An Unsupervised Noisy Sample Detection Method for Deep Learning-Based Health Status Prediction

Abstract

Due to rough environment or data-acquisition equipment failure, the machine monitoring data may be contaminated with noise, which may lead to the generation of noisy samples. For the widely used deep learning-based prediction model, some noisy samples (with low-intensity noise) may improve model generalization while some noisy samples (with high-intensity noise) may hamper the prediction models. To detect the noisy samples that hamper the prediction models, an unsupervised noisy sample detection method is proposed, thereby promoting the accuracy of the deep learning-based prediction model. First, the transforming model with an attention mechanism is built to transform the multivariate input sequences of samples to attention weight vectors, which are related to the prediction task. Then, a clustering method without hyperparameters is proposed to cluster these attention weight vectors and the hierarchy of clusters is created. Afterward, the noisy samples that hamper the prediction models are detected and removed based on the clustering thresholds, and a prediction model with higher prediction accuracy can thus be obtained. The effectiveness of the proposed method is verified using a simulated turbofan engine degradation dataset and a real milling machine monitoring dataset. The results show that the prediction errors for the two datasets have been reduced by 13.44% and 12.97% on average after removing the noisy samples detected by the proposed method. The comparison results show that the proposed method outperforms other methods in terms of improving the prediction accuracy.