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目录
0 引言
1 卷积神经网络故障诊断模型
2 VGG13卷积神经网络故障诊断模型
3 VGG13+SE注意力机制的故障诊断模型
4 模型训练
5 模型使用
6 结语
0 引言
本博客基于tensorfolow2.6.1与【上一篇博客提取的时频图数据】构建2D卷积神经网络做故障诊断（10分类，9类故障+1正常），并采用普通的2D卷积网络→VGG卷积网络→VGG+SE注意力的逐步递进的改进网络方式，教大家水文章。
1 卷积神经网络故障诊断模型
轴承故障诊断就是多分类，我们还利用二维卷积搭建一个10分类的模型，比如可以用LeNet这样的结构来做，其含输入层→2个卷积2个池化→1个全连接层→分类层，数据处理与网络构建代码如下：
# -*- coding: utf-8 -*-
from tensorflow.keras.layers import Conv1D,Conv2D, Dense, \MaxPool1D,MaxPool2D, concatenate, Flatten,Reshape,Layer,\GlobalAveragePooling2D,Activation,multiply,BatchNormalization,Dropout
from tensorflow.keras import Input, Model
import tensorflow.keras.backend as K
import tensorflow as tf
import numpy as np
import os
from PIL import Image# In[1] 定义数据提取函数
def read_directory(directory_name,height,width,normal):'''data: n*height*width*3label:n*1'''file_list=os.listdir(directory_name)file_list.sort(key=lambda x: int(x.split('_')[0]))img = []label0=[]for each_file in file_list:img0 = Image.open(directory_name + '/'+each_file)gray = img0.resize((height,width))img.append(np.array(gray).astype(np.float))label0.append(float(each_file.split('.')[0].split('_')[-1]))if normal:data = np.array(img)/255.0#归一化else:data = np.array(img) label=np.array(label0)return data,label
# In[2] 定义LeNet10分类网络
def LeNet(k1_num,k1_size,k2_num,k2_size,fc1):input1_= Input(shape=(64, 64,3), name='input1')# LeNet构建的网络结构是 输入层-2d卷积-池化-2d卷积-池化-全连接1-全连接2-输出层x1 = Conv2D(k1_num, kernel_size=k1_size, strides=1, activation='relu', padding='same')(input1_)#b*64*64*k1_numx1 = MaxPool2D(pool_size=2, strides=2)(x1)#b*32*32*k1_numx1 = Conv2D(k2_num, kernel_size=k2_size, strides=1, activation='relu', padding='same')(x1)#b*32*32*k2_numx1 = MaxPool2D(pool_size=2, strides=2)(x1)#b*16*16*k2_numx = Flatten()(x1)#b* (16*16*k2_num)# x = Dropout(0.5)(x)x = Dense(fc1, activation='sigmoid', name='fc1')(x) #b*fc1# x = Dropout(0.5)(x)output_ = Dense(10, activation='softmax', name='output')(x)#b*10model = Model(inputs=input1_, outputs=output_)return model
注意，我们首先将时频图在输入之前resize到[64,64,3]，并除255归一化到[0,1]，再输入到网络，每个卷积层我们的padding都是same，因此不论怎么设置卷积核数量k_num与卷积核大小k_size，输入与输出的featuremap的size都是一样的。每个卷积之后接一个最大值池化做一次降采样，最后将池化层的输出Flatten成向量并交给全连接层，再接一个Dense做分类层即可完成，以64*64*3为输入，10输出的分类模型。
基于上述模型，首先利用训练集训练好模型并保存好，当系统检测到机械故障时，利用传感器采集待样本，提取待测样本的时频图输入到训练好的模型输出对应的类别，即可完成故障诊断。
2 VGG13卷积神经网络故障诊断模型
由于LeNet过于简单，直接这样写进论文不太好交差，因此我们稍微用复杂点的网络如GoogleNet、MobileNet、Resnet、VisionTransformer等结构构建故障诊断模型，我们采用VGG13举例，VGG13的结构如下：
两次卷积+1次最大值池化，重复5次，然后将第五次的输出Flatten成向量，经2个Dense进行降维，最后接一个节点数为10的Dense做分类，代码如下：
# -*- coding: utf-8 -*-
from tensorflow.keras.layers import Conv1D,Conv2D, Dense, \MaxPool1D,MaxPool2D, concatenate, Flatten,Reshape,Layer,\GlobalAveragePooling2D,Activation,multiply,BatchNormalization,Dropout
