## 5 Intelligent Fault Diagnosis of Sensor Faults
### Deep-Learning-Based Fault Diagnosis Method
#### 5.3.4 Convolutional Neural Networks (CNN)  
CNNs are a significant subfield of deep learning. A CNN has an input layer, a convolution layer, a pooling layer, a full connection layer, and an output layer. It is capable of auto-extracting features and is both robust and generalizable. CNN is currently used primarily for feature extraction of two-dimensional and three-dimensional image sequences [136]. Still, because CNN has the characteristics of lattice and convolution operations and is suitable for multisensor data processing, some researchers have introduced CNN to the field of fault diagnosis [137]. [PERSON] et al. [138] proposed a CNN fault diagnosis method to diagnose multisensor faults at the same calculation cost to reduce model training time and cost. [PERSON] et al. [139] proposed a CNN-RF sensor fault diagnosis method, which converted the original sensor signal into a grey matrix image, and the CNN is used to automatically extract the features of the gray matrix image, reducing the loss of effective information and improving the accuracy [PERSON] et al. [140; 141] proposed an attention-based deep convolutional neural network (DCNN) architecture to predict the RUL of turbofan engines. They used multivariate time information to extract features, which significantly improved the prediction performance of the model. [PERSON] et al. [142] replaced the conventionally used fully connected layers in traditional CNN with a multi-layer pool classifier, thereby reducing the number of network parameters and the risk of overfitting. The method performed a fault diagnosis on the acceleration sensor, with high fault-diagnosis accuracy and low computing resource consumption. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Diagnostic Method** & **Diagnostic Strategy** & **Application and Improvement** & **Applicable Sensor** & **Applicable Fault Type** & **References** \\ \hline \multirow{6}{*}{CNN} & \multirow{3}{*}{distributed} & The proposed CNN method enables & \multirow{3}{*}{-} & \multirow{3}{*}{Soft fault} & [PERSON] et al. (2020) [129] \\  & & fault diagnosis of multiple sensors & & & \\  & & with the same computational cost & & & \\ \cline{1-1} \cline{2-6}  & \multirow{3}{*}{-} & A CNN-RF method is proposed to & \multirow{3}{*}{Hydrogen} & \multirow{3}{*}{Soft fault} & [PERSON] et al. (2020) [130] \\  & & improve the diagnostic success rate & & & \\ \cline{1-1}  & & and reduce information loss in noisy environments & & & \\ \cline{1-1} \cline{2-6}  & \multirow{3}{*}{distributed} & Multi-layer pooling classifiers replace & \multirow{3}{*}{Acceleration} & \multirow{3}{*}{-} & [PERSON] et al. (2022) [131] \\ \cline{1-1}  & & the fully connected layers of CNN, & & & \\ \cline{1-1}  & & reducing the parameters and the risk of overfitting & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._CNN's ability to process two-dimensional and three-dimensional image data is its primary benefit. [PERSON] et al. [141] proposed a data-driven predictive model based on deep neural networks (DNNs), using sliding time window technology to prepare data, which does not require prior knowledge of prediction or signal processing. Currently, research in fault diagnosis focuses primarily on machinery, particularly rotating machinery, while research on sensor fault diagnosis is limited. Some researchers attempt to convert one-dimensional sensor data into a two-dimensional form, but this may destroy the spatial correlation of the original data and result in the loss of some error-related data. CNN is also suitable for processing large sample sizes but has a problem with overfitting small sample fault data. The aforementioned methods for fault diagnosis are highly accurate, but each has advantages and disadvantages. The model-based method for fault diagnosis is highly interpretable. It does not require a large amount of data, but the model is complex and highly professional, which is unsuitable for daily agricultural applications.