Patent ID: 11927937
Assignee: INSTITUTE OF INDUSTRIAL INTERNET, CHONQING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS
Field: Control (Instruments)
Classification: CPC G | IPC G

Claim 0:
1. A prediction method for tool remaining life of a numerical control machine tool based on a hybrid neural model, comprising: constructing a hybrid neural network model, the model comprising a convolutional neural network, a long short-term memory network and an NFM neural network, the prediction of the tool remaining life of the numerical control machine tool specifically comprising the following steps:
S1: constructing samples of PLC working condition signal data and vibration and current signals in a same time period according to the sampling frequency of tool data, and generating sample data based on low frequency PLC sampling points;
S2: learning the sample data by utilizing a sub-model combined by the convolutional neural network and the long short-term memory network to obtain a first feature vector representing the tool life, specifically comprising the following steps:
S21: acquiring an average value of vibration data collected by a sensor by utilizing a triangle window, extracting a variance by a rectangle window to detect abnormal values, filling the average value into abnormal points, performing moving average sequence filtering on the collected current signals, and standardizing the overall data,
S22: building a convolutional neural network layer, performing convolution operation on an input current signal and a vibration signal sequence through K convolution kernels of the convolution layer to obtain K feature vectors, and pooling each feature vector after convolution layer through a maximum pooling layer to obtain a time series containing K local features,
S23: building a single-layer long short-term memory layer, and obtaining a time series containing historical time information through an LSTM layer containing N LSTM neural units, and
S24: acquiring N eigenvalues of LSTM neurons by separately utilizing a Global Average Pooling layer and a Global Max Pooling layer, and taking 2*N eigenvalues as the first feature vector representing the tool life;
S3: generating a learnable vector table of I*M dimension after hashing a working condition signal of each sampling point into an index value by utilizing the NFM neural network, I being an index number and M being an embedding dimension, and acquiring a second feature vector representing the tool life through Bi-Interaction Pooling layer learning; and
S4: inputting a current working time of a tool, the first feature vector representing the tool life and the second feature vector representing the tool life into a multi-layer perceptron for fusion, and then learning a whole network structure.