Bearing fault diagnosis with intermediate domain based Layered Maximum Mean Discrepancy: A new transfer learning approach

In the last decade, deep learning models for condition monitoring of mechanical systems increasingly gained importance. Most of the previous works use data of the same domain (e.g., bearing type) or of a large amount of (labeled) samples. This approach is not valid for many real-world scenarios from industrial use-cases where only a small amount of data, often unlabeled, is available. In this paper, we propose, evaluate, and compare a novel technique based on an intermediate domain, which creates a new representation of the features in the data and abstracts the defects of rotating elements such as bearings. The results based on an intermediate domain related to characteristic frequencies show an improved accuracy of up to 32 % on small labeled datasets compared to the current state-of-the-art in the time-frequency domain. Furthermore, a Convolutional Neural Network (CNN) architecture is proposed for transfer learning. We also propose and evaluate a new approach for transfer learning, which we call Layered Maximum Mean Discrepancy (LMMD). This approach is based on the Maximum Mean Discrepancy (MMD) but extends it by considering the special characteristics of the proposed intermediate domain. The presented approach outperforms the traditional combination of Hilbert-Huang Transform (HHT) and S-Transform with MMD on all datasets for unsupervised as well as for semi-supervised learning. In most of our test cases, it also outperforms other state-of-the-art techniques. This approach is capable of using different types of bearings in the source and target domain under a wide variation of the rotation speed.

Automated summary

This study introduces a novel technique based on an intermediate domain that achieves improved accuracy on small labeled datasets, and proposes a Convolutional Neural Network architecture for transfer learning along with a new transfer learning approach. The method outperforms traditional combinations on all datasets for both unsupervised and semi-supervised learning, and shows promising results in handling different types of bearings and varying rotation speeds.