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 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 testing within the Department of Defense
Improved 1st attempt failure resolution by 300%
Reduced solution sets size by an average of 40%
Reduced 54% of solution sets to a single solution
Increased False Alarm identification by 72%
Decreased Maintenance due to False Alarms by 70%
Decreased repair times by an average of 15.4%
AIPS Solutions – what we can achieve
Increase Readiness rates
Forecast Failures by component, time and location
Identify False Alarms (FA)
Optimize Maintenance Procedures
Reduce Maintenance Costs
Reduce Maintenance Times
Reduce Panel Intrusions
Reduce Unnecessary Maintenance due to FA
Reduce Unnecessary Maintenance due to FA
Improve Root Cause Analysis
Next-Gen features to include:
Account for weather in failure prediction
Account for Operations in failure prediction
Adjudicate Maintenance Data
Feed Supply for optimized provisioning
Account for Operator/Maintainer in failure forecasting
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.
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