Elevator Load Monitoring for Accurate Entrapment Detection
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Solution Overview
Problem
Existing elevator monitoring systems face challenges in accurately detecting passenger entrapment due to unreliable load weighing devices influenced by rope weight, interior decoration, and inertia effects, leading to increased costs and complexity, and fail to meet government requirements for rapid entrapment reporting.
Innovation Solution
An elevator monitoring system with a gateway device, load weighing device, and elevator controller that calculates a minimum reference load value based on real-time load data, using wireless communication to send alarms to a remote server when passenger entrapment is detected, while accounting for door zone and speed information to ensure accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional sensors (cameras, infrared detectors, positioning sensors) are installed to detect passenger entrapment, then detection capability is improved, but system complexity and maintenance costs increase
Solution Approach 1:
The patent extracts the entrapment detection function from the complex sensor system and concentrates it in the load weighing device. By removing unnecessary sensors (cameras, infrared detectors, positioning sensors) and relying solely on the existing load weighing device with optimized algorithms, the system achieves reliable entrapment detection while significantly reducing device complexity and maintenance requirements.
Solution Approach 2:
The load weighing device performs self-calibration and automatic adaptation to changing conditions (rope weight, interior decoration, inertia effects) through its algorithmic processing. The system serves itself by automatically learning the elevator's operational characteristics and adjusting detection thresholds accordingly, eliminating the need for external sensors and manual calibration procedures.
2Device complexity
If load weighing device is used to detect passenger presence, then system simplicity is maintained, but measurement precision is reduced due to rope weight, interior decoration, and inertia effects
Solution Approach 1:
The system performs preliminary learning of the elevator's baseline characteristics (rope weight, interior decoration weight, acceleration patterns) before actual entrapment detection occurs. By pre-calculating and storing reference values for these parameters, the system can later accurately determine passenger presence by comparing current measurements against the learned baseline, thereby achieving high measurement precision without increasing device complexity.
Solution Approach 2:
The load weighing device incorporates feedback mechanisms that continuously monitor and adjust for changing conditions. The system uses feedback from the elevator's operational state (acceleration, deceleration, door operations) to dynamically compensate for inertia effects and environmental variations, maintaining measurement precision while keeping the system simple.
3Loss of time
If traditional load weighing value comparison is used to detect entrapment, then response speed is fast, but detection reliability is reduced due to inertia effects and variable baseline conditions
Solution Approach 1:
The system performs preliminary learning of the elevator's baseline load characteristics during normal operation, storing reference values for rope weight, interior decoration, and operational variations. This pre-processing occurs before actual entrapment events, ensuring that when entrapment detection is needed, the system already has accurate baseline data for reliable comparison, thereby maintaining both fast response and high reliability.
Solution Approach 2:
The system dynamically adjusts detection thresholds and baseline values based on real-time operational conditions. Instead of using fixed thresholds, the system adapts its reference values according to the elevator's current state (acceleration, deceleration, door operations), ensuring reliable entrapment detection across varying conditions while maintaining rapid response capability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides robust and timely detection of passenger entrapment by learning actual load conditions, reducing sensor complexity and maintenance costs, and ensuring compliance with government reporting requirements.
Implementation Method 1
The load weighing device monitors the current load value of the elevator car in real time and transmits the current load value to the gateway device
Data Source
AI summary
Provided is an elevator monitoring system, including a gateway device, a load weighing device and an elevator controller; the elevator controller is configured to collect status information of an elevator car and fault information when an elevator fails; the gateway device is configured to obtain fault information and status information from the elevator controller; the load weighing device monitors current load value of the elevator car in real time and transmits the current load value to the gateway device; the gateway device calculates a minimum reference load value corresponding to each stopping floor level of the elevator car based on the current load value, updates and records the minimum reference load value in real time; based on the fault information and the status information, if the gateway device determines that the current load value is greater than the minimum reference load value, the gateway device will send a warning alarm.