Abstract:Rolling bearing fault diagnosis is one of crucial tasks in mechanical equipment fault diagnosis. Currently, artificial intelligence and machine learning-driven fault diagnosis methods are extensively utilized for rolling bearing. When compared to traditional techniques, the diagnostic accuracy has significantly improved. These methods, however, need a substantial amount of labelled training data, which is difficult to obtain in actual failures. In order to resolve this problem, Transfer Learning (TL) was creat… Show more
“…A lack of data suitable data and uncertainties associated with data collected via indirect methods can lead to suboptimal prediction and classification outcomes with potentially grievous effects on the lifespan of components. Although there have been studies in recent years investigating few-shot learning for data imbalance [27,28,29], there is limited research on building few-shot learning models using indirect variables to address the scarcity of key direct variables affecting component abnormalities. Most previous research on photomask haze have concentrated on physical and chemical phenomena.…”
Predictive Maintenance (PdM) plays a key role in production management by extending the lifespan of components and reducing maintenance costs. In the manufacture of semiconductors, the scarcity of data related to haze defects makes it difficult to draw correlations between environmental factors and haze formation. The uncertainty associated with a reliance on indirect evidence (i.e., cumulative time in the environment) has seldom been explored in the literature. Therefore, we developed a PdM framework based on fuzzy few-shot learning to deal with photomask haze in the semiconductor industry. The robustness of the model was evaluated in a three-month pilot study conducted in a wafer foundry. This paper also provides cost-benefit analysis of model implementation. The results demonstrate that the proposed haze photomask detection model outperforms existing models in terms of hit rates and false alarm rates. It also proved effective in lowering labor costs and power consumption, as evidenced by the fact that the number of haze candidates was well below the daily inspection cap, which allowed the decommissioning of one photomask inspection device, reducing the photomask inspection volume by 21%, which is equivalent to an annual labor cost reduction of USD 18,780. These results should help to promote the adoption of predictive maintenance applications, even in situations with small sample sizes.
“…A lack of data suitable data and uncertainties associated with data collected via indirect methods can lead to suboptimal prediction and classification outcomes with potentially grievous effects on the lifespan of components. Although there have been studies in recent years investigating few-shot learning for data imbalance [27,28,29], there is limited research on building few-shot learning models using indirect variables to address the scarcity of key direct variables affecting component abnormalities. Most previous research on photomask haze have concentrated on physical and chemical phenomena.…”
Predictive Maintenance (PdM) plays a key role in production management by extending the lifespan of components and reducing maintenance costs. In the manufacture of semiconductors, the scarcity of data related to haze defects makes it difficult to draw correlations between environmental factors and haze formation. The uncertainty associated with a reliance on indirect evidence (i.e., cumulative time in the environment) has seldom been explored in the literature. Therefore, we developed a PdM framework based on fuzzy few-shot learning to deal with photomask haze in the semiconductor industry. The robustness of the model was evaluated in a three-month pilot study conducted in a wafer foundry. This paper also provides cost-benefit analysis of model implementation. The results demonstrate that the proposed haze photomask detection model outperforms existing models in terms of hit rates and false alarm rates. It also proved effective in lowering labor costs and power consumption, as evidenced by the fact that the number of haze candidates was well below the daily inspection cap, which allowed the decommissioning of one photomask inspection device, reducing the photomask inspection volume by 21%, which is equivalent to an annual labor cost reduction of USD 18,780. These results should help to promote the adoption of predictive maintenance applications, even in situations with small sample sizes.
“…TL can help the model achieve higher precision with less computational cost by transferring low-level features and fne-tuning high-level layers. Zhang et al [25] proposed a convolutional neural network (CNN)-based two-layer transfer learning (CTTL) method for fault diagnosis. CTTL changes the process of the transfer learning method from learning the distribution of domains to learning the distribution of fault types in more detail, which will get higher accuracy.…”
The marine diesel engine is an important power machine for ships. Traditional machine learning methods for diesel engine fault diagnosis usually require a large amount of labeled training data, and the diagnosis performance may decline when encounters vibrational and environmental interference. A transfer learning convolutional neural network model based on VGG16 is introduced for diesel engine valve leakage fault diagnosis. The acquired diesel engine cylinder head vibration signal is first converted to time domain, frequency domain, and wavelet decomposition images. Secondly, the VGG16 deep convolutional neural network is pretrained using the ImageNet dataset. Subsequently, fine tuning the network based on the pretrained basic parameters and image enhancement methods. Finally, the well-trained model is adopted to train and test the target dataset. In addition, the cosine annealing learning rate setting method is used to make the learning rate close to the global optimal solution. Experimental results show that the proposed method has higher accuracy and better robustness against noise with a small sample dataset than traditional methods and deep learning models. This study not only demonstrates a novel view for the diagnosis of marine diesel engine valve leakage, but also provides an applicable diagnosis method for other similar issues.
“…Te proposed model was validated on two datasets collected from motor bearings. Zhang et al [14] proposed a new method that combined deep convolutional neural network (DCNN) and transfers' learning (TL) for fault diagnosis to handle diferent fault types. Eren [15] proposed a one-dimensional convolutional neural network (1D-CNN) for a fast and accurate bearing fault detection system.…”
Traditional fault diagnosis methods require complex signal processing and expert experience, and the accuracy of fault identification is low. To solve these problems, a fault diagnosis method based on an improved convolutional neural network (CNN) is proposed. Based on the traditional CNN model, a variety of convolution stride modes were added to extract features of different scales of signals and expand the feature dimension. Firstly, the vibration signals were collected and grouped. Then, the data were divided into a training set and a test set and input into improved CNN for feature extraction and model training to realize fault identification. The proposed model achieved a classification accuracy of 99.3% when testing the vibration data of the armored vehicle. Finally, the proposed model was used to classify different fault types of planetary gearboxes. The gradient-weighted class activation mapping (Grad-CAM) method was used to visualize the classification weight of samples. The results showed that the classification accuracy reaches 98% under various working conditions of the planetary gearbox.
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