Fault diagnosis of planetary gears based on intrinsic feature extraction and deep transfer learning

Self Cite

Rolling bearings play a crucial role as components in mechanical equipment. Malfunctioning rolling bearings can disrupt the normal operation of the equipment and pose safety hazards. Traditional deep learning (DL)-based methods for diagnosing faults in rolling bearings present several issues, such as insufficient feature information of fault samples, high model complexity and low accuracy. To overcome these challenges, this paper introduces an intelligent approach for rolling bearing fault diagnosis using intrinsic feature extraction (IFE) and convolutional block attention module (CBAM)-enhanced InceptionNet. In our researches, variational mode decomposition (VMD) is adopted to decompose the original signal into multiple band-limited intrinsic mode functions (BLIMFs). In the decomposition process of VMD, the number of decomposition layers k is determined by center frequency method and the optimal BLIMF is chosen based on minimum envelope entropy. Subsequently, the continuous wavelet transform (CWT) is employed to transform the optimal BLIMF into time-frequency images. Finally, the obtained time-frequency images are fed into the proposed CBAM-enhanced InceptionNet for fault state diagnosis. Experiments on two different datasets prove that the method has stable and reliable accuracy. Comparative experiments have demonstrated that this method can reduce network model parameters and improve diagnosis efficiency while achieving high accuracy.

Section: Introductionmentioning

confidence: 99%

A novel fault diagnosis approach of rolling bearing using intrinsic feature extraction and CBAM-enhanced InceptionNet

Yuan

et al. 2023

Self Cite

“…The vibration signal has high accuracy, it is the most accessible signal in research and applications. The traditional vibration signal-based fault diagnosis methods are mainly based on machine learning, which depends on the prior fault knowledge and requires manual extraction of features before classification, the accuracy is related to the effectiveness of feature extraction [12,13]. Zhang et al proposed a multivariate dynamic mode decomposition (DMD) algorithm, a tensor low tubal rank component extraction framework was constructed to enable simultaneous extraction of bearing fault features from the multivariate signal, improved the fault diagnostic accuracy effectively [14].…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of bearing-rotor system based on infrared thermography: ReSPP with multi-scaled training method

An,

Liu,

Shao

et al. 2023

The fault diagnosis method of bearing-rotor system based on infrared thermography can reflect the global fault information of the equipment, which is an advanced non-contact monitoring measure. Current diagnosis methods focus on the analysis of single-scale input images, and the network only accepts fixed-size inputs. The model is not capable of fusing multi-scaled fault features while processing single-scaled images, and compression for the size constraint will cause geometric distortion. This paper proposed a novel fault diagnosis model ReSPP (Residual Network with Least Spatial Pyramid Pooling (LSPP)) based on the improved LSPP. LSPP solves the problem of fault feature distortion caused by fixed-size constraint of the network. By replacing the single-scaled training with the proposed multi-scaled training method applied to fault diagnosis, the weight parameters of ReSPP pool the deep fault features of the bearing-rotor system at multiple scales, retaining the critical fault semantic information. The proposed model solves the fault feature loss in the process of feature extraction by improving the subsampling residual block. Experimental results show that ReSPP with multi-scaled training method (ReSPP-MSTM) classifies the fault conditions of the bearing-rotor system with an average diagnostic accuracy of 99.18%.

“…Qian et al [17] proposed a novel transfer learning network framework combining convolution and an autoencoder to address the problem of insufficient labeled data in the target domain and combined CORAL loss and adversarial mechanism simulation to enhance the fault accuracy and anti-interference ability of the proposed network. The above research reduces the domain discrepancy between the source domain and target domain through parameter transfer, domain adaptation, or CORAL loss and adversarial mechanism simulation, aiming to maintain higher robustness in more complex environments and improve the accuracy of fault diagnosis after transfer [18][19][20]. However, the actual operation of large-scale mechanical equipment does not provide access to a large amount of fault data, making transfer learning fault diagnosis a significant challenge that requires a method to obtain more fault data [21,22].…”

Section: Introductionmentioning

confidence: 99%

Transfer learning-based multiple digital twin-assisted intelligent mechanical fault diagnosis

Liu,

Qi,

Gao

et al. 2023

With the advancement of complex system diagnosis, prediction, and health management technologies, digital twin technology has become a prominent research area in the fields of intelligent manufacturing and system operation and maintenance. However, due to the high complexity of practical systems, the difficulty of data acquisition, and the low accuracy of modeling techniques, current digital twin modeling suffers from low accuracy, and the generalization ability of models is poor when applied in model transfer. To address this issue, a novel fault diagnosis method is proposed, which integrates a digital twin model based on transfer learning. The framework introduces an innovative approach to construct multiple digital twin models using both mechanistic and data-driven models. The mechanism twin constructs a universal simulation model based on physical equipment and updates it with system response measurement data. The data twin consists of a high-dimensional fully connected GAN (HDFC-GAN) twin for extracting deep features from data and an LSTM twin for extracting time series features. Subsequently, transfer learning is introduced to achieve deep fusion in the multiple digital twins system. The mechanism twin is used to obtain source domain samples to construct a diagnostic network, and the data twin is used to extract target domain features to correct the diagnostic network, thereby improving the accuracy and reliability of fault diagnosis. Finally, the proposed framework is applied to the fault diagnosis of triplex pump equipment. The accuracy of diagnosis continuously improves as the system is updated and ultimately reaches 89.28%, demonstrating the effectiveness of the algorithm and providing a novel solution for the generalization limitations of current digital twin models.