Gate Sensor Attachment for Automated State and Fault Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Parking facilities face challenges in determining the operational state of autonomous gates, leading to safety issues and potential damage due to undetected malfunctions, as human oversight is unreliable and existing systems lack effective automated monitoring.
Innovation Solution
An edge device is deployed at the parking facility to collect sensor data from gates, using machine learning models to determine the positional and health state of the gates, triggering remedial actions such as alerts or physical modifications when issues are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous gates are used to automate parking facility operations, then operational efficiency is improved, but reliability deteriorates due to lack of human oversight and difficulty in detecting malfunctions
Solution Approach 1:
The patent implements a feedback mechanism where sensor data from the gate is continuously collected and analyzed by a machine learning model to detect malfunctions and provide alerts, creating a closed-loop monitoring system that maintains reliability while preserving automation
Solution Approach 2:
The patent replaces manual inspection and human oversight with an automated sensor-based monitoring system using machine learning algorithms, substituting mechanical/human detection methods with electronic and computational systems
2Reliability
If manual inspection of gates is used, then reliability is maintained through human awareness, but productivity deteriorates due to inability to provide continuous monitoring
Solution Approach 1:
The gate monitoring system performs self-service by automatically collecting sensor data, analyzing it through machine learning models, and generating alerts without requiring human intervention, enabling continuous autonomous monitoring
Solution Approach 2:
The patent substitutes manual human inspection with an automated electronic monitoring system that uses sensors and machine learning to detect gate malfunctions, eliminating the need for physical human presence while improving monitoring capability
3Reliability
If sensor-based monitoring is implemented, then reliability is improved through automated detection, but device complexity increases due to additional hardware and machine learning models
Solution Approach 1:
The patent implements a universal monitoring system where a single edge device performs multiple functions including data collection, machine learning inference, alert generation, and communication, consolidating complexity into a multi-functional unit rather than separate specialized components
Data Source
AI summary
An edge device receives sensor data from a sensor affixed to a moveable gate. The edge device determines the positional state of the moveable gate based on the sensor data by inputting the received data into a machine learning model or by comparing the sensor data to values associated with a positional state through a calibration process. The edge device stores a log that associates the positional state and sensor data. The edge device determines the health state of the moveable gate using a machine learning model that is trained to predict, based on input of a new log, the health state of the gate. Responsive to determining that the health state of the gate is unhealthy, the edge device triggers a remedial action.