Elevator Door Motion Categorization Using Multi-Dimensional Sensor Analysis
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Solution Overview
Problem
Existing elevator monitoring systems struggle to accurately categorize door motion events, particularly short door reversal operations, which can be missed due to duration-based analysis, leading to unreliable health assessments.
Innovation Solution
An elevator monitoring system that utilizes multiple sensor data events, including accelerometer data and additional information from events like elevator car stops and starts, to categorize door motions reliably, even at low power and low sampling rates, using algorithms to match sensor data patterns for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If duration-based analysis is used to categorize door motion, then the system is simple to implement, but short door reversal operations are missed leading to unreliable health assessments
Solution Approach 1:
The system transitions from analyzing only the duration dimension of door motion to analyzing multiple dimensions including acceleration patterns, velocity profiles, and temporal sequences of motion events. This multi-dimensional analysis enables reliable detection of short reversal operations that would be missed by duration-based methods alone.
Solution Approach 2:
The system performs preliminary classification of door motion events by identifying characteristic patterns in acceleration and velocity data before making final categorization decisions. This preliminary analysis allows the system to reliably distinguish between normal operations and reversal events even when duration is similar.
2Measurement precision
If high sampling rates are used to capture all door motion events, then measurement precision is improved, but power consumption increases
Solution Approach 1:
The system maintains continuous monitoring capability at low sampling rates by using event-triggered detection that activates analysis only when door motion events are detected. This approach preserves measurement precision for relevant events while minimizing power consumption during idle periods.
Solution Approach 2:
The system dynamically adjusts sampling parameters based on detected motion states. During normal operation, low sampling rates are used to conserve power. When door motion is detected, the system increases sampling resolution temporarily to capture precise motion characteristics, then returns to low-power mode.
3Measurement precision
If multiple sensor data events are analyzed to improve categorization accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system segments the door motion analysis into distinct phases: detection phase (identifying motion events), characterization phase (analyzing acceleration and velocity patterns), and classification phase (categorizing the motion type). This segmentation allows multiple sensor data events to be processed systematically without overwhelming system complexity.
Solution Approach 2:
The system introduces intermediate processing layers that transform raw sensor data from multiple sources into standardized motion event representations. These intermediaries normalize data from accelerometers, encoders, and other sensors, making it easier to integrate and analyze multiple data streams without increasing overall system complexity.
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
Enhances the reliability of elevator door motion categorization, providing robust health indicators with reduced power consumption and installation complexity, suitable for battery-powered or energy-harvesting systems.
Implementation Method 1
In some examples the one or more sensors comprises an accelerometer and the controller is arranged to acquire accelerometer data. An accelerometer can be used to detect accelerations (including vibrations) in the system and these can be used to identify (amongst other things) movement of the elevator car doors.
Data Source
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AI summary
An elevator monitoring system, comprising: one or more sensors; a controller arranged to: acquire sensor data from the one or more sensors; analyse the sensor data to identify a first event and a second event, the first event corresponding to an elevator door motion; categorize the first event based on the sensor data for the first event and the second event. Categorizing the elevator door motion is useful for determining characteristics relating to the health of the elevator door system. For example, the number of times that the elevator car door has opened is a useful indicator of elevator system health (which can be used to indicate a need for maintenance). Additionally or alternatively, the number of times that the elevator car door has undergone a reversal motion is a useful indicator of elevator system health. A reversal motion is a closing motion which commenced, but did not finish, resulting in a re-opening of the elevator door.