Small-Sample-Oriented Multi-Condition Fault Diagnosis Framework based on Classifier-Free Denoising Diffusion Implicit Model with Multi-Class Contrastive Learning

Industrial fault diagnosis (FD) is crucial for ensuring a manufacturing process’s safety, reliability, and efficiency. However, significant challenges arise from small fault sample sizes and the inherent variability of operating conditions. To tackle the above challenges, this article presents a novel small-sample-oriented FD framework, CF-DDIM-MCCL, leveraging the classifier-free denoising diffusion implicit model combined with multiclass contrastive learning. Additionally, CF-DDIM-MCCL incorporates a dynamic training termination strategy to enhance training efficiency by optimizing the learning process. It also employs a K-nearest neighbor (KNN) classifier instead of the traditional Softmax classifier, resulting in notable improvements in fault identification stability and accuracy. Extensive confirmatory and comparative experiments were conducted on two benchmark datasets, the Case Western Reserve University (CWRU) and Spectra Quest (SQ) bearing datasets, and in a real industrial application scenario, axial flow fan FD from a steel-making plant. Experimental results demonstrate the fault classification accuracies of 99.361%, 98.140%, and 98.542% across the three test cases. The outstanding performance of the proposed approach across diverse benchmark datasets and practical applications underscores its potential to revolutionize FD practices in natural industrial environments.