Fault prediction of pneumatic valves in an LNG plant by the DGM(1, 1) model

Stop production and overhaul is an annual work of LNG (Liquefied Natural Gas) plant. However, the shutdown maintenance work under different market conditions needs comprehensive allocation of resources to achieve the purpose of economy, speed and safety. In order to study the fault situation of pneumatic valve, targeted maintenance work is carried out. In this paper, a workflow of DGM(1, 1) prediction model of grey system and sampling inspection method is proposed. With the help of DGM(1, 1) to predict the universality of "poor information and small sample" and the partial detection method of sampling inspection, the expected engineering purpose is achieved. The research results are as follows: (1) The failure situation of pneumatic valve in LNG plant from 2015 to 2020 is brought into the model to compare the actual value and predicted value in 2020, and the residual error is between 0.92 and 3, which indicates that the predicted result of the model is reliable. It is predicted that the comprehensive failure rate will remain at around 18% in 2021 and 2022. Next, we can continue to use DGM(1, 1) model to analyze the redundancy of actuator measurable signals. Make full use of the field known data, construct the functional relationship of measurable variables of actuators, realize on-line real-time judgment of actuator faults, and dynamically predict the failure rate of parts. (2) For maintenance work, 130 pneumatic valves need to be dismantled, with emphasis on the inspection of three components such as drive gas filter, electromagnetic valve and actuator. If the damage number is found to be less than or equal to 4, production can continue after the spare parts are repaired and replaced, otherwise, total inspection is required. In the next step, the relationship between valve distribution position and parts damage frequency should be counted, and the damage frequency should be reduced by strengthening sealing and changing installation methods.