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AI and Signal Processing Unite to Diagnose Machine Faults Faster
- Researchers have combined artificial intelligence (AI) with signal processing to diagnose faults in machines faster and more effectively. The classifier-guided blind deconvolution (ClassBD) approach co-optimizes blind deconvolution-based signal feature extraction and deep learning-based fault classification. The system uses two filters: one for time-domain quadratic convolutional filters (QCNN) to extract periodic impulses and another in the frequency domain. The ClassBD framework aims to seamlessly integrate BD with deep learning classifiers via co-optimisation of model parameters. The method was tested on three datasets, and results show ClassBD outperforms other methods in noisy conditions, providing better interpretability.
- BD has been a successful approach used to extract bearing fault-specific features from vibration signals under strong background noise. However, one of the major challenges is integration with fault-diagnosing classifiers due to differing learning objectives. When combined, classifiers and BD share separate optimization spaces. Integration has the potential to cause BD issues such as enhancing the cyclic impulses of the fault signal while reducing differences between fault severities. The system developed aims to use classified information to instruct BD to extract features necessary to distinguish classes amid strong noise.
- The ClassBD system includes two neural network modules: one time-domain QCNN module and another of linear filters for signals in the frequency domain. ClassBD aims to integrate BD and deep learning classifiers. This is achieved by employing a deep learning classifier to teach BD filters. The fault labels provide useful information in guiding the BD to distinguish features. ClassBD is the first method to diagnose bearing faults under heavy noise while providing good interpretability.
- The quadratic neural filter enhances the filter's capacity to extract periodic impulses in the time domain. Meanwhile, the linear neural filter offers the ability to filter signals in the frequency domain and improves BD performance. The entire ClassBD system has plug-and-play capability and can be used as a module in the first layer of deep learning classifier, while physics-informed loss and uncertainty-aware weighing loss strategy are used to optimize both classifiers and BD filters.
- The research team conducted computational experiments on three datasets, two public and one private. The ClassBD system was shown to outperform other methods in noisy conditions on all datasets, providing more accurate results and better interpretability.
- In conclusion, combining AI and signal processing allows fault diagnosis in machinery faster and more effectively, providing better interpretability with high accuracy and efficiency. It is an essential step in ensuring the reliable operations of rotating machinery.
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