Abnormality diagnosis device and abnormality diagnosis method
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Solution Overview
Problem
Conventional abnormality diagnosis devices for electric motors in air conditioners face challenges in accurately diagnosing abnormalities when the load is constantly applied and fluctuates, as state quantities like current values also fluctuate, making it difficult to distinguish between normal and abnormal states.
Innovation Solution
An abnormality diagnosis device and method that utilize time-series and frequency-series data of state quantities to calculate feature quantities, determine operation modes, generate feature quantity distributions, and compare them to reference distributions to determine the presence of abnormalities, allowing for accurate diagnosis even under fluctuating load conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional abnormality diagnosis using current values is performed, then the diagnosis can be performed with simple measurement, but the diagnosis accuracy deteriorates when load fluctuates because current values fluctuate along with load changes
Solution Approach 1:
The patent transforms the diagnosis approach by changing from using raw current values to using feature quantities extracted from current waveforms. The waveform analysis unit extracts features such as RMS values, peak values, and harmonic components that remain stable even when load conditions change, thereby maintaining diagnosis accuracy across varying operating conditions.
Solution Approach 2:
The patent introduces feature quantities as an intermediary between the measured current values and the abnormality diagnosis. These feature quantities serve as a stable representation that filters out the effects of load fluctuations while preserving the information needed for accurate diagnosis of motor and compressor abnormalities.
2Measurement precision
If feature quantities are extracted from time-series data, then the diagnosis accuracy improves under fluctuating loads, but the data processing complexity increases
Solution Approach 1:
The patent applies partial action by selecting only the most relevant feature quantities from the time-series data for diagnosis purposes. Rather than analyzing all possible waveform characteristics, the system extracts a limited set of key features (such as RMS value, peak value, and specific harmonic components) that are sufficient for accurate abnormality detection, thereby balancing accuracy with processing efficiency.
Data Source
AI summary
An abnormality diagnosis device includes: a feature quantity calculation unit which calculates a plurality of feature quantities from the time-series data of the current values; an operation mode determination unit which determines an operation mode of a compressor on the basis of the load torque and the drive frequency; a feature quantity distribution generation unit which generates a feature quantity distribution from values of the plurality of feature quantities; a reference region generation unit which generates a reference region on the basis of the feature quantity distribution that is obtained in a normal case; and a determination unit which compares the feature quantity distribution that is obtained during an abnormality diagnosis and the reference region corresponding to the operation mode that is applied during the abnormality diagnosis, to determine whether an abnormality is present or absent in either of the compressor and the electric motor.