Our focus on energy analytics, signal processing, Artificial Intelligence, and Machine Learning also lead to the creation of a corporate spin-off. Named “MHI Energy Information Solutions, Inc.” (MHI), the company develops and implements decision support systems for strategic energy management - integrating primary R&D, legacy systems, and emerging technologies. We assess “What is?” to answer “What if?” focusing on:

Simulation software (e.g., building energy modeling and demand forecasting)

Distributed energy design

Integrated power control and management

Power generation and storage technologies

System reliability (e.g., signal processing and analysis) and financial modeling.


Copyright 2020: International Association of Virtual Organizations, Inc. All rights reserved.
 

IAVOlogobottombanner
IAVOlogord backgroundcomposite1Xn

Systems Development: Dynamic Power Management

IPSManager™  is a power management tool, interoperable with alternative fuel sources, that helps optimize the use of established energy production devices such as diesel engines. The closed-circuit network of a ship is the intended application, but the solution is scalable to other domains as well (e.g., localized smart grid systems). As ships take on a wider range of technologies -- that require large expenditures of energy -- and move to distributed dedicated storage devices, even moderate gains in energy management algorithms can provide enormous benefits when spread over the entire naval fleet. Current methods limit optimization of energy resources in that they do not anticipate future energy requirements, thus limiting strategic energy allocation within an integrated power system. 

IPSManager™  controls the production and allocation of energy aboard ships. The technology was formulated to address several critical shortcomings of current methods:  

The use of probabilistic Markov models to estimate future energy demands to find optimal strategies for real time energy allocation; 

Developing a trade-space between energy cost and quality of service (QOS) to efficiently maintain performance of critical systems; 

Incorporating support for distributed systems to ensure survivability and reliability of power supply to critical infrastructure in the event of catastrophic losses; 

Developing an intelligent processing method which marries the benefits of computationally intensive mathematical solutions with real time solutions; and 

Reducing life cycle costs through an interoperable open architecture that can be easily installed across multiple ship platforms and can readily incorporate energy demands for new devices. 

User Profile: The shipping industry, closed power circuit systems (e.g., non-tethered circuits such as electric cars), and modern industrial design. 

MHIlogo75 MHIScreenrd

http://mhi-energyinfo.com/Index.aspx

Our energy and power management work focuses primarily on control, remaining useful life, and fault management. That work has been aligned with ongoing needs of the U.S. Navy, the U.S. Army, and NASA.

The ongoing and legacy portfolio consists of multiple complimentary software programs.  

DyMA-FM (Dynamic Multivariate Assessment for Fault Management), a tiered hierarchical architecture for monitoring sensor data using evidence-based reasoning within a distributed processing environment for real-time detection and response to fault conditions. The systems engineering was conducted for NASA in support of NASA formalizing best practices of Integrated Systems Health Management.

DyMA-FM offers a robust approach to fault management (FM) by implementing functional/analytic redundancy. It allows for consideration of FM as information inspection, where a set of models for dynamic “normal” conditions are continuously updated as new information arrives and then used to predict future behavior of the system. Outlier information either signals a fault that can be handled automatically or invokes a higher-level model for detection of a larger issue. Each distributed logic engine operates at the time scale appropriate for the signals it monitors, improving computational efficiency in real-time analytics. Each signal is analyzed using wavelets for decomposition, change point estimates for fault identification, exponential smoothing for prognostics, and Monte Carlo simulation for risk analysis.

Unlike traditional methods for signal analysis, wavelets work in both time and frequency domains without loss of efficiency.

DyMA-FM can be applied to similar requirements for diagnostics and prognostics analyses in the related disciplines of Prognostics and Health Management (PHM), Condition-Based Maintenance (CBM), and Reliability-Centered Maintenance (RCM).

Our technology can be designed a core for future development, or embedded in one or more of the FM/diagnostics suites for commercial aviation, equipment maintenance, public utilities, and manufacturing. The U.S. DoD has issued several very specific directives to the service branches to initiate CBM and RCM protocols. Given tighter budgets and the need to extend asset life, there are incentives to detect and identify fault probabilities to improve design, establish more cost effective maintenance procedures, and improve readiness. Faced with an aging and increasingly fragile infrastructure, electric utilities are concerned with the reliability of prime generation systems and distribution equipment. Energy intensive or critical operations installations such as data centers and medical facilities would also benefit from FM diagnostic and prognostic tools.

IPSManagerScreenShotrd

IPSManager primary system segments and ongoing feedback loops

DYNFMex1

GenDPM™ (Generator Diagnostics and Prognostics Monitor) was developed for the US Army (re: "Heuristic-based Prognostic and Diagnostic Method for Installations") to address system reliability and resiliency within microgrids.

Using field data from maintenance and operating reports, manufacturer information, and data simulation, GenDPM applies non-linear diagnostic and prognostic models that estimate a running health index (HI), a diagnostic process that identifies degradation and faults, that is then used to determine remaining useful life (RUL), a prognostics process.

The work was based on the operation and installation of 750 MW diesel-electric standby generators.

The software solution can reside on a commercially available power control system directly on the equipment or as part of an analytical module in design software such as the Department of Energy (DOE) Sandia National Laboratories’ Microgrid Design Toolkit (MDT).

DyMA-FM tiered hierarchical architecture

GenDPMrd450

GenDPM plots detailing system health, remaining useful life, and failure threholds

GebDPM