Residential Plumbing Remaining Life Prediction From Property Data
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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 of homes and their 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.
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 reliability of the remaining useful life evaluation is insufficient
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between raw residential data and remaining useful life predictions. This model processes multiple data types (geospatial, environmental, utility, maintenance records) and transforms them into accurate reliability assessments, resolving the contradiction by providing high measurement precision through a structured computational intermediary rather than simple conventional techniques
Solution Approach 2:
The assessment system is designed to handle multiple data types and assess multiple residential subsystems (plumbing, electrical, HVAC, structural) using a unified machine learning framework. This multi-functional approach enables accurate evaluation across diverse property characteristics while maintaining a consistent assessment methodology, balancing complexity with comprehensive precision
2Reliability
If comprehensive residential data is collected for accurate assessment, then the reliability of the evaluation improves, but the difficulty of obtaining and processing data increases
Solution Approach 1:
The system performs preliminary data collection and processing by integrating multiple data sources (geospatial databases, environmental agencies, utility providers, maintenance records) before the actual assessment occurs. This pre-gathering of comprehensive data from various external sources reduces the difficulty of obtaining information during the assessment process while maintaining high reliability through multi-source validation
Solution Approach 2:
The machine learning model incorporates feedback loops that continuously learn from maintenance outcomes and actual subsystem failures. This feedback mechanism improves the model's ability to reliably predict remaining useful life by adjusting predictions based on historical data, thereby enhancing reliability while the system becomes more efficient at processing comprehensive data over time
3Loss of energy
If traditional maintenance scheduling is used, then the operational simplicity is maintained, but the resource consumption and potential damage from failures increase
Solution Approach 1:
The system performs preliminary assessment of remaining useful life for multiple subsystems and generates prioritized maintenance recommendations before failures occur. By predicting which components need attention soonest and why, the system enables proactive maintenance scheduling that reduces energy loss and resource consumption from failures while maintaining operational simplicity through clear, actionable recommendations presented to users
Solution Approach 2:
The system changes the maintenance scheduling parameter from fixed time intervals to dynamic predictions based on actual subsystem condition and remaining useful life. This parameter transformation optimizes resource consumption by scheduling maintenance only when necessary rather than on rigid schedules, while the machine learning model handles the complexity of calculating dynamic schedules, preserving ease of operation for end users
Data Source
AI summary
A system for evaluating aspects of a residential plumbing system may (1) receive, via a user device, a user input indicative of an address for the residential property; (2) communicate the address to an external database, the external database containing a plurality of residential records associated with the address in a geographic location; (3) receive, via the plurality of residential records, a plurality of plumbing system aspects and a plurality of residential property aspects; (4) predict an estimated remaining lifespan of the plumbing system based upon the plurality of plumbing system aspects and the plurality of residential property aspects; and/or (5) generate a notification, the notification comprising the estimated remaining lifespan of the plumbing system and one or more recommended actions.