Early diagnosis of breakdown through transfer learning

Abstract

Breakdown prediction of equipment is an essential task considering the management of resources and maintenance operations. Early diagnosis systems allow creating alerts on time for taking precautions on production. A significant challenge for diagnosis is to have an insufficient size of data, yet, transfer learning approaches can alleviate such an issue when there is a constrained supply of training data. We intend to improve the reliability of breakdown prediction when there is a limited quantity of training data. We recommend similarity correlation on Remaining Useful Life of these equipment. To do this, we offer learning a common feature space between the target and the source equipment, where we acquire prior knowledge from the source that has different measurements than the target. Within the learned joint feature matrices, we train our model on the vast amount of data of different equipment and finetune it using the data of our target equipment. In this way, we aim to obtain an accurate and reliable model for early breakdown prediction.