Abstract: Systems and methods of monitoring inventory of a product storage facility include an image capture device configured to move about the product storage areas of the product storage facility and capture images of the product storage areas from various angles. A computing device coupled to the image capture device obtains the images of the product storage areas captured by the image capture device and processes the obtained images of the product storage areas to detect individual products captured in the obtained images. Based on detection of the individual products captured in the images, the computing device analyzes each of the obtained images to extract meta data from the packaging the individual products to detect one more keywords and determine the locations of the detected keywords on the packaging, and then utilize this information to predict an identity of the products associated with the packaging.

Abstract: An impedance matching method for a CLC branch of a low-frequency resonance suppression device is provided, which includes: establishing an equivalent circuit model for a power supply system with a low-frequency resonance suppression device joined in; obtaining, according to a target low-frequency harmonic frequency band and the equivalent circuit model, a constraint required by the power supply system for suppressing low-frequency harmonics; constructing an objective function based on a low-frequency harmonic suppression rate in a bus of the power supply system; obtaining a multi-constraint objective optimization function of CLC branch impedance based on the constraint and the objective function; and solving the multi-constraint objective optimization function through an improved harmony search algorithm to obtain an impedance parameter of the CLC branch. The present disclosure can ensure a low-frequency harmonic suppression effect for the power supply system.

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.

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.

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.

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.