Pool Equipment Stall Detection on Sloped Obstacles Using Motion Data
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Solution Overview
Problem
Dynamic pool equipment units often enter a stalled state when unable to advance over obstacles, leading to inefficient operation and potential damage due to prolonged motor usage and increased power consumption, as existing stall detection methods rely on timeout mechanisms that can be overly long and inefficient.
Innovation Solution
A computer-implemented method and system using trained statistical models to detect the stalled state by analyzing movement and operational features of pool equipment units, allowing for immediate identification of the stalled state and reducing the time the unit spends in this state, thereby minimizing damage and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If timeout mechanisms are used for stall detection, then the system can identify stalled states, but the detection time is overly long and reduces operational efficiency
Solution Approach 1:
The system performs preliminary analysis by continuously monitoring movement features and operational features before a complete timeout occurs. The statistical model is trained on normal movement patterns to detect deviations early, allowing the system to identify stalled states before the full timeout period elapses, thus reducing detection time while maintaining accuracy
Solution Approach 2:
The patent replaces the traditional mechanical timeout-based detection system with a statistical model-based detection system. Instead of waiting for a fixed time period to elapse, the system uses machine learning models to analyze movement features and operational features in real-time, substituting the time-based mechanical approach with an intelligent analytical approach that can detect stalls more quickly and accurately
2Productivity
If the pool equipment unit continues attempting to advance in a stalled state, then it may overcome the obstacle, but prolonged motor usage causes wear and tear and increases power consumption
Solution Approach 1:
The system implements feedback by continuously monitoring movement features and operational features, comparing them against normal patterns using statistical models. When the model detects a stalled state with high confidence, the system provides feedback to stop the motor, preventing continued energy consumption. This closed-loop feedback mechanism allows the system to adapt its behavior based on real-time conditions, reducing power loss while maintaining task completion capability
Solution Approach 2:
The system changes the operational parameters by transitioning from continuous motor operation to conditional operation based on detected state. The statistical model analyzes multiple features (movement speed, acceleration, motor current, etc.) and dynamically adjusts the motor operation parameter - either continuing operation or stopping - based on the detected state, thereby optimizing energy consumption while maintaining productivity
3Productivity
If the pool equipment unit continues attempting to advance in a stalled state, then it may overcome the obstacle, but prolonged operation causes wear and tear on the motor
Solution Approach 1:
The system implements feedback by continuously monitoring movement features and operational features, comparing them against normal patterns using statistical models. When the model detects a stalled state with high confidence, the system provides feedback to stop the motor, preventing continued operation that would cause wear and tear. This closed-loop feedback mechanism allows the system to adapt its behavior based on real-time conditions, protecting motor durability while maintaining task completion capability
Solution Approach 2:
The system applies beforehand cushioning by using the statistical model to predict and detect stalled states before they cause significant damage. The continuous monitoring and analysis of movement features and operational features allow the system to identify the onset of a stalled condition early, stopping the motor before prolonged operation can cause extensive wear and tear, thus cushioning against potential reliability issues
Data Source
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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.