Smart multichannel mode extraction for enhanced bearing fault diagnosis

Variable speed is one of the common working conditions of mechanical equipment,which poses an important challenge to equipment fault diagnosis. The current solutions have the shortcomings of low computational efficiency and large diagnostic errors. The ability of attention mechanism to automatically extract useful features has begun to attract widespread attention in the field of mechanical intelligent fault diagnosis. Combining the advantages of attention mechanism and unsupervised learning, this paper proposes a squeeze-excitation attention guided sparse filtering (SESF) method for mechanical intelligent fault diagnosis method under variable speed. Firstly, the SE attention mechanism is embedded in SF algorithm to guide model training. Then, unsupervised feature extraction is carried out on the variable speed signal samples. The training results are adaptively screened and weighted to make the model pay more attention to the region with the most classify discrimination, so as to improve the feature extraction ability of the model to obtain useful information. Finally, two sets of gear and bearing tests under variable speed condition are adopted to testify the performance of the proposed method. The experimental results show that the SESF method can overcome the influence of variable speed to achieve accurate recognition of different mechanical faults and is superior to the other methods.

Section: Introductionmentioning

confidence: 99%

Attention mechanism guided sparse filtering for mechanical intelligent fault diagnosis under variable speed condition

Han,

Wang,

Wan

et al. 2024

“…With the rapid development of the industrial field, data-driven mechanical fault diagnosis research has received more and more attention [1][2][3]. Traditional data-driven fault diagnosis models based on mechanical equipment usually assume the undersampling [15].…”

Section: Introductionmentioning

confidence: 99%

A fault diagnosis method based on hybrid sampling algorithm with energy entropy under unbalanced conditions

Zhao

Liu

Chen

et al. 2023

For the degraded performance of the fault diagnosis model caused by massive normal samples and scarce fault samples under unbalanced conditions, a new fault diagnosis method based on a hybrid sampling algorithm and energy entropy, namely HSEEFD is proposed in this paper. In the proposed method, Empirical Modal Decomposition (EMD) is employed to decompose the vibration signals into Intrinsic Mode Functions (IMFs), and the energy entropy feature of each IMF component is extracted to construct a feature vector matrix. Then, a new hybrid sampling algorithm using Tomek’s Links algorithm, Euclidean distance, K-means algorithm, and synthetic minority over-sampling technique (SMOTE), namely TSHSA is designed to balance the extracted features. Tomek’s Links algorithm is used to identify and remove the confusable majority class samples at the boundary. Euclidean distance is applied to find the suspected noise points in minority class samples and remove them. The k-means algorithm is employed to cluster the minority class samples and SMOTE is used to deal with each cluster according to the density of the clusters to synthesize new features. Finally, the support vector machine (SVM) is applied to classify faults and realize fault diagnosis. The experiment results on the actual imbalanced data show that the proposed HSEEFD method can effectively improve the accuracy of the fault diagnosis under unbalanced conditions by increasing the accuracy value by more than 2.1%, and the AUC and G-mean by more than 0.7%, 2.1%, respectively.

“…Therefore, we can learn from the research methods of bearing fault diagnosis to extract fault features. For rolling bearing fault feature extraction, many experts have proposed effective methods, such as empirical mode decomposition (EMD) [6], variational mode decomposition (VMD) [7], ensemble EMD (EEMD) [8], and wavelet transform (WT) [9], singular value decomposition (SVD) [10] and so on [11,12]. EMD is the earliest proposed adaptive mode decomposition method.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis method of weak vibration signal based on improved VMD and MCKD

Ke,

Liu,

Shi

et al. 2023

During the manufacturing process of electronic equipment, objects such as tin beads and glue blocks may be left in the electronic equipment, causing failure of the electronic equipment. This paper uses experimental equipment to collect weak vibration signals on the surface of electronic equipment. In view of the nonlinear and non-stationary characteristics of the vibration signal and its easy to be masked by strong background noise, a fault diagnosis method of weak vibration signal based on improved variational mode decomposition (VMD) and maximum correlation kurtosis deconvolution (MCKD) is proposed. Cosine factors and adaptive weights are introduced to improve the convergence accuracy of the Whale Optimization Algorithm (WOA). The envelope spectrum peak factor is used as the adaptability function of the improved whale algorithm (IWOA) to optimize the parameters of VMD and MCKD. Firstly, based on the decomposition results of weak fault signals by IWOA-VMD, the optimal modal components are selected. Secondly, the fault impact component in the optimal modal component is enhanced based on the IWOA-MCKD algorithm. Finally, the fault characteristic frequency is extracted through the envelope spectrum. The feasibility and superiority of the proposed optimization method are verified through simulation signal analysis and actual case study.