The predictive maintenance of electrical machines is a critical issue for companies, as it can greatly reduce maintenance costs, increase efficiency, and minimize downtime. In this paper, the issue of predicting electrical machine failures by predicting possible anomalies in the data is addressed through time series analysis. The time series data are from a sensor attached to an electrical machine (motor) measuring vibration variations in three axes: X (axial), Y (radial), and Z (radial X). The dataset is used to train a hybrid convolutional neural network with long short-term memory (CNN-LSTM) architecture. By employing quantile regression at the network output, the proposed approach aims to manage the uncertainties present in the data. The application of the hybrid CNN-LSTM attention-based model, combined with the use of quantile regression to capture uncertainties, yielded superior results compared to traditional reference models. These results can benefit companies by optimizing their maintenance schedules and improving the overall performance of their electric machines.

/pdf/machine-fault-detection-using-a-hybrid-cnn-lstm-attention-4lz2pp94.pdf

Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model

Intelligent fault diagnosis can enhance the reliability of mechanical equipment. However, only a few labels are available in a large amount of fault data due to high labeling costs in practical engineering. The fault recognition capability of existing semi-supervised diagnosis methods is severely insufficient with limited labels, especially with extremely limited labels that only a single labeled sample available per fault type. To address this issue, a novel semi-supervised ensemble fault diagnosis framework termed ADAE-LFDM is proposed based on adversarial decoupled auto-encoder (ADAE) and low-dimensional feature distance metric (LFDM). Firstly, the locally selective combination in parallel outlier ensembles (LSCP) method is introduced to efficiently separate normal and fault samples. Subsequently, an ADAE with branching structure and latent space feature regularization strategy is proposed to decouple and capture the fault feature. Finally, a LFDM strategy that contains feature dimensionality reduction, and centroid-based metric is performed to achieve high-accuracy fault diagnosis. Experimental results based on two rotating machinery datasets have demonstrated that the proposed method achieves a diagnostic accuracy of over 97 % when there is only a single labeled sample available per fault type, and an average diagnostic accuracy of 85 % under cross-operating condition, showing the superiority compared to other methods. 

Semi-supervised Ensemble Fault Diagnosis Method based on Adversarial Decoupled Auto-encoder with Extremely Limited Labels

A Review of Artificial Intelligent Methods for Machined Surface Roughness Prediction

This paper introduces a novel approach for detecting inter-turn short-circuit faults in rotor windings using wavelet transformation and empirical mode decomposition. A MATLAB/Simulink model is developed based on electrical parameters to simulate the inter-turn short circuit by adding a resistor parallel to phase “a” of the rotor. The resulting high current in the new phase indicates the presence of the short circuit. By measuring the rotor and stator three-phase currents, the fault can be detected as the currents exhibit asymmetric behavior. Fluctuations in the electromagnetic torque also occur during the fault. The wavelet transform is applied to the rotor current, revealing an effective analysis of sideband frequency components. Specifically, changes in amplitude and frequency, particularly in d7 and a7, indicate the presence of harmonics generated by the inter-turn short circuit. The simulation results demonstrate the effectiveness of wavelet transformation in analyzing these frequency components. Additionally, this study explores the use of empirical mode decomposition to detect faults in their early stages, observing substantial changes in the instantaneous amplitudes of the first three intrinsic mode functions during fault onset. The proposed technique is straightforward and reliable, making it suitable for application in wind turbines with simple electrical inputs. 

Efficient Fault Detection of Rotor Minor Inter-Turn Short Circuit in Induction Machines Using Wavelet Transform and Empirical Mode Decomposition

