Predictive Maintenance Model Workflow for Complex Equipment Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Typical safety or maintenance engineers face challenges in analyzing large datasets from equipment maintenance and operation due to complexity and lack of expertise in machine learning, making effective predictive maintenance difficult with existing tools like Excel or Tableau.
Innovation Solution
A predictive maintenance model design system that includes a data processing system with a graphical user interface for visualizing and analyzing historical datasets, allowing users to select operational data features and generate predictive models using machine learning methods without requiring extensive data science or programming skills.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning techniques are applied to analyze maintenance data, then predictive maintenance capability is improved, but the complexity of the system increases due to requirements for data science expertise and programming skills
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 includes automated data preprocessing, feature engineering, model selection, and hyperparameter tuning components that eliminate the need for users to have data science expertise. The system translates complex machine learning processes into user-friendly automated workflows, resolving the contradiction by maintaining high predictive capability while reducing system complexity for end users.
Solution Approach 2:
The system performs self-service by automatically executing the entire machine learning pipeline without human intervention. It autonomously preprocesses data, selects relevant features, chooses appropriate models, tunes hyperparameters, and generates predictions. This self-service capability allows maintenance personnel to obtain predictive insights without needing to understand or implement complex machine learning techniques themselves, thus improving reliability while managing complexity.
2Ease of operation
If traditional tools like Excel or Tableau are used for data analysis, then ease of operation is maintained, but the ability to perform effective predictive maintenance analysis deteriorates due to data complexity and quantity
Solution Approach 1:
The patent replaces manual mechanical analysis processes (spreadsheets, manual charting in Tableau) with an automated computational system. The system uses automated data preprocessing pipelines, algorithmic feature engineering, and automated model training to handle large complex datasets that cannot be effectively analyzed with traditional manual tools. This substitution maintains ease of operation by providing automated workflows while dramatically improving predictive maintenance capability through advanced machine learning techniques.
3Measurement precision
If comprehensive data analysis is performed to improve predictive accuracy, then the quality of predictive models is improved, but the time and labor required for analysis increases
Solution Approach 1:
The system performs preliminary actions by pre-processing data, pre-selecting features, and pre-configuring models before actual predictive analysis is needed. It establishes automated pipelines that prepare data in advance, identify relevant features beforehand, and configure appropriate models in advance. When predictive maintenance insights are needed, the system can quickly execute pre-configured analysis rather than performing comprehensive analysis from scratch, thus improving predictive accuracy while reducing analysis time.
Solution Approach 2:
The system automatically adjusts analysis parameters such as data sampling rates, feature selection criteria, model complexity levels, and hyperparameter values based on the specific dataset and problem context. By dynamically changing these parameters, the system optimizes the balance between comprehensive analysis for accuracy and computational efficiency for speed, achieving high predictive accuracy without requiring excessive analysis time and resources.
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, and including maintenance data and operational data. The instructions are further executable to receive a first selection of a first operational data feature and a first system, and display operational data associated with the first operational data feature and the first system, and maintenance data associated with the first system, on a timeline in a graphical user interface. The instructions are further executable to receive a second selection of a second operational data feature and generate a predictive maintenance model using the second operational data feature according to a machine learning method.