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Intelligent optimization algorithm Neural network prediction Radar communication Wireless sensor Power system
Signal processing Image processing Path planning Cellular automaton Drone
Content introduction
With the continuous development of science and technology, people’s requirements for machines are getting higher and higher. In modern industrial fields, machine fault diagnosis is a very important issue. For some large-scale mechanical equipment, once a failure occurs, it will not only affect production efficiency, but also cause huge economic losses. Therefore, how to diagnose machine faults quickly and accurately has become a common concern in industry and academia.
In terms of machine fault diagnosis, neural network algorithm is a very effective method. Among them, BP neural network is the most commonly used neural network algorithm. BP neural network has the advantages of strong adaptability and strong nonlinear mapping ability, which can improve the accuracy of fault diagnosis to a certain extent.
However, BP neural network also has some problems. First, BP neural network requires a large amount of training data to achieve high diagnostic accuracy. Secondly, BP neural network is prone to over-fitting problems, leading to inaccurate diagnosis results. Therefore, how to optimize the BP neural network and improve its fault diagnosis accuracy is an urgent problem that needs to be solved.
Recently, a new optimization algorithm, the Sparrow algorithm, has been introduced into the BP neural network. The sparrow algorithm is an optimization algorithm based on the behavior of bird groups. It simulates the behavior of bird groups in the process of foraging and breeding. It has the advantages of strong global optimization ability and fast convergence speed. By combining the Sparrow algorithm with the BP neural network, the fault diagnosis accuracy of the BP neural network can be effectively improved.
Specifically, the fault diagnosis algorithm process of Sparrow algorithm optimizing BP neural network is as follows:
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Collect training data and preprocess it. Before fault diagnosis, a certain amount of machine operating data needs to be collected and preprocessed, including data cleaning, feature extraction and other steps.
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Build a BP neural network model. Based on the preprocessed training data, a BP neural network model is constructed. When building a model, it is necessary to determine the structure, activation function, learning rate and other parameters of the neural network.
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Use Sparrow algorithm to optimize BP neural network. The BP neural network is optimized through the Sparrow algorithm, so that the weights and thresholds of the neural network can better adapt to the training data, thereby improving the accuracy of fault diagnosis.
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Perform troubleshooting. After completing the BP neural network optimization, fault diagnosis can be performed. By inputting machine operating data, the BP neural network can output corresponding fault diagnosis results.
In general, the Sparrow algorithm is a very effective method to optimize the BP neural network to implement the fault diagnosis algorithm process. By combining the Sparrow algorithm with the BP neural network, some problems existing in the BP neural network can be overcome and the accuracy of fault diagnosis can be improved. In the future, we believe that this method will be widely used in industry and academia to bring better solutions to machine fault diagnosis.
Part of the code
%% Clear environment variables</code><code>warning off % Close alarm information</code><code>close all % Close open figure window</code><code>clear % Clear variables</code><code>clc % clear command line</code><code>?</code><code>%% import data</code><code>res = xlsread('dataset.xlsx');</code><code>?</code><code>%% divide the training set and test set</code><code>temp = randperm(357);</code><code>?</code><code>P_train = res(temp(1: 240), 1: 12)';</code><code>T_train = res(temp(1: 240), 13)';</code><code>M = size(P_train , 2);</code><code>?</code><code>P_test = res(temp(241: end), 1: 12)';</code><code>T_test = res(temp(241 : end), 13)';</code><code>N = size(P_test, 2);</code><code>?</code><code>%% data normalization</code><code>[p_train, ps_input] = mapminmax(P_train, 0, 1);</code><code>p_test = mapminmax('apply', P_test, ps_input);</code><code>t_train = ind2vec(T_train) ;</code><code>t_test = ind2vec(T_test );
Operation results
References
[1] Wang Shuwei. Optimizing BP neural network to diagnose CNC machine tool faults based on Sparrow algorithm [J]. Journal of Shenyang University of Technology, 2023, 45(5):546-551.DOI:10.7688/j.issn.1000-1646.2023.05.12.
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1 Improvements and applications of various intelligent optimization algorithms
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics location selection, cargo space optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, Container ship stowage optimization, water pump combination optimization, medical resource allocation optimization, facility layout optimization, visible area base station and drone site selection optimization
2 Machine learning and deep learning
Convolutional neural network (CNN), LSTM, support vector machine (SVM), least squares support vector machine (LSSVM), extreme learning machine (ELM), kernel extreme learning machine (KELM), BP, RBF, width Learning, DBN, RF, RBF, DELM, XGBOOST, TCN realize wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health status prediction, water body Optical parameter inversion, NLOS signal identification, accurate subway parking prediction, transformer fault diagnosis
2. Image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image splicing, image fusion, image enhancement, image compressed sensing
3 Path planning
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), UAV three-dimensional path planning, UAV collaboration, UAV formation, robot path planning, raster map path planning , multimodal transportation problems, vehicle collaborative UAV path planning, antenna linear array distribution optimization, workshop layout optimization
4 UAV applications
UAV path planning, UAV control, UAV formation, UAV collaboration, UAV task allocation, and online optimization of UAV safe communication trajectories
5 Wireless sensor positioning and layout
Sensor deployment optimization, communication protocol optimization, routing optimization, target positioning optimization, Dv-Hop positioning optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI positioning optimization
6 Signal processing
Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, EMG signal, EEG signal, signal timing optimization
7 Power system aspects
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration
8 Cellular Automata
Traffic flow, crowd evacuation, virus spread, crystal growth
9 Radar aspect
Kalman filter tracking, track correlation, track fusion
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