2results about How to "Reduce dynamic error" patented technology

This invention provides a real-time adaptive 3D measurement system and method for objects based on microstructured light. The system includes: multiple optical measurement units arranged in an equidistant circular array along the circumference on a ring mounting frame and moving synchronously with a lifting and adjusting mechanism; multiple measurement posture acquisition units, a reference posture monitoring unit, a posture compensation unit, a control unit, a data fusion module, and a quality analysis module. According to this invention, the multiple optical measurement units arranged in a circular array enable multi-view synchronous measurement of the workpiece, avoiding the time consumption and reference conversion errors of traditional indexing measurement methods. The real-time posture monitoring and dynamic compensation mechanism effectively suppresses measurement errors caused by factors such as mechanical vibration and temperature drift, reducing posture-related measurement errors. The system achieves automation, real-time measurement, and high precision in 3D measurement, improving measurement efficiency and accuracy.

The invention discloses a hydraulic machinery stall vortex online identification method based on nonlinear dynamic characteristics, and relates to the field of hydraulic machinery operation monitoring and fault diagnose.The hydraulic machinery stall vortex online identification method can distinguish loading and unloading processes by introducing an empirical formula and a model of a guide vane change rate, accurately identifies a vortex strip state on a hysteresis loop, and improves the accuracy of stall vortex identification. The dynamic error is reduced by more than 80%; secondly, through an empirical formula, the input of the neural network has a clear hydraulics meaning (such as a velocity triangle relation), and the unreliability of a pure black box model is avoided; wherein the complex fluid physical law is concentrated in an empirical formula, the neural network only needs to process residual mapping, the operation speed is extremely high, and the method can be deployed in an in-situ control unit.