Fault diagnosis of three-cylinder mud pump based on transfer learning

Abstract

Mud pumps serve as vital components within the circulating system of oil drilling platforms, primarily facilitating the circulation of drilling fluid. With the rapid advancement of deep learning technology, there has been a growing focus on fault diagnosis techniques for mud pumps based on deep learning methodologies. However, existing deep learning approaches often struggle with fault diagnosis of mud pumps under varying operational conditions, as adjustments to the working conditions are necessary in real-time based on drilling depth. To address this challenge, this study introduces an enhanced transfer learning method for diagnosing faults in mud pumps across different operating conditions. Initially, the collected vibration data undergoes resampling to standardize frequency, followed by the utilization of the short-term autocorrelation method to discern phase information of signal impact. Leveraging this phase information, the signal is segmented into distinct segments with uniform phases, thereby minimizing distribution discrepancies between the source and target domains. Subsequently, the transformer is employed as a feature extractor for the model. Finally, a deep sub-domain adaptation network is employed to facilitate transfer from the source domain to the target domain. Validation of the proposed method was conducted using an experimental dataset, with results demonstrating its efficacy compared to other contemporary approaches.