Neural network-supported vibration analysis of turbomachinery for early fault prediction
TL;DR
A new hybrid deep learning architecture, combining one-dimensional convolutional neural networks (1D-CNN) with bidirectional long short-term memory (BiLSTM) networks to the problem of automatic detection and early forecasting of mechanical faults based on raw vibration signals is suggested.
Abstract
Abstract. Timely fault diagnosis in turbomachinery plays an important role in avoiding disastrous failures, reducing the number of unexpected downtimes, and optimizing the maintenance cycle in industrial applications that use a lot of energy. The paper suggests a new hybrid deep learning architecture, combining one-dimensional convolutional neural networks (1D-CNN) with bidirectional long short-term memory (BiLSTM) networks to the problem of automatic detection and early forecasting of mechanical faults based on raw vibration signals. The combination of a multi-domain feature extraction strategy, which covers time-domain statistical indicators, spectral descriptors based on the fast fourier transform (FFT), and continuous wavelet transform (CWT) scalogram representations into a single 128-dimensional feature vector is used as the input of the model. The suggested CNN-BiLSTM design effectively extracts both local spectrotemporal features and long-range sequential dynamics of the rotating machinery vibration data. Experiments on the CWRU Bearing Dataset and a custom turbomachine testbed dataset show that the proposed model can achieve a mean classification accuracy of 97.6% with clean signal conditions and 95.1% at a signal-to-noise ratio of 10 dB, and outperforms standalone CNN, BiLSTM, SVM, ANN, and Random Forest baselines by up to 10.2 The findings determine the appropriateness of the proposed framework on edge-deployable and real-time condition monitoring systems.