Abstract: Some embodiments of the disclosure provide a method for homogeneously controlling a transverse temperature during laminar cooling of a hot-rolled strip. In an embodiment, a mathematical model of middle convexity cooling in a water volume is established by designing different types of middle convexity water cooling heat transfer coefficient curves. Process procedures and equipment parameters of the hot-rolled strip during the laminar cooling are considered to restore the actual situation on site. Through finite element calculation, an optimal middle convexity water cooling heat transfer coefficient curve is obtained. Process parameters corresponding to middle convexity water volume distribution during the laminar cooling (a water flow density) are further obtained to guide a water volume control process.

Abstract: The present disclosure provides a strip flatness prediction method considering lateral spread during rolling. The method includes: step 1: acquiring strip parameters, roll parameters and rolling process parameters; step 2: introducing a change factor of a lateral thickness difference before and after rolling and a lateral spread factor by considering lateral metal flow, and constructing a strip flatness prediction model based on the coupling of flatness, crown and lateral spread; step 3: constructing a three-dimensional (3D) finite element model (FEM) of a rolling mill and a strip, simulating strip rolling by the 3D FEM, extracting lateral displacement and thickness data of the strip during a stable rolling stage, calculating parameters of the strip flatness prediction model based on the coupling of flatness, crown and lateral spread; and step 4: predicting the flatness of the strip by the strip flatness prediction model based on the coupling of flatness, crown and lateral spread.

Abstract: Provided is a fault signal locating and identifying method of industrial equipment based on a microphone array. The method includes the steps of: acquiring sound signals and dividing the acquired signals into a training set, a verifying set and a test set; performing feature extraction on the sound signals in the training set, and extracting a phase spectrogram and an amplitude spectrogram of a spectrogram; sending an output of a feature extraction module, as an input, to a CNN, and in each layer of the CNN, learning a translation invariance in the spectrogram by using a 2D CNN; in between the layers of the CNN, normalizing the output by using a batch normalization, and reducing a dimension by using a maximum pooling layer along a frequency axis; sending an output from the layers of the CNN to layers of RNN; using a linear activation function; and inputting an output of a full connection layer to two parallel full connection layer branches for fault identification and fault location, respectively.

Abstract: Provided is an LSTM-based hot-rolling roll-bending force predicting method including the steps of acquiring final rolling data of a stand of a stainless steel rolling mill when performing a hot rolling process, and dividing the data into a training set traindata and a test set testdata; normalizing the traindata; building a matrix P; using a last row of the matrix P as a label of the training set, namely a true value; calculating and updating an output value and the true value of a network; after network training is completed, taking the last m output data of the LSTM network as an input at a next moment, and then obtaining an output of the network at the next moment, wherein the output is a predicted value of the roll-bending force at the next moment; repeating the steps until a sufficient number of prediction data is obtained; and comparing the processed data with the true value in the testdata to check the validity of the network.

Abstract: The present disclosure provides a strip flatness prediction method considering lateral spread during rolling. The method includes: step 1: acquiring strip parameters, roll parameters and rolling process parameters; step 2: introducing a change factor of a lateral thickness difference before and after rolling and a lateral spread factor by considering lateral metal flow, and constructing a strip flatness prediction model based on the coupling of flatness, crown and lateral spread; step 3: constructing a three-dimensional (3D) finite element model (FEM) of a rolling mill and a strip, simulating strip rolling by the 3D FEM, extracting lateral displacement and thickness data of the strip during a stable rolling stage, calculating parameters of the strip flatness prediction model based on the coupling of flatness, crown and lateral spread; and step 4: predicting the flatness of the strip by the strip flatness prediction model based on the coupling of flatness, crown and lateral spread.