AI is reshaping how vibration diagnosis is done. End-to-end recognition built on raw vibration time series and one-dimensional convolutional neural networks (1D-CNN) now reaches deployable accuracy for early bearing and gearbox faults (inner race, outer race, pitting), while multimodal predictive maintenance fusing vibration with temperature has become a patent hotspot in the past two years. On the demand side, an SAS survey cited by Grand View Research finds about 71% of manufacturers already running predictive maintenance on AIoT platforms.
Yet algorithms can only be as good as the data. Sufficient sampling rate, avoided mounting resonance and on-edge feature extraction all directly shape model performance. Field wisdom: before chasing fancier models, build a high-quality data pipeline first.
That is the design logic of Corecloud DAQ products: 24-bit ADC high-fidelity acquisition with nine features (RMS, peak, kurtosis, peak-to-peak and more) computed at the edge, providing clean, stable and reproducible inputs for cloud AI models.
Sources: public academic and patent literature; SAS survey as cited by Grand View Research.