Predictive Maintenance Model Building With Guided Feature Selection
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Solution Overview
Problem
Engineers face challenges in analyzing large datasets from equipment maintenance and operation, such as aircraft fleets, due to complexity and the need for specialized skills, making effective application of machine learning techniques difficult for typical safety or maintenance engineers.
Innovation Solution
A predictive maintenance model design system that includes a data processing system with processors and memory, using supervised machine learning classification models to evaluate correlation between operational data features and maintenance events, and generating predictive models through a graphical user interface, allowing for selection and visualization of key data features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning techniques are applied to analyze maintenance data, then predictive accuracy is improved, but the complexity of the system increases and requires specialized expertise
Solution Approach 1:
The patent introduces an automated machine learning system that acts as an intermediary between raw maintenance data and predictive insights. This system includes automated data preprocessing, feature engineering, model selection, and hyperparameter tuning components that bridge the gap between complex ML techniques and user-friendly predictions, eliminating the need for users to have specialized data science expertise while maintaining high predictive accuracy
Solution Approach 2:
The system performs self-service through automated model training and evaluation processes. The machine learning pipeline automatically selects appropriate algorithms, tunes hyperparameters, and validates models without requiring manual intervention from experts. This automation allows the system to serve itself in managing the complexity of machine learning while delivering accurate predictions to end users
2Productivity
If automated machine learning processes are implemented, then productivity is improved, but the ease of operation deteriorates due to technical complexity
Solution Approach 1:
The patent employs pre-configured machine learning templates and standardized analysis pipelines that can be copied and applied to different datasets. These templates encapsulate best practices and proven methodologies, allowing users to quickly deploy analyses without needing to understand the underlying complex processes. The system copies successful model configurations and adapts them to new data, maintaining ease of use while achieving high productivity
Solution Approach 2:
The system is designed with universal, multi-functional components that can handle various types of maintenance data and predict different failure modes using the same underlying platform. This universality allows a single system to perform multiple analysis functions without requiring users to learn different tools or processes, thereby maintaining ease of operation while delivering high productivity across diverse applications
Data Source
AI summary
A data processing system for generating predictive maintenance models is disclosed, including one or more processors, a memory, and a plurality of instructions stored in the memory. The instructions are executable to receive a historical dataset relating to each system of a plurality of systems, the historical dataset including maintenance data and operational data. The instructions are further executable to evaluate correlation between each of a plurality of operational data features and maintenance events of the maintenance data, using a supervised machine learning classification model. The instructions are further executable to display a quantitative result of the evaluation for each operational data feature in a graphical user interface, receive a selection of one or more operational data features of the plurality of operational data features, and generate a predictive maintenance model using the selected one or more operational data features according to a machine learning method.