Driver Predictive Maintenance Using Peak Slope Abnormality Detection
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Solution Overview
Problem
Current predictive maintenance methods for drivers (motors, pumps, etc.) are inadequate in preventing huge losses due to failures, as they fail to detect abnormal symptoms promptly and reliably, leading to significant downtime and operational costs.
Innovation Solution
A precise predictive maintenance method that measures and collects peak and constant speed values during normal and failure states, sets alarm limits and slope values, and detects abnormal conditions in real-time by comparing these values with pre-set limits, thereby alerting for timely maintenance and replacement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current predictive maintenance methods are used for drivers, then some level of maintenance can be performed, but abnormal symptoms cannot be detected promptly and reliably, leading to huge losses and significant downtime
Solution Approach 1:
The system performs preliminary actions by collecting driving information and establishing baseline data during normal operation phases (acceleration, constant speed, deceleration). Alarm values are pre-calculated based on this baseline data before actual failures occur, enabling the system to detect abnormalities promptly when deviations from these pre-established baselines are detected
Solution Approach 2:
The system implements continuous feedback by monitoring driving information in real-time, comparing current values against pre-calculated alarm values, and generating alerts when abnormalities are detected. This closed-loop feedback mechanism ensures reliable and prompt detection of driver abnormalities, preventing huge losses and reducing downtime
2Measurement precision
If comprehensive monitoring of driving information is performed to detect abnormal symptoms, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The monitoring system is segmented into distinct functional modules: an information acquisition unit that collects driving data, a calculation unit that computes alarm values based on segmented driving phases (acceleration, constant speed, deceleration), and a comparison unit that detects abnormalities. This modular segmentation maintains detection accuracy while managing system complexity through clear functional separation
Solution Approach 2:
The system focuses monitoring on critical parameters specific to driver operation (acceleration patterns, constant speed maintenance, deceleration characteristics) rather than all possible parameters. By changing the approach to monitor only these key parameters with pre-calculated alarm thresholds, the system achieves high detection accuracy without excessive complexity
Data Source
AI summary
The present invention relates to a precise predictive maintenance method of a driver and the configuration includes: collecting slope information for a peak value between drive periods by connecting the peak value in a respective drive period in a driving state of the driver before a failure of the driver occurs; setting an alarm slope value for the peak value between the drive periods based on the collected slope information; and detecting, in a case where an average slope value for the peak value between the drive periods measured at a unit time interval set in a real-time driving state of the driver is more than the alarm slope value, the case as an abnormal state of the driver.