Multi-Source Joint Adaptive Distribution With Online Transfer Learning for Cross-Domain Fault Diagnosis

Methods based on transfer learning have achieved rich research results in the field of intelligent diagnosis of mechanical devices. However, current transfer learning methods typically require the source and target domains to be known in advance, heavily relying on historical data, which fails to meet the requirements of practical applications. Therefore, this study proposes a multisource online transfer learning with joint adaptive distribution selection (MSOTL-JADS) algorithm for real-time diagnosis of online samples. First, during the offline phase, a metric function is designed to extract sub-domain feature information in the multisource domain scenario, facilitating accurate knowledge transfer. Second, the multisource weight distribution parameters obtained based on the distribution distance are dynamically selected for the multisource domains to achieve differentiated transfer in the domain. Third, in the online phase, the pretrained offline model is integrated using online input target samples to construct an online diagnostic model and fine-tuning the weight parameters to match the model accuracy. Finally, several online transfer diagnostic tasks were constructed using two public datasets and one self-constructed dataset. The experimental results demonstrate that the proposed MSOTL-JADS outperforms the comparison methods in terms of performance.