Elevator Driving Unit Predictive Maintenance via Current Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for maintaining elevator driving units are inadequate in predicting breakdowns and preventing safety accidents, as they rely on infrequent inspections and lack real-time monitoring of abnormal symptoms.
Innovation Solution
A method that distinguishes between upward and downward elevator movements, collects and classifies driving information into different operational sections, sets critical levels, and detects abnormal symptoms in real-time by comparing current values with established limits, enabling precise predictive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If regular inspection is performed every 2 years or less, then safety of the elevator is maintained, but breakdown prediction capability is insufficient and maintenance efficiency is low
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing driving information (current values) to predict breakdowns before they occur. The predictive maintenance approach proactively identifies abnormal symptoms and warns of potential failures, allowing maintenance to be scheduled in advance rather than waiting for the regular 2-year inspection cycle or actual breakdown.
Solution Approach 2:
The system implements feedback by continuously monitoring driving information, comparing it against critical levels, and providing real-time feedback on the health status of the driving unit. This closed-loop feedback mechanism enables dynamic adjustment of maintenance timing based on actual condition, improving both safety and maintenance efficiency.
2Measurement precision
If real-time monitoring of driving information is implemented, then breakdown prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The monitoring system is segmented into distinct functional modules: information collection module, critical level setting module, and abnormal symptom detection module. Each module performs a specific function, making the overall complex system manageable and maintainable while achieving high prediction accuracy through specialized processing at each stage.
Solution Approach 2:
Critical levels serve as intermediaries between the raw driving information and the abnormal symptom detection. Instead of directly comparing complex driving data patterns, the system uses pre-established critical levels as reference thresholds, simplifying the detection process while maintaining high accuracy in predicting breakdowns.
3Measurement precision
If driving information is collected and classified into multiple sections (unlocked, activated, constant-speed, stopped, locked), then abnormal symptom detection precision is improved, but information processing complexity increases
Solution Approach 1:
The driving information is segmented into five distinct operational sections (unlocked, activated, constant-speed, stopped, locked) based on the elevator's operational state. This segmentation allows the system to apply section-specific critical levels and detection criteria, improving detection precision while managing complexity through structured organization of the processing logic.
Solution Approach 2:
Each operational section has its own specific critical levels and detection criteria tailored to that state's characteristics. For example, current value thresholds and abnormal symptom definitions are optimized for each section, providing locally optimized detection precision rather than using a single generic detection approach for all operational states.
Data Source
AI summary
The present invention provides a method of predictively maintaining an elevator driving unit which distinguishes between a condition when moving an elevator upward and a condition when moving the elevator downward, collects driving information of the driving unit (information on a change over time in current values) in a normal state, collects driving information of the driving unit before the occurrence of breakdown, sets critical levels based on the collected information, detects in real time an abnormal symptom of the driving unit by comparing the driving information of the driving unit, which is collected in real time, with the set critical level, and then performs stable predictive maintenance of the elevator driving unit, thereby efficiently preventing a safety accident of the elevator caused by a breakdown of the elevator driving unit.