## 5 Intelligent Fault Diagnosis of Sensor Faults
### Deep-Learning-Based Fault Diagnosis Method
#### 5.3.3 Recurrent Neural Network (RNN)  
An RNN is a type of neural network that processes sequence data. Its most distinguishing characteristic is that the output of neurons is transmitted to the input of itself or other neurons. Its properties can connect the nodes between hidden layers, and the input of the hidden layer incorporates the input of the input layer and the output of the hidden layer at the time of its creation. This concatenated neural network structure is appropriate for time series data and can preserve data dependencies [130]. Later, [PERSON] et al. [131] proposed a Long Short-Term Memory (LSTM) to improve the traditional RNN model to address the issue that RNNs are not good at long-term memory. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Diagnostic** & **Diagnostic** & **Application and Improvement** & **Applicable** & **Applicable** & **Apult Type** & **References** \\ \hline \multirow{4}{*}{Model-Based} & \multirow{2}{*}{-} & \multirow{2}{*}{Distributed} & Adaptive thresholds to ensure & \multirow{2}{*}{Temperature, \(\text{CO}_{2}\)} & \multirow{2}{*}{-} & [PERSON] et al. (2015) [79] \\  & & & robustness to noise and modeling & & & CO\({}_{2}\) & \\ \cline{1-1}  & & & & & Verification of the reliability of PCA & & \\ \cline{1-1}  & & & & for fault diagnosis of air conditioners & & & \\ \cline{1-1}  & & & sensors & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Development and application of intelligent sensor fault diagnosis approach. \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 State estimation & - & Fault diagnosis of battery sensor based on PLS and UKF & Voltage, current & - & [PERSON] et al. (2021) [81] \\ \hline TSA & Centralized & Detection of sensor faults of agricultural robot navigation system based on time series theory & Positioning & Drift fault & [PERSON] et al. (2015) [82] \\ \hline NMF & Distributed & Application of NMF to fault diagnosis of soil moisture sensors & Humidity & Soft fault & [PERSON] et al. (2014) [14] \\ \hline PCA & - & Introducing NN as classifiers into PCA fault diagnosis models & Temperature, air volume & Soft fault & [PERSON] et al. (2020) [85] \\ \hline Expert system & Distributed & Fuzzy rule fault node classification and management scheme to save & - & Hard fault & [PERSON] et al. (2016) [90] \\ \cline{2-6} Artificial Intelligence-Based & - & The three-input FIS sensor hardware fault diagnosis model improves the accuracy & - & Hard fault & [PERSON] et al. (2017) [91] \\ \hline \multirow{4}{*}{SVM} & \multirow{4}{*}{distributed} & Developed a multi-SVM model for sensor fault diagnosis & Dissolved oxygen & Hard fault & [PERSON] et al. (2017) [95] \\ \cline{2-6}  & & The OS-LSSVM sensor fault detection method addresses the lack of sparsity in SVM & Gyroscope & Bias fault & [PERSON] et al. (2017) [96] \\ \cline{2-6}  & & An LS-SVM model is proposed, which reduces the computational complexity & Temperature & Hard fault & [PERSON] et al. (2019) [97] \\ \hline \multirow{4}{*}{ANNs} & \multirow{4}{*}{distributed} & A BP model is built with recent local data, which can be used for online detection & Temperature & - & [PERSON] et al. (2014) [103] \\ \cline{2-6}  & & Introducing wavelet denoising into BPNN to improve the fault diagnosis rate & Temperature & Hard fault & [PERSON] et al. (2016) [104] \\ \cline{2-6}  & & fault diagnosis of HVAC sensors based on AANN & Water temperature & Drift and bias fault & [PERSON] et al. (2020) [105] \\ \hline \multirow{4}{*}{AE} & \multirow{4}{*}{distributed} & A three-layer network AE fault diagnosis system is designed, which can diagnose faults at the sensor & Temperature humidity & Spike and bias fault & [PERSON] et al. (2018) [110] \\ \cline{2-6}  & & A normalized sparse AE model is proposed to solve the variant feature problem of self-extracted features & - & [PERSON] et al.