Industrial Experience + Data Analysis = Meaningful Information
Our unique approach is to use knowledge engineering - taking the contributions of domain experts, insights, and reasoning; apply rules to multiple attributes. The rules that are applied are organized into a knowledge base, which is then controlled by the end-user who does not have to be an expert.
Our team helps clients achieve their analytics goals through application of extensive experience with industrial modeling to achieve quick turn-around on investment. Analytic approaches include statistics, machine learning, deep learning, and custom algorithms as necessary. Approaches are deployed within existing customer application environments.
AARON HUSSEY, P.E.
Founder & Principal
Aaron Hussey is the principal for Integral Analytics’ business and consulting activities with an emphasis on power industry implementation of advanced pattern recognition (APR) models and analytics software design and prototyping. Current responsibilities include APR model development and program implementation for Ontario Power Generation (OPG) monitoring & diagnostic center under contract to WSC, Inc., assessment of critical sensors using a failure modes and effects analysis (FMEA) for large electric generators under contract to the Electric Power Research Institute (EPRI), and various analytics software design and prototyping efforts for software vendors and electric utilities covering nuclear, fossil-fuels, and hydroelectricity. Before founding Integral Analytics, Aaron’s responsibilities included implementation of predictive analytic models for Duke Energy’s 11-unit fleet of nuclear power plants, Tennessee Valley Authority’s (TVA) fossil, hydro, and nuclear generating units, and South Carolina Electric & Gas (SCE&G) VC Summer nuclear site. Prior to that, he fulfilled various roles over 9+ years at the Electric Power Research Institute, focusing on instrumentation and controls (both nuclear and non-nuclear) research including project management for the Fleet-Wide Monitoring Interest Group.
Howard Nudi received his BSEE from Geneva College in 1987 and his MBA from the University of Nebraska in 2000. Mr. Nudi has over 30 years’ experience in equipment reliability program planning, implementation, and training. He started his career in the United States Air Force as an Aircraft Electrician. After completing his BSEE degree, Mr. Nudi continued his military career in the United States Navy as a Nuclear Trained Submarine Officer where he held various positions responsible for submarine nuclear power plant operations, maintenance, and reliability performance. He was certified as Engineer Officer by US Naval Reactors (NAVSEA 08) in 1993. He retired from the Navy in 2005 at the rank of Commander. Following his Military Career, he started work in the commercial nuclear power industry with Duke Energy where he held various engineering management positions at both McGuire Nuclear Station and Corporate Engineering. While at Duke Energy, Mr. Nudi developed and implemented the Nuclear Fleet Monitoring & Diagnostic Center and all associated models, procedures, and staff training as the Program Manager. He was the honorable recipient of the 2016 EPRI Technology Transfer Award for his program. Mr. Nudi also served on the EPRI Advanced Remote Monitoring Technical Advisory Committee as Chairperson. He retired from Duke Energy in 2018.
Data Science Intern
Sanchita is a graduate student pursuing a Master's Degree in Data Science from The University of North Carolina at Charlotte. She has a Bachelor's Degree in Computer Engineering and previous internship experience in data analytics. for Integral Analytics, she has:
Developed a predictive model to prevent a unit trip condition saving at least 100 thousand dollars per event for a natural gas combined-cycle power plant with EMERSON Electric and Xcel Energy.
Analyzed inlet air filter’s differential pressure to plan cleaning schedules to avoid substantial loss of energy efficiency using advanced machine learning algorithms.
Developed MATLAB applications and operator/engineering visualizations in PowerBI to assist in duplication of model for other units.
Increased reliability and availability with reduced downtime by monitoring generator exciter currents to avoid heating of the rotor by using a proprietary multivariate estimation technique (simpler version of neural networks).
Automated key knowledge engineering processes using text analytics and natural language processing in Python.