Backlash is a commonly non-linear phenomenon, which can directly degrade the control accuracy of a pneumatic control valve. To explain the cause and law of backlash error, and to propose an effective method, many research works on the modeling of a pneumatic control valve system have been carried out. The currently model of a control valve system can be classified as a physical model, data-driven model, and semi-physical model. However, most models only consider the force-displacement conversion process of a pneumatic diagram actuator in a pneumatic control valve system. A physical model based on the whole workflow of the pneumatic control valve system is established and a control method to eliminate the backlash error is proposed in this paper. Firstly, the physical model of the pneumatic control valve system is established, which is composed of three parts: pneumatic diaphragm actuator model, nozzle-flapper structure model and electromagnetic model. After that, the input–output relationship of the pneumatic control valve system can be calculated according to the established physical model, and the calculation results are consistent with the experimental result. Lastly, a self-calibration PID (SC-PID) control method is proposed for backlash error elimination. The proposed method can solve valve stem oscillation caused by backlash during valve control.

Valve positioner is the core component of the pneumatic control valve. A new software and hardware design scheme of intelligent valve positioner is presented in this paper. The circuit composition of each part of the intelligent valve positioner is introduced in hardware part. Based on it, a hardware solution to realize HART ‘multi-point’ communication is proposed in this research. In the software design, a novel combined PID control algorithm is proposed to solve the nonlinear problem caused by the friction between the valve stem and the packing during the control process. Simulation results show that the method proposed in this paper is better than traditional PID method and fuzzy PID method. The software and hardware design scheme of the valve positioner proposed in this paper has certain guiding significance for the development of related products.

Localization of fatigue cracks imposes immense significance to ensure the health of the engineering structures and prevent further catastrophic accidents. The nonlinear ultrasonic waves, especially the nonlinear Lamb waves, have been increasingly studied and employed for identifying micro-damages that are usually invisible to traditional linear ultrasonic waves. However, it remains a challenge to locate the fatigue cracks using nonlinear Lamb waves owing to the enormous difficulties in decoding location information from acoustic nonlinearity. Motivated by this, this work presents a data-driven method for precise location of fatigue crack using nonlinear Lamb waves. A 1D-Attention-convolutional neural network is developed to correlate the fatigue crack location with the wavelet coefficients at the second harmonic frequency of Lamb wave signals. The introduction of the Attention layer enables the models to pay more attention to the desired nonlinear features which dominates locating the fatigue crack. In particular, a convenient dataset creation scheme guided by the relative value label is proposed to generate sufficient data commonly required for deep learning approach. In addition, a lightweight single-excite-multiple-receive signal acquisition method is adopted instead of full-matrix capture method used in the traditional research, which highly improves detection efficiency. Numerical simulation and experimental validation manifest that the trained network can be used to establish the complex mapping between the nonlinear ultrasonic signals and the fatigue crack location features, so as to locate barely visible fatigue cracks. Our work provides a promising and practical way to facilitate nonlinear Lamb waves to accurately locate fatigue cracks in large-scale plate-like structures.