Abstract Traction motor bearings are crucial for guaranteeing the safe operation of metro vehicles. However, in the metro traction motor bearing fault diagnosis, there are usually problems of small samples and missing fault samples, leading to inaccurate results. Therefore, a novel bearing fault diagnosis method utilizing a track-vehicle-bearing coupled dynamic model and the IDCG-MAMCNN model is proposed in this paper. The IDCG-MAMCNN model combines an improved deep convolutional generative adversarial network (IDCGAN) with a multi-scale convolutional neural network with mixed attention (MA-MCNN). Specifically, simulation data is first provided by the coupled dynamic model to supplement missing fault samples. Secondly, the IDCGAN, along with a training method that involves pre-training models with simulation samples and fine-tuning models with experimental samples, is introduced to generate high-quality samples and augment experimental samples under small samples. Lastly, the MA-MCNN serves as the classification model, trained with the augmented dataset comprising experimental, simulation, and generated samples. The fault diagnosis performance of the proposed method is evaluated on the experimental samples of two bearing datasets under small samples and various conditions of missing fault samples. It has been demonstrated by the experimental results that the proposed method exhibits robust fault diagnosis performance and generates high-quality samples under small samples and missing fault samples. Furthermore, the proposed method showcases its adaptability to different operation speeds. 

Simulation data-driven fault diagnosis method for metro traction motor bearings under small samples and missing fault samples

The realization of accurate fault diagnosis is crucial to ensure the normal operation of machines. At present, an intelligent fault diagnosis method based on deep learning has been widely applied in mechanical areas due to its strong ability of feature extraction and accurate identification. However, it often depends on enough training samples. Generally, the model performance depends on sufficient training samples. However, the fault data are always insufficient in practical engineering as the mechanical equipment often works under normal conditions, resulting in imbalanced data. Deep learning-based models trained directly with the imbalanced data will greatly reduce the diagnosis accuracy. In this paper, a diagnosis method is proposed to address the imbalanced data problem and enhance the diagnosis accuracy. Firstly, signals from multiple sensors are processed by the wavelet transform to enhance data features, which are then squeezed and fused through pooling and splicing operations. Subsequently, improved adversarial networks are constructed to generate new samples for data augmentation. Finally, an improved residual network is constructed by introducing the convolutional block attention module for enhancing the diagnosis performance. The experiments containing two different types of bearing datasets are adopted to validate the effectiveness and superiority of the proposed method in single-class and multi-class data imbalance cases. The results show that the proposed method can generate high-quality synthetic samples and improve the diagnosis accuracy presenting great potential in imbalanced fault diagnosis.

/pdf/fault-diagnosis-method-for-imbalanced-data-based-on-multi-3oita94u.pdf

Fault Diagnosis Method for Imbalanced Data Based on Multi-Signal Fusion and Improved Deep Convolution Generative Adversarial Network

Traditional on-line surface roughness prediction models are mainly established by surrogate models, which can achieve well prediction accuracies with a fixed tool-workpiece combination. However, a poor prediction accuracy comes to an established model when the tool or workpiece is changed. Then, multiple experiments are required to obtain sufficient new data to establish a new prediction model, increasing the time and economy costs. This paper proposes a data-driven method using transfer learning for on-line classifying the surface roughness under multiple milling conditions. First, a source tool is selected to perform the milling experiments to construct the source data. A stack sparse autoencoder (SSAE) is pre-trained to online classify the surface roughness, where the inputs are the machining parameters and the features derived from the force signals in time and frequency domains. Then, a new tool is selected to perform the milling experiments under fewer milling conditions to construct the target data. The pre-trained SSAE are fine-tuned by re-training the network using the limited target data. Finally, a surface roughness classifier of the target tool is established to adapt to the new milling conditions. Furthermore, a detailed experimental validation is carried out on three different tools of a vertical machining center, indicating a significant potential in establishing an accurate surface roughness classifier with limited milling experiments. 

On-line surface roughness classification for multiple CNC milling conditions based on transfer learning and neural network

Digital element approach based automated conformal mesh generation methodology for meso-scale modeling of realistic textile composites and progressive failure analysis under compressive load

Dual-conditional feature constraints based tri-component decoupling domain adaptation network for cross-machine fault diagnosis

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Papers

Fault Diagnosis Method for Imbalanced Data Based on Multi-Signal Fusion and Improved Deep Convolution Generative Adversarial Network

On-line surface roughness classification for multiple CNC milling conditions based on transfer learning and neural network

Digital element approach based automated conformal mesh generation methodology for meso-scale modeling of realistic textile composites and progressive failure analysis under compressive load

Dual-conditional feature constraints based tri-component decoupling domain adaptation network for cross-machine fault diagnosis