A zero-cost unsupervised transfer method based on non-vibration signals fusion for ball screw fault diagnosis

Vibration -based fault diagnosis methods of ball screw are susceptible to noise and transmission path. Moreover, the accuracy of supervised deep learning models depends on large amounts of labeled samples, which are not only difficult to obtain but also laborious to label. Therefore, to solve these problems, a zero-cost unsupervised transfer method based on non -vibration signals fusion is proposed to achieve ball screw fault diagnosis in this paper. Firstly, non -vibration signals, such as current and speed, are adopted and orderly fused together to constitute multi-source fusion signal samples, which are easier to obtain and contain fewer interferences than vibration signals. Secondly, by virtue of its excellent abnormal detection ability, isolation forest algorithm is circularly utilized to generate pseudo-labels of source domain samples without manual labeling, which further realizes zero-cost sample labeling and unsupervised process. Finally, large amount of generated pseudo-labeled samples of source domain is applied to pre-train the transfer model parameters, and fine-tuning strategy with small number of labeled samples of target domain is used to complete transfer fault diagnosis of ball screw. The effectiveness of the proposed method is verified by ball screw signals across three different operation conditions, ablation and comparison analysis are also studied to illustrate its advantages.