Residential Plumbing Life Prediction Using Impact Scoring
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Solution Overview
Problem
Existing techniques are inefficient and cumbersome for assessing the well-being and remaining useful life of residential properties, making it difficult to obtain relevant information for maintenance or modifications that impact the integrity and safety of residential systems.
Innovation Solution
A computer system utilizing a trained machine learning model analyzes various types of residential data to generate a subsystem impact score, recommending maintenance actions, component additions or replacements, and providing a user interface to improve the subsystem's remaining useful life, thereby reducing potential damage, inefficiencies, and safety risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques are used to assess residential property well-being, then the assessment process is simple, but the accuracy and comprehensiveness of the remaining useful life prediction is insufficient
Solution Approach 1:
A trained machine learning model serves as an intermediary between raw residential data and remaining useful life predictions. The model processes multiple data types (geographical location, age, system characteristics, maintenance history) and transforms them into accurate predictions, resolving the contradiction by handling complexity internally while delivering precise results.
Solution Approach 2:
The patent replaces conventional manual assessment methods with an automated machine learning-based system. This substitution enables comprehensive data analysis and accurate predictions without requiring complex human expertise or manual processes, achieving high measurement precision through computational rather than mechanical means.
2Measurement precision
If comprehensive residential data is collected and analyzed, then the remaining useful life prediction accuracy improves, but the difficulty of obtaining and processing data increases
Solution Approach 1:
The machine learning model is designed to process multiple types of residential data (geographical location, property age, system characteristics, maintenance history) through a single unified framework. This multi-functional approach enables accurate predictions while simplifying data acquisition, as the system can handle diverse data sources through one standardized process.
Solution Approach 2:
The system automatically collects, processes, and analyzes residential data without requiring manual intervention. The machine learning model self-services by ingesting available data from various sources and generating predictions autonomously, reducing the difficulty of data acquisition and processing while maintaining high accuracy.
3Reliability
If proactive maintenance actions are taken based on predictions, then resource consumption and safety risks are reduced, but the complexity of implementing maintenance recommendations increases
Solution Approach 1:
The system performs preliminary analysis by predicting remaining useful life before actual failures occur. By providing advance predictions and maintenance recommendations, the system enables proactive maintenance actions that improve reliability while simplifying implementation, as issues are addressed before they become critical or complex emergencies.
Solution Approach 2:
The system provides feedback through actionable maintenance recommendations based on prediction results. This feedback loop guides users in implementing targeted maintenance actions, improving system reliability while reducing the complexity of maintenance implementation by providing clear, data-driven guidance rather than requiring users to navigate complex maintenance decisions independently.
Data Source
AI summary
A system for assessing a remaining useful life of a residential plumbing system may (1) receive a plurality of different types of residential data associated with the residential plumbing system; (2) determine, by processing the residential data using a trained machine learning model, a residential plumbing impact score, the residential plumbing impact score indicating the remaining useful life of the residential plumbing system, where the trained machine learning model is configured to: (3) estimate an impact of the residential data on the remaining useful life of the residential plumbing system, and (4) generate the residential plumbing impact score by predicting the remaining useful life of the residential plumbing system using the estimated impacts of the residential data on the remaining useful life; and/or (5) initiate an action relating to the residential plumbing system responsive to the generation of the residential plumbing impact score.