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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
Fault diagnosis is a very important task in modern industrial fields. With the continuous development of technology, people have begun to explore the use of machine learning algorithms to achieve automated fault diagnosis. Among them, methods based on neural networks have achieved great success in the field of fault diagnosis. This article will introduce an Adaboost fault diagnosis algorithm process based on the Golden Jackal algorithm optimized long short-term memory neural network (GJO-LSTM).
First, we need to understand the basic concepts of fault diagnosis. Fault diagnosis refers to analyzing and judging the operating status of the system to determine the type and location of faults in the system. In industrial production, fault diagnosis can help us discover and solve equipment faults in time, improving production efficiency and product quality.
Neural network is a computational model that simulates the working principle of neurons in the human brain. Long short-term memory neural network (LSTM) is a special neural network structure that can effectively process sequence data and has a memory function. In fault diagnosis, LSTM can predict possible future faults by learning the patterns and laws of historical data.
However, there are some problems with the traditional LSTM model. First, the training process of the LSTM model requires a large amount of computing resources and time. Secondly, the LSTM model is prone to gradient disappearance or gradient explosion problems when dealing with long-term dependencies. In order to solve these problems, we introduce the Golden Jackal algorithm for optimization.
The golden jackal algorithm is an optimization algorithm based on the idea of biological evolution, which simulates the hunting behavior of a pack of jackals. By simulating the foraging behavior and information transmission process of a group of jackals, the golden jackal algorithm can effectively search for the optimal solution. In this article, we apply the Golden Jackal algorithm to the training process of the LSTM model to improve the training efficiency and accuracy of the model.
Next, we introduce the specific steps of the GJO-LSTM-Adaboost fault diagnosis algorithm process. First, we use the Golden Jackal algorithm to optimize the initial parameters of the LSTM model. Then, we use the optimized LSTM model to train historical fault data to obtain an initial fault diagnosis model.
Next, we use the Adaboost algorithm to optimize the initial fault diagnosis model. Adaboost is an ensemble learning algorithm that builds a stronger classifier by combining multiple weak classifiers. In this article, we apply the Adaboost algorithm to the training process of the fault diagnosis model to further improve the accuracy and robustness of the model.
Finally, we use the optimized GJO-LSTM-Adaboost model to diagnose unknown fault data. By inputting unknown fault data into the model, we can get the corresponding fault type and location. In this way, we can take appropriate measures in time to solve the fault and avoid production interruption and loss.
To sum up, this article introduces an Adaboost fault diagnosis algorithm process based on the golden jackal algorithm to optimize the long short-term memory neural network. This algorithm can predict possible future failures by learning patterns and regularities of historical data, and improve the accuracy and robustness of the model through integrated learning algorithms. By applying this algorithm, we can achieve automated fault diagnosis and improve the efficiency and quality of industrial production.
Part of the code
%% Clear environment variables
warning off % Turn off alarm information
close all % Close the open figure window
clear % clear variables
clc % clears the command line
%% Import Data
res = xlsread('dataset.xlsx');
%% Divide training set and test set
temp = randperm(357);
P_train = res(temp(1: 240), 1: 12)';
T_train = res(temp(1: 240), 13)';
M = size(P_train, 2);
P_test = res(temp(241: end), 1: 12)';
T_test = res(temp(241: end), 13)';
N = size(P_test, 2);
%% Data normalization
[p_train, ps_input] = mapminmax(P_train, 0, 1);
p_test = mapminmax('apply', P_test, ps_input);
t_train = ind2vec(T_train);
t_test = ind2vec(T_test );
Running results
References
[1] Li Da, Zhang Zhaosheng, Liu Peng, et al. Multi-weather vehicle classification method based on improved long short-term memory neural network-adaptive enhancement algorithm [J]. Automotive Engineering, 2020, 42(9):8.DOI:10.19562 /j.chinasae.qcgc.2020.09.015.
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1 Improvement and application 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 application
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
Kalman filter tracking, track correlation, track fusion
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