Patents by Inventor Zhiwei Mao

Zhiwei Mao has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20210255059
    Abstract: The present disclosure relates to a fault diagnosis method of a reciprocating machinery based on a keyphasor-free complete-cycle signal. The method includes the following steps: 1) building a complete-cycle vibration signal image library; 2) training an image recognition model; 3) acquiring a complete-cycle data on a keyphasor-free basis; 4) building an automatic feature extraction model; and 5) inputting a hidden layer feature of an autoencoder into a support vector machine (SVM) classifier to obtain a diagnosis result. By using a deep cascade convolutional neural network (CNN), the present disclosure achieves the goal of complete-cycle data acquisition on a keyphasor-free basis, solves the problems that traditional intelligent fault diagnosis relies on a keyphasor signal and real-time diagnosis fails due to insufficient installation space. In addition, by using an autoencoder for automatic feature extraction, the present disclosure avoids manual feature selection, reduces labor costs.
    Type: Application
    Filed: February 2, 2021
    Publication date: August 19, 2021
    Applicant: Beijing University of Chemical Technology
    Inventors: Jinjie ZHANG, Zhinong JIANG, Haipeng ZHAO, Zhiwei MAO, Kun CHANG
  • Publication number: 20210140431
    Abstract: A load identification method for reciprocating machinery based on information entropy and envelope features of an axis trajectory of a piston rod. According to the present disclosure, firstly, the position of an axial center is calculated according to a triangle similarity theorem to obtain an axial center distribution; secondly, features are extracted from the axial center distribution of the piston rod by means of an improved envelope method for discrete points as well as an information entropy evaluation method; thirdly, a dimensionality reduction is carried out on the features by means of manifold learning to form a set of sensitive features of the load; and finally, a neural network is trained to obtain a load identification classifier to fulfill automatic identification on the operating load of the reciprocating machinery. The advantages of the present disclosure are verified by means of actual data of a piston rod of a reciprocating compressor.
    Type: Application
    Filed: November 4, 2020
    Publication date: May 13, 2021
    Applicant: Beijing University of Chemical Technology
    Inventors: Jinjie Zhang, Zhinong Jiang, Xudong Zhang, Zhiwei Mao, Yao Wang