from tensorflow.keras import Input, Model
import tensorflow.keras.backend as K
import tensorflow as tf
import numpy as np
import os
from PIL import Image# In[1] 定义数据提取函数
def read_directory(directory_name,height,width,normal):'''data: n*height*width*3label:n*1'''file_list=os.listdir(directory_name)file_list.sort(key=lambda x: int(x.split('_')[0]))img = []label0=[]for each_file in file_list:img0 = Image.open(directory_name + '/'+each_file)gray = img0.resize((height,width))img.append(np.array(gray).astype(np.float))label0.append(float(each_file.split('.')[0].split('_')[-1]))if normal:data = np.array(img)/255.0#归一化else:data = np.array(img) label=np.array(label0)return data,label
def VGG13(k1_num,k1_size,k2_num,k2_size,k3_num,k3_size,k4_num,k4_size,k5_num,k5_size,fc1,fc2):input1_= Input(shape=(64, 64,3), name='input1')# VGG13构建的网络结构是 输入层-2d卷积*2-池化-2d卷积*2-池化-2d卷积*2-池化-2d卷积*2-池化-2d卷积*2-池化-全连接1-全连接2-输出层x1 = Conv2D(k1_num, kernel_size=k1_size, strides=1, activation='relu', padding='same')(input1_)#B*64*64*k1_numx1 = Conv2D(k1_num, kernel_size=k1_size, strides=1, activation='relu', padding='same')(x1)#B*64*64*k1_num#连续两个卷积的卷积核数量与大小是一致的x1 = MaxPool2D(pool_size=2, strides=2)(x1)#B*32*32*k1_numx1 = Conv2D(k2_num, kernel_size=k2_size, strides=1, activation='relu', padding='same')(x1)#B*32*32*k2_numx1 = Conv2D(k2_num, kernel_size=k2_size, strides=1, activation='relu', padding='same')(x1)#B*32*32*k2_numx1 = MaxPool2D(pool_size=2, strides=2)(x1)#B*16*16*k2_numx1 = Conv2D(k3_num, kernel_size=k3_size, strides=1, activation='relu', padding='same')(x1)#B*16*16*k3_numx1 = Conv2D(k3_num, kernel_size=k3_size, strides=1, activation='relu', padding='same')(x1)#B*16*16*k3_numx1 = MaxPool2D(pool_size=2, strides=2)(x1)#B*8*8*k3_numx1 = Conv2D(k4_num, kernel_size=k4_size, strides=1, activation='relu', padding='same')(x1)#B*8*8*k4_numx1 = Conv2D(k4_num, kernel_size=k4_size, strides=1, activation='relu', padding='same')(x1)#B*8*8*k4_numx1 = MaxPool2D(pool_size=2, strides=2)(x1)#B*4*4*k4_numx1 = Conv2D(k5_num, kernel_size=k5_size, strides=1, activation='relu', padding='same')(x1)#B*4*4*k5_numx1 = Conv2D(k5_num, kernel_size=k5_size, strides=1, activation='relu', padding='same')(x1)#B*4*4*k5_numx1 = MaxPool2D(pool_size=2, strides=2)(x1)#B*2*2*k5_numx = Flatten()(x1)#B* (2*2*k5_num)# x = Dropout(0.5)(x)x = Dense(fc1, activation='sigmoid', name='fc1')(x)#B*fc1# x = Dropout(0.5)(x)x = Dense(fc2, activation='sigmoid', name='fc2')(x)#B*fc2output_ = Dense(10, activation='softmax', name='output')(x)#B*10model = Model(inputs=input1_, outputs=output_)return model3 VGG13+SE注意力机制的故障诊断模型
直接用VGG13网络老师可能会说没得创新，同时也为了写论文的时候凑字数，因此需要再次加点内容灌水，常见的方式就是改网络中的模块，比如卷积改成可变型卷积，就说自己的诊断模型可以更好的提取故障特征；加各种注意力模型，比如SE、CBRM，就说自己的诊断模型具备可以聚焦数据中的重要特征；网络组合CNN+LSTM组合，就说自己的诊断模型还可以充分发掘数据中的时域特征。最后改个名字就叫Improved VGG Fault Diagnosis Model，简称IVFD-Net。这篇博客中我们给VGG13加入SE注意力模块，结构如下，代码如下：
