Fault Diagnosis of a Propeller Using Sub-Nyquist Sampling and Compressed Sensing

The fault diagnosis of rotating machinery is generally performed using methods that employ vibration and sound. These methods are simple and accurate. However, all of these methods measure vibration data on the basis of the sampling theorem. Thus, they require a high measurement frequency, resulting...

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Bibliographic Details
Published inIEEE access Vol. 10; pp. 16969 - 16976
Main Author Kato, Yuki
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Compressed sensing
Data collection
data compression
Fault diagnosis
Fourier transforms
Frequency measurement
Measurement methods
Monitoring
Noise measurement
Pressure measurement
Propellers
Random sampling
Rotating machinery
Rotating machines
Sampling methods
signal processing
Sound pressure
Theorems
Vibration measurement
Vibrations
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Summary:The fault diagnosis of rotating machinery is generally performed using methods that employ vibration and sound. These methods are simple and accurate. However, all of these methods measure vibration data on the basis of the sampling theorem. Thus, they require a high measurement frequency, resulting in a large data volume and expensive measurement equipment. In recent years, a method that uses compressed sensing has been proposed to solve this problem, but it requires dedicated hardware to realize random sampling. To overcome this drawback, we developed a random start uniform sampling method (RSUSM) and combined it with compressed sensing (CS). RSUSM is a method of measuring data at a fixed frequency with a random start time. Numerical experiments demonstrate how the specific constant changes for each RSUSM parameter. This allows us to know the limit of how many measurement points are required for the number of non-zero components. We also applied CS by RSUSM to the sound pressure measurement results of the failed propeller, and found that the signal could be recovered less than 25% error even in a noisy real environment within the aforementioned limit. In this case, we found that the measurement frequency could be compressed to 1/80th of the frequency required by the sampling theorem, and the measurement data size to 1%. This approach is expected to diagnose faults in more rotating machines by significantly reducing the costs associated with data collection and storage.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3149756