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machinery fault diagnosis is an attractive but challenging task, especially for low-speed conditions. Therefore, a new discriminative approach that introduces robust principle component analysis (RPCA) and multi-kernel to deep neural networks is proposed to perform intelligent fault diagnosis. Firstly, RPCA is applied to extract fault signals from extreme background noise based on its sensitivity to grossly corrupted data. Secondly, two cascaded MKPCA stages with additional robustness to distortions in feature extraction are used to enhance the energy of spectrum symptom and overcome the tricky issues of low-speed machinery. Especially, the multi-kernel is introduced into the basic PCA filters to learn the data-adapting convolution filter and gain additional robustness to nonlinearity in the signal. Finally, the proposed method is demonstrated on signals from laboratory tests (with a slightly damaged defect in a bearing) and structural fault data, outperforming those of traditional machine learning and classical deep learning methods. Moreover, hidden information of the network is visualized to analyse the reasons for its high performance.

To realize an automatic diagnosis of rotating machinery structure faults, this paper presents a novel fault diagnosis model based on adaptive multiband filter and stacked autoencoders (SAEs). First, to solve the problem where the actual rotating frequency and its harmonics cannot be accurately extracted in engineering applications, an improved adaptive multiband filtering method is designed. This method takes the theoretical rotating frequency as the search center, extracts the maximum within the positive and negative deviation as the actual rotating frequency, and sets a threshold according to the actual value to realize multiband filtering. This method can effectively remove background noise and accurately extract the actual rotating frequency and its harmonics. Second, an unsupervised SAE multiclassification model is established to realize an automatic diagnosis of fault types. This model can automatically extract the in-depth features of the filtered signal and improve the fault classification accuracy. Third, engineering and comparative experiments were carried out to verify the effectiveness and superiority of this model. Results show that the proposed automatic diagnosis model can extract the characteristic components abundantly and accurately recognize rotating machinery structural faults.

To obtain a high-precision bearing fault diagnosis of different speeds under intense background noise, a novel method called state-space principal component tracking filtering (SPCTF) is proposed for fault diagnosis. SPCTF can recover the fault information through the sequence of “Prediction – Measurement – Correction - Optimal estimation - Principal component extraction” from the polluted raw signals with noise. Firstly, the switch Kalman filter (SKF) is utilized to establish a dynamic filter model for time-series signals. And then, a feature tracking matrix is generated from the principal component features extracted from the optimal estimated state matrix of each time in the model. Thirdly, the principal component analysis extracts the effective fault feature from redundant information generated in the feature tracking matrix. Finally, the fault characteristic frequency is analyzed with the envelope spectrum to achieve a high-precision fault bearing diagnosis. The proposed method is demonstrated on signals from simulation signals and laboratory tests (several speeds), outperforming traditional methods.

Since the raw signal collected from the sliding bearing is contaminated with background noise, and it is difficult to obtain high-precision results for the traditional methods due to the low signal-to-noise ratio (SNR). Therefore, a stepwise intelligent diagnosis method based on statistical filter and stacked auto-encoder (SAE) that is established with several auto-encoders is proposed to identify several faults of sliding bearing in a rotor system. Firstly, the statistical filter is utilized to reduce the interference information for the different abnormal states and to increase the SNR. Secondly, the stepwise intelligent diagnosis based on SAE is performed to learn the useful fault features, and it can automatically complete the fault diagnosis which is contributed with the superiority binary classification to fully mine the relationship between the fault characteristics and the health condition of bearing. Finally, the diagnosis of the oil whirl and structural faults in a rotor system is cited as an example to demonstrate the effectiveness of proposed method. It can effectively illustrate the advantages of the stepwise diagnosis method to obtain the maximum diagnostic accuracy.