Equipment Lifecycle Data Integration for Performance and Cost Prediction
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Solution Overview
Problem
Legacy systems face challenges in integrating and analyzing dispersed equipment lifecycle data across multiple servers, leading to incomplete datasets and inefficient prediction of equipment performance and cost of ownership.
Innovation Solution
A system utilizing a visualization server that combines equipment lifecycle datasets from multiple servers, employing machine learning models to automatically extract visualization datasets, including lifecycle utilization and cost estimates, and outputs these for display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If equipment lifecycle data is stored across multiple distributed servers, then data collection capacity increases, but data integration and analysis efficiency deteriorates
Solution Approach 1:
The patent combines dispersed equipment lifecycle data from multiple distributed servers into a unified dataset. The system integrates purchasing data, deployment data, repair data, and usage data from various sources into a single comprehensive dataset that can be analyzed together, resolving the inefficiency of distributed data storage while maintaining the benefit of extensive data collection
Solution Approach 2:
The patent introduces an intermediary data integration layer that mediates between the distributed servers and the analysis system. This intermediary component automatically extracts and consolidates relevant features from multiple data sources, enabling efficient analysis without requiring direct access to all distributed servers
2Device complexity
If manual methods are used to integrate and analyze dispersed data, then system complexity is reduced, but analysis time and resource consumption increase
Solution Approach 1:
The patent implements automated self-service mechanisms where the system automatically extracts features, integrates data, and generates analysis results without requiring manual intervention. The system autonomously processes equipment lifecycle data from multiple servers, performs feature extraction, and produces integrated datasets, significantly reducing analysis time while managing complexity through automation
Solution Approach 2:
The patent performs preliminary data processing and feature extraction automatically before analysis is needed. The system pre-integrates data from multiple sources and pre-processes the information into usable formats, so that when analysis is required, the work is already substantially complete, reducing actual analysis time
3Measurement precision
If comprehensive equipment lifecycle data is collected from multiple sources, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent extracts only the most relevant features and data elements from comprehensive equipment lifecycle datasets. Rather than processing all collected data, the system identifies and extracts key features such as usage patterns, repair histories, and performance metrics that are most predictive of equipment behavior, maintaining high prediction accuracy while reducing processing complexity
Solution Approach 2:
The patent applies different processing methods and feature extraction techniques to different types of data based on their specific characteristics. Purchasing data, deployment data, repair data, and usage data are processed with appropriate methods tailored to each data type's properties, improving prediction accuracy while managing complexity through specialized processing
Data Source
AI summary
An example system includes a set of servers configured to store a plurality of equipment lifecycle datasets, a display, and a visualization server in operable communication with the plurality of servers and the display, where the visualization server comprises a processor and a non-transitory memory storing instructions, that, when executed, cause the processor to: receive the plurality of equipment lifecycle datasets from the plurality of servers; automatically extract, from the plurality of equipment lifecycle datasets, a visualization dataset; and output the visualization dataset for display.