MSOTL: Multi-source online transfer learning-enabled performance degradation assessment of machinery

Abstract

Performance degradation assessment is an important component of predictive maintenance for manufacturing systems and is crucial in determining whether they will persist to the overhaul check stage. However, different manufacturing systems’ feature space varies widely across service and degradation processes. Moreover, in real industrial scenarios, data arrives in real-time in a sequential manner. There is a bottleneck in how the model is being updated in real-time when the data is acquired online. To address the above issues, a novel degradation performance assessment method based on online transfer learning (OTL-DPA) is proposed. The initial transformed matrix is calculated between each source domain and partial target domain to obtain the mapped feature space, based on which the k-nearest neighbor (K-NN) classifier is constructed. When the sequential instances in the remaining target domain arrive, corresponding feature representations are obtained using the above-transformed matrices to reduce the distribution discrepancy. The final assessment results are obtained by integrating the multiple K-NN classifiers on mapped feature spaces. The key of OTL-DPA is to update the transformed matrices and classifier weights each time for a certain amount of samples. Experimental evaluation on fault type diagnosis and fault severity assessment cases indicates that OTL-DPA significantly outperforms non-transfer and general transfer learning methods in the bearing online assessment problems where data were obtained randomly or in a time sequence.