Driving Unit Predictive Maintenance Using Peak Interval Gradients
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Solution Overview
Problem
Current predictive maintenance methods for driving units are inadequate in detecting abnormal conditions promptly, leading to significant downtime and economic losses due to malfunction, as they lack precision in identifying peak intervals and gradient values that indicate potential issues.
Innovation Solution
A precise predictive maintenance method that collects and analyzes peak intervals and gradient values from driving information in both normal and malfunction states to set alarm limits, detecting abnormal conditions in real-time and issuing alerts for timely repair or replacement, incorporating energy size changes, vibration, noise, frequency, temperature, humidity, and pressure data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional predictive maintenance methods are used, then maintenance can be performed, but abnormal conditions cannot be detected promptly leading to significant downtime
Solution Approach 1:
The patent replaces traditional mechanical monitoring systems with signal processing-based detection. By converting physical vibrations and acoustic emissions into electrical signals and analyzing them through spectral analysis, the system achieves prompt detection of abnormal conditions, thereby reducing downtime while maintaining reliability.
Solution Approach 2:
The patent introduces intermediate parameters (peak interval, gradient value) as mediators between the physical state of the driving unit and the detection system. These intermediaries translate complex vibration patterns into quantifiable metrics that can be promptly analyzed, enabling early detection of abnormalities without significant downtime.
2Productivity
If traditional maintenance methods are used, then operations continue, but huge economic losses occur due to malfunction
Solution Approach 1:
The patent performs preliminary detection and analysis of vibration signals to identify early signs of malfunction. By detecting abnormal peak intervals and gradient values before complete failure occurs, the system enables proactive maintenance scheduling that maintains operational continuity while preventing the huge economic losses associated with unexpected breakdowns.
3Measurement precision
If simple detection methods are used, then the system is easy to operate, but precision in identifying peak intervals and gradient values is insufficient
Solution Approach 1:
The patent segments the complex vibration signal analysis into distinct processing stages: signal acquisition, spectral analysis, peak interval extraction, and gradient value calculation. This segmentation enables precise measurement of peak intervals and gradient values while managing system complexity through modular processing steps that can be implemented systematically.
Data Source
AI summary
The present invention relates to a precise predictive maintenance method for a driving unit and a configuration thereof includes a first base information collecting step S10 of collecting change information of an energy size, a second base information collecting step S20 of collecting a peak interval, a setting step S30 of setting an alarm gradient value for the peak interval, and a detecting step S40 of detecting the driving unit as an abnormal state.