Abstract
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.
Highlights
Failures inevitably occur in rotating machinery owing to defects in materials, fatigue, and aging
Numerical experiments demonstrated how the specific constant varies with respect to γ, which is defined as the ratio of the number of random sampling points Mq to the number of sampling points Mp at a fixed interval (Eq (18))
It was found that the signal could be recovered within the aforementioned limit less than 25% error even in a real environment with noise
Summary
Failures inevitably occur in rotating machinery owing to defects in materials, fatigue, and aging. Zhang et al [8] showed that in the diagnosis of bearing faults from vibration signals, the amount of measurement data could be significantly reduced by applying sparse coding (a type of CS), in which the basis was obtained via dictionary learning. As described above, the data compression ratio can be increased by using dictionary learning, and the data storage cost can be reduced These methods require a high-speed logger because the data must be measured at high speed and randomly thinned out. We measured the sound pressure for a failed propeller and evaluated the signal compression ratio and diagnostic accuracy of fault diagnosis using the RSUSM and CS.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
