Fan Anomaly Detection Using Built-In Sensor Data and ML
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Solution Overview
Problem
Existing appliances with fans often fail to detect anomalies in fan performance before actual failure, leading to reduced capacity or other issues that can render the appliance inoperable.
Innovation Solution
A method involving connecting a service computer or remote server to the appliance to collect and analyze fan usage data using a machine learning model, flagging the fan for replacement when anomalies are detected, thereby predicting potential failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fan monitoring is used without advanced detection systems, then the system remains simple and cost-effective, but fan failures are not detected until they occur, leading to appliance inoperability
Solution Approach 1:
The fan motor incorporates built-in sensors and data collection capabilities that enable it to self-monitor its own operational parameters (current, speed, temperature) and transmit this data externally for analysis, eliminating the need for separate complex detection systems while maintaining high reliability
Solution Approach 2:
The patent replaces traditional mechanical monitoring methods with sensor-based electrical measurements and machine learning algorithms that analyze current signatures and operational data to detect anomalies, providing more reliable failure prediction without significant complexity increase
2Productivity
If fan operation continues without monitoring until failure, then the system operates continuously without interruptions, but the fan may fail unexpectedly causing appliance deactivation and operational disruptions
Solution Approach 1:
The system performs preliminary detection of fan anomalies by continuously monitoring operational parameters and comparing them against learned normal patterns, enabling early warning of potential failures before they occur, thus maintaining both productivity and reliability
Solution Approach 2:
The patent implements a feedback loop where fan operational data is collected, analyzed by machine learning models, and used to generate alerts or predictions about future failures, allowing proactive maintenance decisions that ensure continuous appliance operation
3Reliability
If fan usage data is collected and analyzed using machine learning models, then fan anomalies can be detected prior to failure, but additional data collection infrastructure and processing capability are required
Solution Approach 1:
The fan motor serves dual purposes by both operating the fan and collecting its own operational data through built-in sensors, eliminating the need for separate monitoring hardware and reducing overall system complexity while maintaining high detection accuracy
Solution Approach 2:
The patent makes the fan motor multi-functional by integrating data collection, self-diagnosis, and communication capabilities into it, allowing a single component to perform both its primary cooling function and the secondary function of predictive maintenance monitoring
Data Source
AI summary
A method of detecting anomalies of a fan within an appliance includes receiving, at a service computer and/or remote server, data indicative of usage of the fan, analyzing the data indicative of usage of the fan with a machine learning model on the service computer and/or remote server, and flagging the fan for replacement when the machine learning model detects an anomaly in the data indicative of usage of the fan.