Robust Unsupervised Acoustic Anomaly Detection for Turbo Molecular Pumps Using ResNet–Convolutional Block Attention Module and Structural Similarity Loss

Abstract

In semiconductor and optoelectronics manufacturing, the reliability of turbo molecular pumps (TMPs) is vital for maintaining vacuum integrity and ensuring product yield. However, acoustic monitoring in cleanrooms faces severe challenges due to ambient noise levels routinely exceeding 80 dBA and the scarcity of labeled anomaly data. This study proposes an unsupervised acoustic anomaly detection and localization system to address these issues. The performance of the proposed framework is evaluated using an acoustic dataset collected from operational turbopumps in an industrial semiconductor cleanroom environment, encompassing both normal operations and naturally occurring failure conditions. We introduce a Convolutional Autoencoder (CAE) based on ResNet-18, integrated with a Convolutional Block Attention Module (CBAM) to adaptively suppress high-decibel environmental noise. To enhance sensitivity to structural spectral defects, a hybrid loss function combining Mean Squared Error (MSE) and structural similarity index measure (SSIM) is implemented. Experimental results, supported by rigorous hyperparameter sensitivity analysis, demonstrate that the proposed model achieves an outstanding AUC of 0.9535 and a fault recall of 99.06% under a validation-calibrated threshold, significantly outperforming standard U-Net architectures. Furthermore, the system generates anomaly heatmaps for precise time–frequency localization, enabling explainable diagnostics. With a model inference throughput of 330.28 FPS and an end-to-end processing rate of ≈73× in real time, the proposed framework provides an efficient and robust solution for real-time predictive maintenance in noisy industrial settings.