Driving Device Preventive Maintenance via Energy Thresholds
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Solution Overview
Problem
Current preventive maintenance methods for driving devices are inadequate in preventing significant losses due to breakdowns, as they fail to efficiently detect abnormal symptoms in real-time, leading to unexpected downtime and substantial economic losses.
Innovation Solution
A preventive maintenance method that collects and analyzes operation information during normal and pre-breakdown states to set fault and warning thresholds for peak and mean periods, allowing for real-time detection of abnormal energy changes, such as current, vibration, and noise, to prompt timely repairs and replacements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time monitoring and analysis of operation information is implemented to detect abnormal symptoms early, then reliability of the driving device is improved, but device complexity and loss of energy increase due to continuous data collection and processing
Solution Approach 1:
The monitoring system is divided into multiple independent modules: data collection module that gathers operation information, analysis module that processes the collected data, and warning module that generates alerts. This segmentation allows each module to perform its specific function efficiently while reducing the complexity of the overall system.
Solution Approach 2:
The system automatically monitors its own operation parameters and generates warnings without external intervention. The driving device itself provides the operation information needed for monitoring, and the system self-manages the detection and warning processes, reducing the need for external complex monitoring infrastructure.
2Productivity
If continuous real-time monitoring of operation information is performed to enable early detection of abnormalities, then productivity is improved through reduced downtime, but loss of energy increases due to continuous data collection and processing
Solution Approach 1:
Instead of truly continuous monitoring, the system performs periodic sampling of operation information at predetermined intervals. This periodic action reduces energy consumption compared to continuous monitoring while still enabling timely detection of abnormal symptoms that would affect productivity.
Solution Approach 2:
The system uses feedback mechanisms where warning information is generated based on analyzed operation data and fed back to operators or maintenance systems. This feedback loop enables proactive maintenance scheduling that improves productivity by preventing breakdowns while optimizing energy use by only activating full monitoring when anomalies are detected.
Data Source
AI summary
The present invention includes: collecting information about a change in energy magnitude according to time measured with the driving device in normal operation separately for each of a peak period and a mean period; collecting information about a change in energy magnitude according to time measured with the driving device in operation before the driving device breaks separately for each of the peak period and the mean period; setting a mean fault of the mean period on the basis of the information collected; and collecting information about a change in energy magnitude according to time measured in real time with the driving device in operation separately for each of the peak period and the mean period and of detecting the driving device in an abnormal state when the collected energy values in the mean period exceed the peak fault of the mean period set in the setting.