A novel method for fault diagnosis of fluid end of drilling pump under complex working conditions

Accurate fault diagnosis of drilling pump under complex operating conditions poses a significant challenge in drilling operations. This paper addresses the difficulty of extracting fault features from the timing signals of the drilling pump's fluid end under complex working conditions by proposing a method for generating images to represent multidimensional timing signals. Additionally, a multiscale deep recursive inverse residual neural network (MDRIRNN) is introduced to achieve fault diagnosis of the fluid end under such conditions. Firstly, a multidimensional relative position matrix (MRPM) is proposed, which transforms one-dimensional timing signals into three-dimensional images using a 3D data structure. This approach effectively distinguishes the features of different timing signals by adding dimensions. Next, a recursive inverse residual block is designed, and multiscale learning is incorporated to form the MDRIRNN. This model enables the extraction of feature information from the multidimensional images at different scales, thereby enhancing the fault diagnosis process. Subsequently, a fault diagnosis experiment is conducted, demonstrating an average diagnostic accuracy of 96.14 %. Furthermore, the adaptability of the proposed method is validated using the MFPT dataset, achieving a diagnostic accuracy of 99.88 %. This novel method provides a prospecting approach for equipment fault diagnosis under complex operating conditions.