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Maintenance time and logistical delays drive high sustainment costs across any maintenance environment. Artificial Intelligence/Machine Learning can automate the analysis in order to optimize maintenance planning, scheduling, and execution.

AIPS was developed with a wide variety of applications in mind, virtually any maintenance environment where the point of performance data is collected.

  • Air, Land, and Sea
  • Defense
  • Transportation
  • Energy
  • Commercial

Stand-Alone
AIPS can be utilized as a Stand-Alone application in support of existing Maintenance Management Systems

Embedded in Existing MMS
AIPS can be embedded within an existing Maintenance Management System

The chart below highlights the AIPS (Artificial Intelligence Prognostic Steering™) system workflow:

AIPS Workflow Chart

AIPS provides this automation and is scalable to diverse data structures and advanced optimization. Available as a stand-alone or integrated within an existing maintenance management system, AIPS provides the most current analysis of the most recent data available.

Machine Learning – and AI – are the implementation of statistical models and techniques within an information system architecture that enables iterative:

  • Collection of new data
  • Machine-driven adjustment of the model parameters to improve performance

    In similar fashion to most engineering work, developing a machine learning algorithm involves:

    • Structuring the problem in a way that it can be addressed by one (or a combination of) the tools available. Every needs to be turned into one of a finite set of structures so it can be processed using an existing tool – it needs to be structured into either a nail, a screw, a bolt, or a fastener.
    • Processing the raw data to clean, extract, and identify the relevant features.
    • Feeding the subset of the processed data into a mathematical model.
    • Adjusting the model parameters to optimize its performance against a separate subset of test data.
    • Developing an information system that allows for the periodic retraining of the model.

    The chart below highlights the Machine Learning workflow:

    AIPS Machine Learning Workflow Chart

    AIPS testing within the Department of Defense

    N

    Improved 1st attempt failure resolution by 300%

    N

    Reduced solution sets size by an average of 40%

    N

    Reduced 54% of solution sets to a single solution

    N

    Increased False Alarm identification by 72%

    N

    Decreased Maintenance due to False Alarms by 70%

    N

    Decreased repair times by an average of 15.4%

    AIPS Solutions – what we can achieve

    N

    Increase Readiness rates

    N

    Forecast Failures by component, time and location

    N

    Identify False Alarms (FA)

    N

    Optimize Maintenance Procedures

    N

    Reduce Maintenance Costs

    N

    Reduce Maintenance Times

    N

    Reduce Panel Intrusions

    N

    Reduce Unnecessary Maintenance due to FA

    N

    Reduce Unnecessary Maintenance due to FA

    N

    Improve Root Cause Analysis

    Next-Gen features to include:

    N

    Account for weather in failure prediction

    N

    Account for Operations in failure prediction

    N

    Adjudicate Maintenance Data

    N

    Feed Supply for optimized provisioning

    N

    Account for Operator/Maintainer in failure forecasting

    N

    Feed Mission Planning/Forecasting

    BOOK A DEMO

    Learn how Artificial Intelligence Prognostic Steering™ with AIPS will benefit your business. We provide a customized tour and can answer all your questions.

    AIPS GUIDE

    Download our Artificial Intelligence Prognostic Steering™ with AIPS brochure for a more in-depth guide to available solutions.

    Corporate Headquarters

    615 Lynnhaven Parkway,
    Suite 104,
    Virginia Beach, VA 23452

    330 Crossing Blvd.,
    Orange Park, FL 32073

    USA Offices

    Crystal City, VA
    Havelock, NC
    Oklahoma City, OK
    Patuxent River, MD
    San Diego, CA

    Get in Touch

    757-340-9070
    904-637-2020

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