Building Asset Notifications Using ML for Preventive Maintenance
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Solution Overview
Problem
Small and medium-sized building owners lack reliable tools to monitor and manage building assets effectively, leading to delayed maintenance, increased costs, and potential hazards due to a lack of scalable and affordable solutions for risk mitigation and asset management.
Innovation Solution
A method for generating and transmitting customized notifications regarding the status of building assets using a computing device that receives maintenance data from sensors, analyzes it with a machine learning engine, and sends alerts to stakeholders, facilitating proactive maintenance and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional building management systems are used, then large enterprise sized buildings can be monitored, but small and medium sized buildings cannot be served due to lack of scalability
Solution Approach 1:
The system segments building monitoring into modular components: sensor modules for data collection, communication modules for data transmission, and processing modules for analysis. This segmentation allows the system to be scaled and configured appropriately for small and medium sized buildings without requiring complex enterprise-level infrastructure.
Solution Approach 2:
The building management system is designed with universal functionality that can serve multiple building sizes and types. The same core architecture and communication protocols are used across different building scales, allowing small and medium sized buildings to access the same monitoring capabilities as large enterprises through a standardized platform.
2Reliability
If reactive maintenance systems are used, then immediate response to failures is possible, but delayed detection of issues leads to catastrophic failures and higher costs
Solution Approach 1:
The system performs preliminary actions by continuously monitoring building assets and detecting potential issues before they become catastrophic failures. Sensors detect early signs of problems such as temperature anomalies, vibration patterns, or equipment performance degradation, triggering notifications that enable preventive maintenance before actual failure occurs.
Solution Approach 2:
The system implements continuous feedback loops where sensor data is constantly collected, analyzed, and used to generate real-time notifications. This feedback mechanism ensures that building owners and maintenance personnel are immediately informed of asset status changes, enabling timely responses to prevent catastrophic failures.
3Loss of energy
If no maintenance monitoring system is used, then operational costs are lower initially, but lack of visibility into asset status leads to increased costs from delayed maintenance and catastrophic failures
Solution Approach 1:
The system introduces an intermediary layer between building assets and owners: a network of sensors and communication modules that bridge the information gap. These intermediaries continuously monitor asset status and transmit data to owners, providing visibility into conditions that would otherwise be invisible and enabling informed maintenance decisions.
Solution Approach 2:
The system replaces manual inspection and reactive maintenance with automated electronic monitoring and data transmission. Sensors electronically detect asset conditions and automatically transmit information through communication networks, substituting mechanical inspection methods with electronic surveillance that provides continuous, real-time visibility into asset status.
Data Source
AI summary
A method for generating and transmitting customized alerts regarding a status of at least one building asset of a building includes receiving, by a computing device, for at least one building asset of a building, maintenance data associated with the at least one building asset, at least one maintenance datum received from at least one sensor associated with the at least one building asset. A machine learning engine analyzes the received maintenance data to identify at least one characteristic associated with the at least one building asset. The method includes generating, for the at least one building asset, a notification associated with the at least one building asset, responsive to the identified at least one characteristic. The method includes transmitting, for the at least one building asset, to at least one user associated with the at least one building asset, at least one notification associated with the generated notification.


