the Enterprise Solution Center, the umbrella organization of QCIC. “UConn has a long history of pioneering disruptive technologies. Our mission at QCIC is to continue that legacy by assisting local technological innovators with crucial resources and applied research, leading to growth in their business and the state’s economy.”

Founded in Storrs in 1988, Control Station is a product of the university’s entrepreneurial environment. The company is now located in Manchester, Connecticut and licenses a portfolio of process diagnostic and optimization tools. The company’s PlantESP™ Control Loop Performance Monitoring platform assesses the performance of a manufacturing site’s regulatory control systems, proactively identifying issues that undermine production efficiency and throughput. The collaboration between Control Station and QCIC seeks a root-cause analysis utility that fully automates PlantESP’s ability to isolate a single bad actor from among hundreds or even thousands of potential sources.

“A typical manufacturing site is highly complex with innumerable interacting processes that mask the root-cause of issues affecting production,” stated Bob Rice, Control Station’s Vice President of Engineering and holder of both masters and doctoral degrees from UConn’s School of Engineering. “Without precise and accurate information production staff waste valuable time investigating the symptoms of poor control rather than addressing the root-cause. Our work with QCIC seeks a solution to that problem.”

Most controller performance monitoring solutions like PlantESP employ forensic tools such as Cross Correlation and Power Spectral Density to assess system interaction. While the information from these tools has proven valuable, essential details associated with the phase of each process’ frequency are lost and manual assessments are required. Such assessments are often inefficient and they are routinely subject to error. Control Station successfully conducted initial research into the extraction of signal phase values and their corroboration with the associated Power Spectral Density values. The research supports development of an automated method capable of identifying and sequencing the interaction among and between a production facility’s many control systems.

Control Station is a leading provider of process diagnostic and optimization technologies. The company’s products have been recognized with numerous awards for innovation, and they have been licensed to nearly 40% of all manufacturers listed on the Fortune 500. The company’s PlantESP solution has been successfully deployed at production facilities located on five continents. Earlier this year the Small Business Administration recognized Control Station as the 2018 Exporter of the Year for the State of Connecticut.

About Quiet Corner Innovation Cluster

QCIC, funded by the US Economic Development Administration, the University of Connecticut, and Connecticut Innovations, supports business growth potential of small and medium-sized technology and manufacturing enterprises in rural Tolland, Windham, and New London Counties. QCIC forms partnerships with select SMEs to enhance or expand their product and service offerings by leveraging UConn’s extensive R&D capabilities and office of commercialization. SMEs that participate in the program collaborate with faculty who specialize in their particular area of focus and benefit from the new Proof of Concept Center at the UConn. In addition to the POCC, UConn will be creating the new Connecticut Manufacturing Simulation Center to enable SMEs rapid prototyping capabilities and further facilitate growth.


About Control Station

Control Station empowers process manufacturers to increase production efficiency and throughput. The company’s software-based solutions actively monitor and optimize plant-wide control loop performance.

The company’s products are both highly innovative and award-winning. PlantESP™ is the leading CLPM solution for identifying and isolating issues that negatively affect control loop performance. Control Station’s portfolio of LOOP-PRO™ products is recognized as the process industry’s leading solution for PID controller tuning. It is the only controller tuning software that accurately models oscillatory and noisy process data.

Control Station’s solutions are licensed to leading process manufacturers worldwide and they are available direct from Control Station and through its network of distribution partners. The company is headquartered in the United States.


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