A Study on Low-Speed Sampling for Vibration Analysis in Predictive Maintenance

Abstract

This paper proposes a configuration to detect slight frequency changes in high-frequency bands by low-speed sampling, utilizing the accumulation for real-time serial-to-parallel converter (ARS). Recently, there has been significant interest in predictive maintenance through vibration monitoring. To obtain accurate waveforms in vibration measurement, we must set the sampling frequency to meet the sampling theorem. However, this requirement results in greater costs for memory and data traffic as the data volume increases. This is an obstacle to achieving low-cost and low-power consumption. A possible solution to this problem is to measure at a lower sampling frequency, although measurements with low-speed sampling cause aliasing. Aliasing is known to cause high-frequency signals to appear in low-frequency bands, and it is possible to calculate the frequency of the original signal by analyzing the signal appearing in low-frequency bands. However, the fast Fourier transform (FFT), which has conventionally been used as a typical method, suffers from the low-frequency resolution in low-frequency bands, so it is not suitable for analyzing aliasing signals. As a solution to this problem, we apply the ARS which is characterized by its high resolution in low-frequency bands and low computational complexity, to the aliasing signals. To verify the performance of the proposed method, we conducted a computer simulation. In this simulation, the pseudo-signal of a bearing with a damaged outer race was analyzed to estimate the ball pass frequency of the outer race (BPFO). The results show that ARS is capable of estimating BPFO, while FFT could not estimate BPFO appearing at low-frequency bands. Based on these results, the configuration of the proposed method is expected to be useful for the construction of low-cost and low-power predictive maintenance systems.