Degradation recognition plays an important role in improving the safety of mechanical operation. For most of the existing recognition methods, using large amounts of labeled training data that obey the same probability distribution as testing data is an important pre-requisite for training effective recognition model. Unfortunately, it is difficult to collectmassive labeled condition data for some machines like ball screw. This makes it a huge challenge to build reliable recognition model since the labeled training data collected in target domain are insufficient. However, large amounts of labeled data collected in different but related domain (called source domain) are usually available, which can be treated as auxiliary training data to help build better recognition model in target domain. Inspired by the idea of transferring knowledge from source domain to target domain, a new intelligent method named instance-based ensemble deep transfer learning network (IEDT) is proposed in this paper to recognize the degradation under various operating conditions. IEDT mainly consists of three parts: First, source instances that fit better with the target domain are selected by filtering out these unrelated samples iteratively. The remaining source instances are employed to assist insufficient labeled target data to train multiple stacked auto-encoders (SAEs) with different activation functions. And then, the trained SAEs are transferred to target domain as feature extractors. The extracted features are fed into support vector machine (SVM) to construct SAE-SVM models, which are further trained by limited target training data and used for degradation recognition. Finally, ensemble strategy is proposed to calculate final results based on multiple predicted labels of individual SAE-SVMs. Run-to-failure test of ball screw is carried out to collect experimental data under different working conditions. The effectiveness of the proposed IEDT method is validated by four transfer degradation recognition experiments of ball screw. Results indicate that the proposed IEDT method is superior to comparison methods in degradation recognition when there is only a small amount of labeled condition data. (C) 2020 Elsevier Ltd. All rights reserved.
