Yulin Wang

The issue of insufficient multi-scale feature extraction and difficulty in accurately classifying fault features in rolling bearing fault diagnosis is addressed by proposing a novel diagnostic method that integrates stochastic convolutional neural networks (SCNNs) and a hybrid kernel extreme learning machine (HKELM). First, the convolutional layers of the CNN were designed as multi-branch parallel layers to extract richer features. A stochastic pooling layer, based on a Bernoulli distribution, was introduced to retain more spatial feature information while ensuring feature diversity. This approach enabled the adaptive extraction, dimensionality reduction, and elimination of redundant information from the vibration signal features of rolling bearings. Subsequently, an HKELM classifier with multiple kernel functions was constructed. Key parameters of the HKELM were dynamically adjusted using a novel optimization algorithm, significantly enhancing fault diagnosis accuracy and system stability. Experimental validation was performed using bearing data from Paderborn University. A comparative study with traditional diagnostic methods demonstrated that the proposed model excelled in both fault classification accuracy and adaptability across operating conditions. Experimental results showed a fault classification accuracy exceeding 99%, confirming the practical value of the method.

The fault diagnosis of industrial equipment is crucial for ensuring production safety and improving the efficiency of equipment operation. With the advancement of sensor technologies, the number of data generated in industrial environments has increased dramatically. Deep learning techniques, with their powerful feature extraction and classification capabilities, have become a research hotspot in the field of fault diagnosis. However, deep learning models are vulnerable to adversarial attacks, which can lead to a decrease in diagnostic accuracy and compromise system safety. This paper proposes a Joint Projection Gradient Descent (SCS-JPGD) method based on single-channel signal features. The proposed method first introduces a gradient-based attack approach for signal samples, which can add tiny perturbations to the input samples, causing misclassification in black-box models. Secondly, a joint training strategy is proposed for gradient attacks on signal samples, aiming to enhance the model’s adaptability to small perturbations in a limited range. Experiments were conducted on the CWRU dataset under four different operating conditions. The results show that, under a deep learning model with diagnostic accuracy exceeding 90%, the joint training method allows the model to maintain an average accuracy of 84.6% even after the addition of adversarial samples, which are barely distinguishable by the human eye. The proposed SCS-JPGD method provides a safer and more accurate approach for fault diagnosis in deep learning research.