Load Abnormality Detection Device for Induction Motors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional facility abnormality diagnosis methods face difficulties in detecting sideband waves due to increased spectrum intensities near the power supply frequency when load torque variations occur in induction motors, making it hard to identify abnormalities.
Innovation Solution
A load abnormality detection device that includes a current input unit, FFT analysis unit, peak detection calculation unit, averaging calculation unit, sideband wave extraction unit, and spectrum peak series determination unit to detect abnormalities based on a power spectrum series of sideband waves, ensuring peak detection and noise reduction through averaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional facility abnormality diagnosis method is used, then sideband wave detection is performed, but detection accuracy deteriorates when load torque varies due to increased spectrum intensities near power supply frequency
Solution Approach 1:
The patent applies preliminary action by performing multiple FFT analyses on current waveforms captured during stable operating periods before the actual abnormality detection. By pre-processing the current waveform data and storing the processed results, the system prepares clean reference data that can be reliably compared during abnormality detection, even when load torque varies during operation.
Solution Approach 2:
The patent uses an intermediary approach by introducing a comparison mechanism between the processed current waveform data and reference values. The system compares the processed current waveform with reference current waveform data to detect abnormalities, using this intermediary comparison step to filter out noise and variations caused by load torque changes, thereby improving detection accuracy.
2Quantity of substance
If spectrum analysis is performed on varying load conditions, then more operational data is captured, but noise interference increases making peak detection difficult
Solution Approach 1:
The system performs preliminary FFT analysis on current waveforms during stable operation periods before abnormality detection. By pre-processing the data and storing the results, it accumulates clean operational data that can be reliably used for comparison, increasing the quantity of useful operational data while maintaining signal quality.
Solution Approach 2:
The patent extracts only the relevant stable operation periods from the overall operational data for analysis. By selecting and processing only the current waveforms captured during stable operation, it separates the useful operational data from the noisy varying load conditions, thereby increasing the quantity of reliable data while reducing noise interference.
3Object-affected harmful factors
If multiple power spectrums are averaged, then signal intensity of noise peaks is reduced, but sideband wave extraction becomes more critical
Solution Approach 1:
The system performs preliminary FFT analysis and averaging during stable operation periods before abnormality detection. By pre-computing the average of multiple power spectrums during normal operation, it reduces noise signal intensity in advance, making the subsequent sideband wave extraction and abnormality detection more reliable even when load torque varies.
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
To provide, even in the case of an electric motor of which load torque varies, a load abnormality detection device that can detect an abnormality in a load driven by the electric motor on the basis of a spectrum peak series of a sideband wave that occurs in a peak shape at both sides in the vicinity of a power supply frequency. In the load abnormality detection device, the current of an electric motor 5 is detected by a current detector 4 and inputted from a current input unit 10. Then, in a logic calculation unit 11, a plurality of power spectrum analysis results each obtained by frequency analysis being performed on the current waveform when the current is stable are subjected to an averaging process. Then, a sideband wave is detected from a power spectrum analysis result obtained through the averaging process, and the presence/absence of an abnormality in a load is determined on the basis of a spectrum peak series. When occurrence of an abnormality is determined, an alarm output unit 21 outputs an alarm.