代码如下：
# -*- coding: utf-8 -*-
from tensorflow.keras.layers import Conv1D,Conv2D, Dense, \MaxPool1D,MaxPool2D, concatenate, Flatten,Reshape,Layer,\GlobalAveragePooling2D,Activation,multiply,BatchNormalization,Dropout
from tensorflow.keras import Input, Model
import tensorflow.keras.backend as K
import tensorflow as tf
import numpy as np
import os
from PIL import Image# In[1] 定义数据提取函数
def read_directory(directory_name,height,width,normal):'''data: n*height*width*3label:n*1'''file_list=os.listdir(directory_name)file_list.sort(key=lambda x: int(x.split('_')[0]))img = []label0=[]for each_file in file_list:img0 = Image.open(directory_name + '/'+each_file)gray = img0.resize((height,width))img.append(np.array(gray).astype(np.float))label0.append(float(each_file.split('.')[0].split('_')[-1]))if normal:data = np.array(img)/255.0#归一化else:data = np.array(img) label=np.array(label0)return data,label class SE_AttentionLayer(Layer):def __init__(self, **kwargs):super(SE_AttentionLayer, self).__init__(**kwargs)def build(self, input_shape):assert len(input_shape) == 4self.W1 = self.add_weight(name='att_weight',shape=(input_shape[-1], input_shape[-1]//4),initializer='uniform',trainable=True)self.W2 = self.add_weight(name='att_weight',shape=(input_shape[-1]//4, input_shape[-1]),initializer='uniform',trainable=True)super(SE_AttentionLayer, self).build(input_shape)def call(self, inputs): channel = inputs.shape[-1]se_feature = GlobalAveragePooling2D()(inputs)se_feature = Reshape((1, 1, channel))(se_feature)se_feature = K.dot(se_feature, self.W1)se_feature = Activation('sigmoid')(se_feature)se_feature = K.dot(se_feature, self.W2)se_feature = Activation('sigmoid')(se_feature)# se_feature = Dense(channel // 4,# activation='relu',# use_bias=False)(se_feature)# se_feature = Dense(channel,# activation='sigmoid',# use_bias=False)(se_feature)outputs = multiply([inputs, se_feature])return outputsdef ProposedNet2D(k1_num,k1_size,k2_num,k2_size,k3_num,k3_size,k4_num,k4_size,k5_num,k5_size,fc1,fc2):input1_= Input(shape=(64, 64,3), name='input1')# VGG13构建的网络结构是 输入层-2d卷积*2-池化-2d卷积*2-池化-2d卷积*2-池化-2d卷积*2-池化-2d卷积*2-池化-全连接1-全连接2-输出层# VGG13+SE构建的网络结构是 输入层-2d卷积*2-池化-SE注意力-2d卷积*2-池化-SE注意力-2d卷积*2-池化-SE注意力-2d卷积*2-池化-SE注意力-2d卷积*2-池化-SE注意力-全连接1-全连接2-输出层x1 = Conv2D(k1_num, kernel_size=k1_size, strides=1, activation='relu', padding='same')(input1_)#B*64*64*k1_numx1 = Conv2D(k1_num, kernel_size=k1_size, strides=1, activation='relu', padding='same')(x1)#B*64*64*k1_num#连续两个卷积的卷积核数量与大小是一致的x1 = MaxPool2D(pool_size=2, strides=2)(x1)#B*32*32*k1_numx1 = SE_AttentionLayer()(x1) #B*32*32*k1_numx1 = Conv2D(k2_num, kernel_size=k2_size, strides=1, activation='relu', padding='same')(x1)#B*32*32*k2_numx1 = Conv2D(k2_num, kernel_size=k2_size, strides=1, activation='relu', padding='same')(x1)#B*32*32*k2_numx1 = MaxPool2D(pool_size=2, strides=2)(x1)#B*16*16*k2_numx1 = SE_AttentionLayer()(x1) #B*16*16*k2_numx1 = Conv2D(k3_num, kernel_size=k3_size, strides=1, activation='relu', padding='same')(x1)#B*16*16*k3_numx1 = Conv2D(k3_num, kernel_size=k3_size, strides=1, activation='relu', padding='same')(x1)#B*16*16*k3_numx1 = MaxPool2D(pool_size=2, strides=2)(x1)#B*8*8*k3_numx1 = SE_AttentionLayer()(x1) #B*8*8*k3_numx1 = Conv2D(k4_num, kernel_size=k4_size, strides=1, activation='relu', padding='same')(x1)#B*8*8*k4_numx1 = Conv2D(k4_num, kernel_size=k4_size, strides=1, activation='relu', padding='same')(x1)#B*8*8*k4_numx1 = MaxPool2D(pool_size=2, strides=2)(x1)#B*4*4*k4_numx1 = SE_AttentionLayer()(x1) #B*4*4*k4_numx1 = Conv2D(k5_num, kernel_size=k5_size, strides=1, activation='relu', padding='same')(x1)#B*4*4*k5_numx1 = Conv2D(k5_num, kernel_size=k5_size, strides=1, activation='relu', padding='same')(x1)#B*4*4*k5_numx1 = MaxPool2D(pool_size=2, strides=2)(x1)#B*2*2*k5_numx1 = SE_AttentionLayer()(x1) #B*2*2*k5_numx = Flatten()(x1)#B* (2*2*k5_num)# x = Dropout(0.5)(x)x = Dense(fc1, activation='sigmoid', name='fc1')(x)#B*fc1# x = Dropout(0.5)(x)x = Dense(fc2, activation='sigmoid', name='fc2')(x)#B*fc2output_ = Dense(10, activation='softmax', name='output')(x)#B*10model = Model(inputs=input1_, outputs=output_)return model注意SE注意力里面要对featuremap通道数//4，因此ki_num需要大于4。
4 模型训练
有了上一篇博客的数据与这一篇博客的模型，即可进行模型的训练，正式训练前需要创建一个model文件夹保存训练的模型，result文件夹保存损失曲线图与混淆矩阵图，完整代码如下：
# coding: utf-8
# In[1]: 导入必要的库函数
import os
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
import seaborn as sns
from model import ProposedNet2D,SE_AttentionLayer,read_directory
import tensorflow as tf
from tensorflow.keras.losses import SparseCategoricalCrossentropy
from tensorflow.keras.optimizers import Adam,SGD
import random
tf.random.set_seed(0)
np.random.seed(0)
random.seed(0)
# In[] 加载数据
height=64
width=64# 时频图---2D-CNN输入
x_train1,y_train=read_directory('image/train',height,width,normal=1)
x_valid1,y_valid=read_directory('image/test',height,width,normal=1)# In[] 搭建模型
# 参数设置，有点多 分别
# 是第1卷积层的卷积核数量k1_num、卷积核大小k1_size*k1_size
# 是第2卷积层的卷积核数量k2_num、卷积核大小k2_size*k2_size
# 是第3卷积层的卷积核数量k3_num、卷积核大小k3_size*k3_size
# 是第4卷积层的卷积核数量k4_num、卷积核大小k4_size*k4_size
# 是第5卷积层的卷积核数量k5_num、卷积核大小k4_size*k5_size
# 全连接层的节点数fc1,fc2
# 训练次数 epoches，batchsize,学习率 learningrate
# learningrate,epochs,batch_size,k1_num,k1_size,k2_num,k2_size,k3_num,k3_size,k4_num,k4_size,k5_num,k5_size,fc1,fc2=\# [0.0001, 1, 35, 4, 1, 19, 2, 21, 2, 16, 2, 16, 2, 149, 50]
k1_num,k1_size,k2_num,k2_size,k3_num,k3_size,k4_num,k4_size,k5_num,k5_size,fc1,fc2=4,3,8,3,16,3,32,3,64,3,40,20
