Fault identification and remaining useful life prediction of bearings using Poincare maps, fast Fourier transform and convolutional neural networks

Bearings are integral components of rotating machinery and their failure tends to be a catastrophic the machine. Poincare Maps used detect bearing failures using concept non-linear dynamics. Each time-domain vibration signature array has its own Map over period time. Fast Fourier Transform (FFT) is method analysing frequency plots signature. Convolutional Neural Networks (CNN) process Continuous Wavelet images provide Remaining Useful Life (RUL) bearing. The FFT diagnose type location fault in bearing, whereas CNN helps fraction Life. study concludes that combination Maps, analysis constitutes robust precise monitoring conditions.