Online evaluation of crack development in fracturing pumps using an improved MLP network

Crack evaluation in the fracturing pump chamber is essential to ensure the safe conduct of oil and gas fracturing construction. However, the traditional manual evaluation methods have bottlenecks such as downtime for dismantling, grave danger, low efficiency, etc., and detecting crack development in real-time is challenging. This paper proposes an online crack evaluation framework based on an improved multilayer perceptron (MLP), which aims to effectively ensure the safety of equipment construction and personnel, and provide real-time feedback on crack development information. The framework includes (1) Adaptive threshold noise reduction and wavelet packet transform. It effectively resists external complex interference and obtains damage-sensitive signal features as an energy spectrum. (2) MLP network based on arithmetic whale optimisation. It realises high-precision identification of the cavity’s crack state and reduces computation and parameters compared with other advanced networks. (3) The first online crack evaluation system in the fracturing industry. Through oilfield engineering field tests, it can realise online evaluation and early warning of crack development in the pump body cavity during fracturing, with a comprehensive accuracy rate of 87.06%, further validating the engineering feasibility and broad application prospect of the proposed framework.