Vibration signals, serving as critical sources of information for monitoring the status of rotating machinery, demand effective extraction and rational utilization of its features to significantly enhance the accuracy and reliability of fault diagnosis. However, vibration signal features typically manifest as nonlinear and nonstationary, posing a significant challenge in industrial settings. To tackle this challenge, this article proposes an enhanced deep intelligent model based on feature fusion and ensemble learning for practical fault diagnosis of rotating machinery. First, a parallel network structure is introduced to comprehensively and accurately explore the fault characteristics of rotating machinery. This network comprises two branches: the first branch designs an improved one-dimensional convolutional neural network to extract locally robust features from raw signals; the second branch adopts variational mode decomposition to decompose raw signals into a set of intrinsic mode functions and extract comprehensive statistical features in both the time and frequency domains, significantly enhancing the signal representation capability. Subsequently, a deep neural network is used to extract more stable feature information. The features from the two branches are then fused, and the final network output is generated through a softmax regression function. Finally, ensemble learning uses a majority voting scheme to obtain more stable final outputs. To confirm the effectiveness of the proposed method, experiments are conducted on two laboratory cases and one industrial case. The experimental results demonstrate that the proposed method significantly improves fault diagnosis accuracy and reliability in controlled laboratory environments and real-world industrial applications, making it highly applicable for real-time monitoring and predictive maintenance of industrial machinery. These improvements can reduce maintenance costs and downtime, thus enhancing operational efficiency in various industrial settings.
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