Physical model driven fault diagnosis method for shield Machine hydraulic system

Abstract

The hydraulic system of shield machine is critical component, and any faults in this system can have severe consequences on construction progress and even pose risks to the safety of construction personnel. However, the high manufacturing and construction costs of shield machines, as well as the use of highly reliable hydraulic components, make it challenging to obtain a large number of fault samples that accurately reflect the actual operating conditions of the hydraulic system. This limitation is one of the major obstacles to the practical application of intelligent fault diagnosis in shield machines. Furthermore, due to the large scale and complex structure of the hydraulic system in shield machines, few scholars have studied the bottleneck that restricts the fault diagnosis of the hydraulic system of the shield machine. To address these challenges, this paper proposes physical model-driven approach for fault diagnosis in shield machine hydraulic systems, aiming to overcome the lack of fault feature information during the fault diagnosis process. Firstly, physical models for various hydraulic components of the shield machine hydraulic system are established, and the relationship between the physical models and fault characteristics is investigated to provide a theoretical basis for fault injection. Then, a physical model correction method based on cosine similarity is proposed. Using recorded high-frequency fault data from shield machine hydraulic systems, fault injection is performed to obtain simulated data that effectively reflects the actual fault states and normal states of the hydraulic system. Finally, using the simulated data as training samples and measured data as test samples, various artificial intelligence diagnosis models, such as Support Vector Machines (SVM), Extreme Learning Machines (ELM), and Convolutional Neural Networks (CNN), are employed for diagnostic classification. The classification results demonstrate the feasibility and effectiveness of the physical model-driven approach for fault diagnosis in shield machine hydraulic systems. Furthermore, this approach exhibits strong universality and scalability, providing a solution for fault diagnosis scenarios with limited or even no fault samples.