Abstract: A two-dimensional three-degree-of-freedom micro-motion platform structure for high-precision positioning and measurement. Two series flexible hinges are connected in parallel to form a moving pair. One end of the moving pair is fixed, and driving force is applied to the other end. The driving force is amplified by means of a lever to drive the moving pair to translate and rotate. The moving pair drives a first flexible hinge of a platform to rotate, thereby driving the platform to produce corresponding displacement. Four identical moving pairs and four identical first flexible hinges respectively constitute four branch hinges which are different in arrangement; two branch hinges opposite to the platform are in central symmetry and constitute one group, and the four branch hinges are divided into two groups in total.

Abstract: A two-dimensional three-degree-of-freedom micro-motion platform structure for high-precision positioning and measurement. Two series flexible hinges are connected in parallel to form a moving pair. One end of the moving pair is fixed, and driving force is applied to the other end. The driving force is amplified by means of a lever to drive the moving pair to translate and rotate. The moving pair drives a first flexible hinge of a platform to rotate, thereby driving the platform to produce corresponding displacement. Four identical moving pairs and four identical first flexible hinges respectively constitute four branch hinges which are different in arrangement; two branch hinges opposite to the platform are in central symmetry and constitute one group, and the four branch hinges are divided into two groups in total.

Abstract: Disclosed is a method for evaluating a health status of mechanical equipment. Firstly, status data of main components on mechanical equipment are collected by a sensor, and feature extraction is performed to obtain feature parameters. Then, noise data and fault data are extracted by an outlier detection algorithm, and only the fault data are retained. Subsequently, dimension reduction processing is performed to obtain a feature vector for final evaluation. Finally, equipment status evaluation is performed, a self-organizing map neural network model is established by health status data and failure status data, rate impact factors of each group of data to be evaluated are calculated by an entropy weight theory, and the rate impact factors are introduced into a neural network to perform health factor calculation. The present invention implements overall status evaluation for mechanical equipment, provides a basis for health maintenance of the mechanical equipment, and avoids unnecessary economic losses.