Fault detection via sparsity-based blind filtering on experimental vibration signals

Detection of bearing faults is a challenging task since the impulsive pattern of bearing faults often fades into the noise. Moreover, tracking the health conditions of rotating machinery generally requires the characteristic frequencies of the components of interest, which can be a cumbersome constraint for large industrial applications because of the extensive number of machine components. One recent method proposed in literature addresses these difficulties by aiming to increase the sparsity of the envelope spectrum of the vibration signal via blind filtering (Peeters. et al., 2020). As the name indicates, this method requires no prior knowledge about the machine. Sparsity measures like Hoyer index, l1/l2 norm, and spectral negentropy are optimized in the blind filtering approach using Generalized Rayleigh quotient iteration. Even though the proposed method has demonstrated a promising performance, it has only been applied to vibration signals of an academic experimental test rig. This paper focuses on the real-world performance of the sparsity-based blind filtering approach on a complex industrial machine. One of the challenges is to ensure the numerical stability and the convergence of the Generalized Rayleigh quotient optimization. Enhancements are thus made by identifying a quasi-optimal filter parameter range within which blind filtering tackles these issues. Finally, filtering is applied to certain frequency ranges in order to prevent the blind filtering optimization from getting skewed by dominant deterministic healthy signal content. The outcome proves that sparsity-based blind filters are effective in tracking bearing faults on real-world rotating machinery without any prior knowledge of characteristic frequencies.

Bearing Fault Diagnosis Using Refined Composite Generalized Multiscale Dispersion Entropy-Based Skewness and Variance and Multiclass FCM-ANFIS

Bearing vibration signals typically have nonlinear components due to their interaction and coupling effects, friction, damping, and nonlinear stiffness. Bearing faults affect the signal complexity at various scales. Hence, measuring signal complexity at different scales is helpful to diagnosis of bearing faults. Numerous studies have investigated multiscale algorithms; nevertheless, multiscale algorithms using the first moment lose important complexity data. Accordingly, generalized multiscale algorithms have been recently introduced. The present research examined the use of refined composite generalized multiscale dispersion entropy (RCGMDispEn) based on the second moment (variance) and third moment (skewness) along with refined composite multiscale dispersion entropy (RCMDispEn) in bearing fault diagnosis. Moreover, multiclass FCM-ANFIS, which is a combination of adaptive network-based fuzzy inference systems (ANFIS), was developed to improve the efficiency of rotating machinery fault classification. According to the results, it is recommended that generalized multiscale algorithms based on variance and skewness be examined for diagnosis, along with multiscale algorithms, and be used to achieve an improvement in the results. The simultaneous usage of the multiscale algorithm and generalized multiscale algorithms improved the results in all three real datasets used in this study.

Weighted Reconstruction and Improved Eigenclass Combination Method for the Detection of Bearing Faults

Aiming at the difficulty of extracting and classifying early bearing faults, a fault diagnosis method based on weighted average time-varying filtering empirical mode decomposition and improved eigenclass is proposed in this paper. Firstly, the bearing fault signal is decomposed into a series of intrinsic mode functions by the signal decomposition method, and the amplitude of the component is modulated by the weighted average method to enhance the fault impulse component. Then, the fractional Fourier transform is used to filter the reconstructed signal. Regarding classification issues, the eigenclass classifier is optimized by the IDE method that can be used for feature dimensionality reduction. Finally, the optimal features are selected and input into the IDE-EigenClass model. The experimental results show that the bearing fault diagnosis method proposed in this paper has higher accuracy and stability than the traditional PNN, SVM, BP, and other methods.

Performance analysis of pre-trained transfer learning models for the classification of the rolling bearing faults

Nowadays, artificial intelligence techniques are getting popular in modern industry to diagnose the rolling bearing faults (RBFs). The RBFs occur in rotating machinery and these are common in every manufacturing industry. The diagnosis of the RBFs is highly needed to reduce the financial and production losses. Therefore, various artificial intelligence techniques such as machine and deep learning have been developed to diagnose the RBFs in the rotating machines. But, the performance of these techniques has suffered due the size of the dataset. Because, Machine learning and deep learning methods based methods are suitable for the small and large datasets respectively. Deep learning methods have also been limited to large training time. In this paper, performance of the different pre-trained models for the RBFs classification has been analysed. CWRU Dataset has been used for the performance comparison.