Water Fixture Monitoring Interface for Predictive Maintenance
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Solution Overview
Problem
Existing water management systems lack comprehensive, intelligent, and customizable solutions for monitoring and controlling various water management fixtures, leading to inefficiencies in maintenance, resource usage, and operational inefficiencies.
Innovation Solution
A water management system with end point devices that collect data from fixtures, process it, and provide intelligent insights through a user interface, allowing users to customize settings, receive alerts, and schedule maintenance based on historical and predictive analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If comprehensive monitoring and control of water management fixtures is implemented, then operational efficiency and predictive maintenance are improved, but device complexity increases
Solution Approach 1:
The system divides water management fixtures into multiple independent end point devices, each capable of collecting and processing data locally. This segmentation allows comprehensive monitoring without requiring a single complex centralized system, as each end point device operates independently while contributing to the overall system intelligence.
Solution Approach 2:
The end point devices are designed with multi-functionality, capable of performing data collection, processing, analysis, and communication tasks. This universal design reduces the need for separate specialized components, thereby managing system complexity while achieving comprehensive operational efficiency across all water management fixtures.
2Measurement precision
If real-time data analysis and predictive analytics are provided, then maintenance scheduling accuracy is improved, but processing requirements and system complexity increase
Solution Approach 1:
The system performs preliminary data processing and analysis at the end point devices before transmitting data to central systems. By pre-processing data locally and preparing predictive maintenance schedules in advance, the system reduces real-time processing requirements while maintaining high accuracy in maintenance scheduling.
Solution Approach 2:
The system implements partial data processing at end point devices and partial central processing, distributing the processing load. This approach provides sufficient predictive analytics accuracy without requiring complete centralized processing, thereby managing overall system complexity while maintaining measurement precision.
3Ease of operation
If customizable control settings are provided for different users, then ease of operation is improved, but system configuration complexity increases
Solution Approach 1:
The user interface is designed to be dynamic and adaptable, automatically adjusting based on user roles, preferences, and interaction patterns. This dynamic customization allows each user to have a personalized interface without requiring manual configuration of complex system settings, as the system adapts automatically while maintaining ease of operation.
Solution Approach 2:
The system provides self-service customization capabilities where users can adjust their own interface preferences and control settings without requiring system administrator intervention. This self-service approach improves ease of operation while avoiding the complexity of manual system configuration, as users independently customize their experience.
Data Source
AI summary
A water management system including a first end point device in communication with a first fixture, the first end point including a first end point electronic processor configured to receive data associated with the first fixture and a first fixture identifier. The water management system also includes a non-transitory computer-readable medium storing instructions executable by at least one electronic processor to perform a set of functions, such as receiving the data associated with the first fixture and the first fixture identifier, associating the first fixture identifier with a first location classification and a first type classification, processing the data associated with the first fixture to organize the data based at least in part on the first location classification associated with the first fixture identifier or the first type classification associated with the first fixture identifier, and transmitting the organized data of the first fixture to a user device for display.