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Content introduction
Fault diagnosis has always been an important issue in many industries, especially in manufacturing and engineering fields. With the continuous advancement of technology, people’s demand for predicting and diagnosing equipment faults in advance is becoming more and more urgent. In the past few years, deep learning technology has made significant progress in the field of fault prediction. This article will introduce a fault prediction algorithm process based on convolutional neural network-bidirectional gated recurrent unit (CNN-BIGRU).
The goal of the fault prediction algorithm is to predict possible equipment failures in advance by analyzing the equipment’s operating data, and take corresponding maintenance measures to avoid equipment shutdowns and production interruptions. Traditional fault prediction methods usually rely on manual feature extraction and traditional machine learning algorithms. This method has problems such as difficulty in feature extraction and limited feature representation capabilities. The emergence of deep learning technology provides a new solution for fault prediction.
Convolutional neural network (CNN) is a deep learning model that has achieved great success in the field of image processing. It extracts the spatial features of input data through convolutional layers and pooling layers, and performs classification or regression through fully connected layers. In fault prediction, we can regard the operating data of the equipment as one-dimensional time series data, use it as the input of CNN, and use CNN to extract the spatiotemporal characteristics of the data.
Bidirectionally gated recurrent unit (BIGRU) is a recurrent neural network (RNN) model capable of processing sequence data. Compared with traditional RNN models, BIGRU can consider both past and future information simultaneously, thereby better capturing long-term dependencies in sequence data. In fault prediction, we can regard the operating data of the equipment as a time series, use it as the input of BIGRU, and use BIGRU to learn the time dependence of the data.
The fault prediction algorithm process based on CNN-BIGRU can be divided into the following steps:
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Data preparation: Collect equipment operating data and preprocess the data, including data cleaning, data smoothing, etc.
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Feature extraction: Convert the preprocessed data into the input format of the convolutional neural network. The sliding window method can be used to divide time series data into multiple subsequences, and convert each subsequence into an input in the form of an image.
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Model training: Build a CNN-BIGRU model and use training data to train the model. The cross-entropy loss function and gradient descent algorithm can be used to optimize the model.
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Fault prediction: Use the trained model to predict new equipment operating data. Different thresholds can be set based on the prediction results to determine whether the equipment is faulty.
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Effect evaluation: Evaluate the accuracy and reliability of the fault prediction algorithm by comparing it with actual fault conditions. Indicators such as confusion matrix, precision rate, and recall rate can be used for evaluation.
Through the above steps, the fault prediction algorithm based on CNN-BIGRU can effectively predict equipment failures and provide timely maintenance measures to avoid equipment downtime and production interruption. This algorithm has broad application prospects in manufacturing and engineering fields.
In summary, fault diagnosis is a critical issue that is of great significance to many industries. The fault prediction algorithm flow based on convolutional neural network-bidirectional gated cyclic unit provides a new solution that can more accurately predict equipment faults and take corresponding maintenance measures. With the continuous development of deep learning technology, we believe that fault prediction algorithms will be more widely used in the future.
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化</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
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