Chaoli Sun

Real-time intelligent fault diagnosis is crucial for the early detection of incipient faults; however, its effectiveness is often severely hindered by the scarcity of fault data in predominantly healthy operating environments. To address this data imbalance, we propose a novel conditional denoising diffusion probabilistic model, designed to generate high-fidelity fault signals to assist in improving the fault diagnostic performance. Specifically, the proposed method incorporates an adaptive dilated convolution module that dynamically adjusts receptive fields based on the input signal content, thereby enhancing the capability to capture cross-scale temporal features. To further optimize signal quality, we introduce an adaptive timestep-aware attention mechanism. This component flexibly modulates the generation process, effectively distinguishing between the establishment of global temporal sequences and the refinement of local fault details. Furthermore, to tackle the challenge of mode collapse in small-sample tasks, an intra-batch diversity regularization loss is designed to guide the network in generating distinct samples by penalizing excessive similarity. Experimental results on three public datasets demonstrate that the proposed model significantly outperforms existing approaches in generating synthetic data in terms of signal quality and diversity, leading to improved fault diagnosis accuracy and highlighting its potential for robust industrial fault monitoring.

Surrogate-assisted evolutionary algorithms are frequently applied to solve time-consuming, resource-intensive, and black-box multiobjective optimization problems. Multiple approximation effectively identifies an approximate optimal solution set within finite exact function evaluations, which may need significant training time for the surrogate models. This article offers a model training method guided by estimation errors to assist the evolutionary algorithm in the search for optimal solutions. In model training, we dynamically use the Gaussian process (GP) and radial basis function (RBF) models following the adjacent generation discrepancy of estimation errors to reduce computational time. They are updated if only the current estimation error exceeds the previous, where the estimation error combines the minimum distance in the decision space and the prediction error of all test samples. In the model-assisted search, an autonomous function estimation method is proposed based on the preference for approximate model errors. The selection of the updated GP or RBF approximation is via a lower model estimation error; in contrast, the average is considered the function value of an individual. In infill sampling, the solution is selected based on the nondominated sorting of function estimation with the maximum angle. The uncertainty-based sampling method is to replenish when these models are not updated. The experiment investigates the effectiveness of the error preference-guided approximation method. The results of two classic benchmark problems and one practice problem show the superiority of the proposed algorithm compared to even well-performed optimization algorithms.