Large industrial facilities are typically huge systems with many moving parts. A single point of failure can shut down an entire system until the problem is resolved. An “outage,” whether it’s planned or forced, can cost more than $1 million each day, depending on plant size. Knowing what equipment must be maintained at each outage (thereby reducing the chance of a forced outage) and what equipment does not need maintenance (thereby reducing the time required for a planned outage) is critical to plants to be able to operate most efficiently.
Draper combines specific domain expertise and knowledge of how to apply the latest analytics techniques to extract meaningful information from raw data to better understand complex, dynamic processes. Our system design approach encompasses effective organization and processing of large data sets, automated analysis using algorithms and exploitation of results. To facilitate user interaction with these processed data sets, Draper applies advanced techniques to automate understanding and correlation of patterns in the data. Draper’s expertise encompasses machine learning (including deep learning), information fusion from diverse and heterogeneous data sources, optimized coupling of data acquisition and analysis and novel methods for analysis of imagery and video data.
Draper combines mission planning, PN&T, situational awareness, and novel GN&C designs to develop and deploy autonomous platforms for ground, air, sea and undersea needs. These systems range in complexity from human-in-the-loop to systems that operate without any human intervention. The design of these systems generally involves decomposing the mission needs into sets of scenarios that result in trade studies that lead to an optimized solution with key performance requirements. Draper continues to advance the field of autonomy through research in the areas of mission planning, sensing and perception, mobility, learning, real-time performance evaluation and human trust in autonomous systems.
Draper has continued to advance the understanding and application of human-centered engineering to optimize the interaction and capabilities of the human’s ability to better understand, assimilate and convey information for critical decisions and tasks. Through its Human Systems Technology capability, Draper enables accomplishment of users’ most critical missions by seamlessly integrating technology into a user’s workflow. This work leverages human-computer interaction through emerging findings in applied psychophysiology and cognitive neuroscience. Draper has deep skills in the design, development, and deployment of systems to support cognition – for users seated at desks, on the move with mobile devices or maneuvering in the cockpit of vehicles – and collaboration across human-human and human-autonomous teams.
Current approaches to this challenge are highly labor intensive and employ a “predictive maintenance” program in which plant staff manually collect and analyze individual data samples. Analysis is often outsourced and results may take several days to arrive. Technicians spend an estimated 80 percent of their time gathering and analyzing data on machinery that is perfectly healthy – wasting precious time and effort. Analysis of that data is often as much “art” as it is science; experienced operators often have difficulty training new technicians or analysts. Draper’s approach automates that data acquisition, saving staff from the time-consuming task of manually gathering data samples.
Draper has developed automated technology that enables these experts to perform their job function much more effectively. The technology is based on software that Draper developed for the International Space Station, where it controls crew-assisted operations and payloads. Using fixed sensors, networking and advanced data analysis algorithms, Draper has created an intelligent, automated measurement and diagnostic system providing analysis and decision support based on thousands of critical parameters that are vital to achieving high reliability. Draper calls its system SmartGen.
If an anomaly is detected, the data analysis and diagnosis of condition are both automated. Operators receive alerts of equipment that needs their attention, and data are archived automatically, so historical perspectives are always available.