To detect the eccentric fault of permanent magnet synchronous linear motor (PMSLM) safely, efficiently, and accurately, this article proposes a diagnosis method based on fusion feature extraction and a new hybrid network model. First, according to motor topology structure characteristics, a tunneling magnetoresistance (TMR) sensor-integrated design scheme is put forward to measure the external stray magnetic field density signal of PMSLM, which can realize noninvasive fault diagnosis. Second, 2-D image coding methods, namely, Gramian Angular Field (GAF) and Markov Transition Field (MTF) are introduced to convert 1-D fault signal into 2-D images, which can realize fault feature enhanced display. Visual saliency map and weighted least-square (VSMWLS) image fusion technology are applied to fuse GAF and MTF images, combining the advantages of both methods. It can also enhance the complementary information of fault features. Then, a hybrid multifeature fusion classification framework called ConvNeXt–ResNet–CNN–GCNet (CRCG) is proposed to integrate different deep feature extraction capabilities of the three models. In addition, GRCG incorporates the GCNet attention mechanism to capture multichannels' dependencies to realize quantitative and refined diagnosis of eccentricity faults. Finally, the PMSLM prototype experimental platform is established. The accuracy and F1 score are up to 97.4% and 97.9%, respectively. All these results are higher than other popular methods that can certify the superiority and effectiveness of this method.
This work proposes a new diagnostic method for high-resistance contact (HRC) and bearing faults in a switched reluctance motor (SRM) on the basis of multisource signal visual fusion and an intelligent classifier. First, an electric vehicle experimental platform with an SRM prototype is built, and the stator winding current and vibration signals are collected in a noninvasive manner. Second, an optimizing multisource signal symmetrized dot pattern (OMSSDP) method is proposed to characterize fault signals and realize 2D visualization enhancement under different fault states. This method can also process multisource signal and fuse them into one image. Third, a MobileViT-ECA model is built to extract the global and local fault features of SRM. The model can effectively distinguish the type and severity degree of compound faults in SRM. Last, comparison experiments with different time-series signal processing methods and intelligent classifiers are performed. Results prove that the proposed method can accurately identify the HRC fault phase location and bearing fault type, and its classification accuracy can reach 98.71%.
This study investigates a novel image morphology texture feature extraction method to realize the demagnetization fault location and severity detection of double-sided permanent magnet synchronous linear motor (DPMSLM). Initially, according to the constraints of DPMSLMs topology structure, the three lines magnetic density signal in motor air gap is extracted by finite element analysis as effective fault signal. Then, the grayscale fusion image (GFI) method is introduced to transform 1D data signal to 2D fused grayscale image which can better describe the demagnetization fault information. The unique features are visualized using the image enhancement techniques, and the image morphology texture features such as the area, Euler number, perimeter operator, correlation of binary image and so on can be extracted to constitute the demagnetization fault indexes. In addition, fisher score (FS) is used for feature optimization which can reduce the feature dimension. Furthermore, the two-level multiverse optimization support vector machine (MVO-SVM) algorithm is established to conduct demagnetization fault classification. Comparison experiments with other classifiers show that the MVO-SVM has a high fault identification accuracy of more than 98.3% and low running time less than 2.57s. Finally, the motor prototype experiment results show that the proposed method can accurately identify the location and severity of DPMSLM demagnetization faults, and it is an effective and feasible method which can be applied in DPMLM batch demagnetization inspection before delivery.
This paper presents a new non-invasive air gap flux density measurement method for permanent magnet synchronous linear motor (PMSLM) using tunneling magnetoresistance (TMR) sensor and convolutional neural networks-long short-term memory (CNN-LSTM) regression modeling. First, the analytical and finite element models of the air gap magnetic field of PMSLM are established as the data basis. Second, TMR sensor is used to measure the external stray magnetic density. Gramian Angular Field method combined with image similarity matching technology are used to obtain the optimal measurement position of the TMR sensor. Then, a new deep learning regression method as CNN-LSTM is introduced to establish a high-precision mapping model to realize non-invasive high-precision measurement of the air gap magnetic density by “substituting external to internal.” Finally, PMSLM prototype experimental platform with TMR sensor hardware acquisition circuit is built. Comparison confirmatory experiments with Gauss meter can verify the effectiveness and superiority of the proposed method.
Monitoring the status of linear guide rails is essential because they are important components in linear motion mechanical production. Thus, this paper proposes a new method of conducting the fault diagnosis of linear guide rails. First, synchrosqueezing transform (SST) combined with Gaussian high-pass filter, termed as SSTG, is proposed to process vibration signals of linear guide rails and obtain time-frequency images, thus helping realize fault feature visual enhancement. Next, the coordinate attention (CA) mechanism is introduced to promote the DenseNet model and obtain the CA-DenseNet deep learning framework, thus realizing accurate fault classification. Comparison experiments with other methods reveal that the proposed method has a high classification accuracy of up to 95.0%. The experimental results further demonstrate the effectiveness and robustness of the proposed method for the fault diagnosis of linear guide rails.
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