Rolling Bearing Fault Diagnosis Based on SCNN and Optimized HKELM

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.