epochs=100
batch_size = 64
learningrate=0.001model= ProposedNet2D(k1_num,k1_size,k2_num,k2_size,k3_num,k3_size,k4_num,k4_size,k5_num,k5_size,fc1,fc2)
model.summary()
checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath='model/best', monitor='val_accuracy', verbose=0, save_best_only=True, mode = 'max')model.compile(optimizer=Adam(learning_rate=learningrate),loss=SparseCategoricalCrossentropy(),metrics=['accuracy'])
# exit()
# In[] 训练
train_again = True # 为 False 的时候就直接加载训练好的模型进行测试 True就重新训练
# 训练模型
if train_again:history = model.fit(x_train1,y_train, epochs=epochs,validation_data=(x_valid1, y_valid),batch_size=batch_size, verbose=1,callbacks= [checkpoint])model=tf.keras.models.load_model('model/best',custom_objects = {'SE_AttentionLayer': SE_AttentionLayer})# 画loss曲线plt.figure()plt.ylabel('MSE')plt.xlabel('Epoch')plt.plot(history.history['loss'], label='training')# plt.plot(history.history['val_loss'], label='validation')plt.title('loss curve')plt.legend()plt.savefig('result/model2d_loss_curve.jpg')
else: # 加载模型model=tf.keras.models.load_model('model/best',custom_objects = {'SE_AttentionLayer': SE_AttentionLayer})test_pred = model.predict(x_valid1)
pred_labels = np.argmax(test_pred,1)
acc=np.sum(pred_labels==y_valid)/len(y_valid)
print('分类精度为:%f '%(acc*100),'%')
C1= confusion_matrix(y_valid, pred_labels)
print('混淆矩阵:')
print(C1)
xtick=[str(i) for i in range(10)]
ytick=[str(i) for i in range(10)]plt.figure()
# C2=C1/C1.sum(axis=1)
sns.heatmap(C1,fmt='g', cmap='Blues',annot=True,cbar=False,xticklabels=xtick, yticklabels=ytick)
plt.xlabel('Predict label')
plt.ylabel('True label')
plt.title('Confusion_matrix')
plt.savefig('result/Confusion_matrix_2d.jpg')
plt.show()
5 模型使用
这里就是加载训练好的模型做推理，没得什么好说的，用pyqt5做一个qt界面，可以更好的水文章，代码如下：
# coding: utf-8
# In[1]: 导入必要的库函数
import numpy as np
import tensorflow as tf
from model import SE_AttentionLayer
from PIL import Image# In[] 加载数据
#加载一个待测样本 我们就取测试集中的第0个 0_2.jpg 第0张图片是第2类
img0 = Image.open('image/test/0_2.jpg')
img0 = img0.resize((64,64))
img0=np.array(img0).astype(np.float)/255
x_test1=img0[np.newaxis,:,:,:]
# In[] 加载训练好的模型
model=tf.keras.models.load_model('model/best',custom_objects = {'SE_AttentionLayer': SE_AttentionLayer})
test_pred = model.predict(x_test1)
pred_label = np.argmax(test_pred,1)
print('该样本为第：',pred_label[0],'类')6 结语
通过第4节的网络训练，测试集的精度大约是84%，这个数据集是可以达到98%-100%的分类精度（数据集只用了一种频率（48KHz）一种负载（0HP）的轴承数据，因此这个数据很简单），之所以没有达到是因为我们在创建网络的时候，超参数是自己随便设计的，总共需要设定的参数有：learningrate,epochs,batch_size,k1_num,k1_size,k2_num,k2_size,k3_num,k3_size,k4_num,k4_size,k5_num,k5_size,fc1,fc2，要通过手动选择这些参数使网络效果达到最优，十分困难，这就引出了我们的下一篇博客【基于蛇算法的VGG13SE网络超参数优化】。
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