Power Transmission Monitoring via Zoned Motor Current Analysis
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Solution Overview
Problem
Existing abnormality detection techniques for power transmission mechanisms, such as those in injection molding machines, face challenges in precision and sensitivity, especially at the initial stages of deterioration, where subtle changes in current spectra can be misinterpreted as noise rather than actual abnormalities.
Innovation Solution
A management device and method that divides the power transmission process into zones, calculates average current values, and uses these values to estimate a state amount, enhancing the precision and probability of abnormality detection by distinguishing between normal and abnormal states through feature calculation and state amount estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current spectrum analysis is used for abnormality detection, then abnormality can be detected, but detection precision is low at initial stages of deterioration
Solution Approach 1:
The patent divides the current spectrum into multiple frequency bands and segments the analysis into different operational phases. By segmenting the current waveform into multiple cycles and analyzing each phase separately, the system can identify subtle abnormalities that would be masked in overall spectrum analysis, thereby improving detection precision at early deterioration stages.
Solution Approach 2:
The patent changes the analysis parameters by calculating effective values of current in different frequency bands and phases, rather than using traditional spectrum peak analysis. This parameter transformation allows detection of gradual changes in current characteristics that indicate early-stage abnormalities, improving both reliability and precision.
2Reliability
If traditional abnormality detection methods are used, then equipment can be monitored, but subtle changes are misinterpreted as noise
Solution Approach 1:
The patent applies local quality analysis by examining specific frequency bands and operational phases separately rather than analyzing the entire current spectrum uniformly. By focusing on local characteristics of current waveforms in different phases, the system can distinguish genuine abnormality signals from background noise, improving signal discrimination accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where detected abnormalities trigger more detailed analysis and comparison with reference values. This feedback loop allows the system to learn from previous measurements and improve its ability to distinguish between noise and genuine abnormality signals over time.
Data Source
AI summary
A management device that can sense abnormality (deterioration) detection on a power transmission mechanism with higher precision and probability is implemented. A management device 30 for a power transmission mechanism that transmits a driving force from an electric motor 13 to a load-side device 12 includes a current acquiring section that acquires a current value of the electric motor 13 per unit process in which the power transmission mechanism is driven, a feature calculating section 121 that divides the unit process into multiple zones and calculates an average current value obtained by averaging the current value of each of the zones, and a diagnosis section 122 that executes abnormality detection. The diagnosis section 122 calculates a state amount estimation value on the basis of the average current value of the multiple zones, and executes abnormality sensing in the unit process on the basis of the state amount estimation value.