Active label-denoising algorithm based on broad learning for annotation of machine health status

Deep learning has led to tremendous success in machine maintenance and fault diagnosis. However, this success is predicated on the correctly annotated datasets. Labels in large industrial datasets can be noisy and thus degrade the performance of fault diagnosis models. The emerging concept of broad learning shows the potential to address the label noise problem. Compared with existing deep learning algorithms, broad learning has a simple architecture and high training efficiency. An active label denoising algorithm based on broad learning (ALDBL) is proposed. First, ALDBL captures the embedded representation from the time-frequency features by a recurrent memory cell. Second, it augments wide features with a sparse autoencoder and projects the sparse features into an orthogonal space. A proposed corrector then iteratively changes the weights of source examples during the training and corrects the labels by using a label adaptation matrix. Finally, ALDBL finetunes the model parameters with actively sampled target data with reliable pseudo labels. The performance of ALDBL is validated with three benchmark datasets, including 30 label denoising tasks. Computational results demonstrate the effectiveness and advantages of the proposed algorithm over the other label denoising algorithms.