Roll a pinner4/16/2023 ![]() ![]() Gs sr rate load BPFO BPFI FTF BSF Label FileName Starting parallel pool (parpool) using the 'local' profile. After pinpointing the frequency band with the highest kurtosis, a bandpass filter can be applied to the raw signal to obtain a more impulsive signal for envelope spectrum analysis. They are powerful tools to locate the frequency band that has the highest kurtosis (or the highest signal-to-noise ratio). Kurtogram and spectral kurtosis compute kurtosis locally within frequency bands. Kurtogram and Spectral Kurtosis for Band Selection ![]() The next section will introduce kurtogram and spectral kurtosis to extract the signal with highest kurtosis, and perform envelope spectrum analysis on the filtered signal. Extracting the impulsive signal with amplitude modulation at BPFO (or enhancing the signal-to-noise ratio) is a key preprocessing step before envelope spectrum analysis. The normal signal does not show any amplitude modulation. For an outer race fault signal, the amplitude modulation at BPFO is slightly noticeable, but it is masked by strong noise. It is shown that inner race fault signal has significantly larger impulsiveness, making envelope spectrum analysis capture the fault signature at BPFI effectively. Here are the formulae for those critical frequencies. In this example, only the data collected from the test rig with known conditions are used.Įach data set contains an acceleration signal "gs", sampling rate "sr", shaft speed "rate", load weight "load", and four critical frequencies representing different fault locations: ballpass frequency outer race (BPFO), ballpass frequency inner race (BPFI), fundamental train frequency (FTF), and ball spin frequency (BSF). The remaining 3 data sets are from real-world machines: an oil pump bearing, an intermediate speed bearing, and a planet bearing. The first 20 data sets are collected from a bearing test rig, with 3 under good conditions, 3 with outer race faults under constant load, 7 with outer race faults under various loads, and 7 with inner race faults under various loads. MFPT Challenge data contains 23 data sets collected from machines under various fault conditions. Machinery Failure Prevention Technology (MFPT) Challenge Data ![]()
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