Elevator Car Load Baseline Detection for Entrapment Alerts
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Solution Overview
Problem
Existing elevator systems struggle to accurately detect entrapment situations, as sensor-based solutions cannot differentiate between human passengers and non-human objects, leading to potential safety risks and delays in addressing inoperable conditions.
Innovation Solution
A method and computing unit that analyze telemetry data from elevator car loads over multiple travels to establish a baseline load value, comparing it with the current load to detect significant deviations indicative of human entrapment, using parameters like mass, power, torque, and current data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If sensor-based solutions (weight measuring devices) are used to detect entrapment situations, then detection capability is provided, but the ability to differentiate between human passengers and non-human objects is lost
Solution Approach 1:
The system changes the parameter being measured from simple weight to a composite analysis including weight, travel pattern, frequency of use, and comparison with baseline values. This multi-parameter approach enables differentiation between human passengers and non-human objects without requiring additional sensors, thus resolving the contradiction between detection capability and measurement precision.
2Device complexity
If manual reporting by passengers is required, then system complexity is reduced, but response time and safety are worsened due to delays in addressing entrapment situations
Solution Approach 1:
The system implements self-service detection by automatically monitoring weight values and comparing them against baseline data to identify entrapment situations. This eliminates the need for manual passenger reporting while maintaining low system complexity, thus resolving the contradiction between device complexity and response time.
3Measurement precision
If baseline load values are established through multiple travels observation, then detection accuracy is improved, but the time required for system initialization and adaptation increases
Solution Approach 1:
The system performs preliminary action by continuously collecting and analyzing weight data during normal operation to establish baseline load values before entrapment detection is critically needed. This preliminary data collection and baseline establishment occurs in the background during regular elevator use, minimizing the impact on operational time while ensuring accurate detection readiness.
Data Source
AI summary
The invention relates to a method for detecting an entrapment situation inside an elevator car. The method comprising: obtaining a telemetry message after a configurable number of travels of the elevator car, wherein each telemetry message comprises load values representing a load of the elevator car during the configurable number of travels of the elevator car; observing the load values of a predefined number of the most recent travels of the elevator car to define a constant baseline load value representing the load of the elevator car without human objects inside it; comparing the load value of the last travel of the elevator car to the constant baseline load value; and detecting the entrapment situation inside the elevator car, if the result of the comparing indicates that the load value of the last travel is substantially greater than the constant baseline load value. The invention relates also to a computing unit and a detection system for detecting an entrapment situation inside an elevator car.