Predictive Maintenance Model Design for Non-Expert Fleet Analysis
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Solution Overview
Problem
The complexity and volume of maintenance and operational data from equipment fleets, such as aircraft, overwhelm typical safety or maintenance engineers, making effective predictive maintenance analysis time-consuming and laborious, and machine learning techniques are often out of reach for non-specialists.
Innovation Solution
A predictive maintenance model design system that integrates and preprocesses historical data, visualizes trends, and generates predictive models using machine learning, allowing users to define custom data features and algorithms without extensive programming expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning techniques are applied to analyze maintenance and operational data, then predictive maintenance capability is improved, but the complexity and expertise required increases significantly
Solution Approach 1:
The patent introduces an automated model design system that acts as an intermediary between raw maintenance data and predictive maintenance decisions. This system automatically performs feature engineering, model selection, and hyperparameter tuning, eliminating the need for users to directly engage with complex machine learning techniques while still achieving predictive maintenance capabilities.
Solution Approach 2:
The system enables non-expert users to generate predictive maintenance models through automated processes that self-adjust and optimize parameters without human intervention. The automated feature engineering and model tuning allow the system to serve itself, reducing the expertise barrier while maintaining high predictive capability.
2Ease of operation
If automated model design systems are implemented, then ease of use is improved, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary automated feature engineering and data preprocessing before model training, organizing and transforming raw data into meaningful features in advance. This preliminary action reduces the computational burden during the actual model training phase, balancing ease of use with resource consumption.
Data Source
AI summary
A data processing system for generating predictive maintenance models is disclosed, including one or more processors, a memory including one or more digital storage devices, and a plurality of instructions stored in the memory. The instructions are executable by the one or more processors 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 receive a rule set for processing a first attribute of the operation data and calculate a custom data feature from the historical dataset according to the received rule set. The instructions are further executable to generate a predictive maintenance model, using the custom data feature according to a machine learning method.