Pool Cleaner Stall Detection Using Motion and Motor Features
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Solution Overview
Problem
Dynamic pool equipment units often enter a stalled state when attempting to advance over obstacles, leading to prolonged motor operation and increased power consumption, which can cause wear and tear and reduce operational efficiency.
Innovation Solution
A computer-implemented method and system using trained statistical models to detect the stalled state by analyzing movement and operational features of the equipment, allowing the unit to stop attempting to advance and reducing the duration of the stalled state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the pool equipment unit continuously attempts to advance over obstacles using timeout mechanisms, then it ensures complete obstacle crossing attempts, but it increases power consumption and causes wear and tear on motors
Solution Approach 1:
The system performs preliminary detection of stalled states by analyzing movement features and operational parameters before the timeout period expires. The trained statistical model identifies stalled conditions early based on patterns in motor current, velocity, and position data, allowing the system to stop unnecessary motor operation before the full timeout duration elapses, thus reducing power consumption while maintaining reliable obstacle crossing attempts
Solution Approach 2:
The system continuously monitors movement features and operational parameters during obstacle crossing attempts and uses feedback from the trained statistical model to detect stalled states in real-time. This feedback mechanism allows the system to adaptively stop motor operation when a stalled state is detected, preventing excessive power consumption and wear while ensuring that legitimate crossing attempts are completed
2Measurement precision
If the pool equipment unit uses lengthy timeout mechanisms to detect stalled states, then it reduces false detections, but it increases task execution time and reduces operational efficiency
Solution Approach 1:
The trained statistical model performs preliminary analysis of movement features and operational parameters to detect stalled states before the timeout period expires. By evaluating patterns in motor current, velocity, and position data in real-time, the system can accurately identify stalled conditions early, reducing task execution time while maintaining high detection accuracy through the model's trained recognition capabilities
Solution Approach 2:
The system replaces the traditional mechanical timeout-based detection mechanism with a trained statistical model that analyzes movement features and operational parameters. This substitution enables accurate stalled state detection based on pattern recognition rather than fixed time delays, significantly reducing task execution time while maintaining or improving detection accuracy
3Productivity
If the pool equipment unit operates motors during prolonged stalled states, then it maintains forward momentum attempts, but it causes increased wear and tear on motor components
Solution Approach 1:
The system uses feedback from the trained statistical model that continuously analyzes movement features and operational parameters to detect stalled states. When a stalled state is detected, the system provides feedback to stop motor operation, preventing excessive wear and tear while maintaining productivity by quickly identifying when forward movement attempts are futile and redirecting energy to other productive tasks
Data Source
AI summary
Disclosed herein is a method of detecting stalled state of a dynamic pool equipment unit, comprising receiving a plurality of movement features relating to a dynamic pool equipment unit deployed in a water pool which are captured during a predefined sampling window and comprise (1) motion features of the pool equipment unit, and (2) operational features of electric motor(s) of the pool equipment unit, determining a movement pattern of the pool equipment unit using one or more statistical models applied to the plurality of movement features which are trained to estimate a stalled state of the pool equipment unit in which the pool equipment unit is pitched up and unable to advance on a slopped obstacle in the water pool, and causing the pool equipment unit to stop attempted advance in a current direction responsive to determining that the pool equipment unit is in the stalled state.