## 2 Related Works
### Fault Diagnosis of a Vehicle
This system is a process history based method; only normal operating conditions data are needed for training the system. The approach could tackle the problem of false alarms, due to the correlation among variables [39]. RBF neural network consensus-based distributed control scheme is proposed for non-holonomic autonomous vehicles in a pre-defined formation along the specified reference trajectory [40]. [PERSON] proposed a deep convolution neural network that is no less than nine layers. Compared with a traditional vehicle recognition based on machine learning, which requires vehicle location and has low accuracy of shortcomings, the proposed model using a deep convolution neural network displays better performance [41]. [PERSON] described an acoustic based fault diagnosis technique of the three-phase induction motor. Four states of the three-phase induction motor were analyzed: healthy three-phase induction motor, three-phase induction motor with broken rotor bar, three-phase induction motor with two broken rotor bars, and a three-phase induction motor with the faulty ring of a squirrel-cage. The proposed feature extraction methods: SMOFS-32-MULTIEXPANDED-2-GROUPS and SMOFS-32-MULTIEXPANDED-1-GROUP were analyzed. The Nearest Neighbor classifier, backpropagation neural network, and the modified classifier based on words coding were used for recognition. It can prevent unexpected failure and improve the maintenance of electric motors. Advantages of the acoustic based fault diagnosis technique are: non-invasive technique, low cost, and instant measurement of the acoustic signals [42]. [PERSON] reports on the feasibility of performing vibrational monitoring in real world conditions; i.e., by running the vehicle on the road and performing the analysis. The data were acquired for the various conditions of the gearbox and features were extracted from the time-domain data and a decision tree was trained for the time-domain analysis. Fast Fourier Transform was performed to obtain the frequency domain, which was divided into segments of equal size and the area covered by the data in each segment was calculated for every segment to train the decision trees. The classification efficiencies of the decision trees were obtained and in an attempt to improve the classification of efficiencies, the time-domain, and a frequency-domain analysis was also performed on the normalized time-domain data [43]. [PERSON] focuses on the problem of the prediction of surface roughness in AWJ process and contributes to the online monitoring of the hydro-abrasive material disintegration process and its possible control. The main scope of study is to contribute to the usage of an artificial neural network as a decisive part in surface roughness prediction and to outline a suitable online control mechanism [44]. [PERSON] considers the usage of artificial intelligence, in particular, neural networks, to correct and compensate for thermocouple errors. Proposed in this paper are the correction of the thermocouple tolerance, the error due to the conversion characteristic drift under the influence of high operating temperatures as well as the compensation of the error due to acquired thermoelectric inhomogeneity of thermocouple legs. The corrections are carried out using individual mathematical models based on neural networks. The neural network method for controlling a temperature field to compensate for the error due to the acquired thermoelectric in-homogeneity is proposed [45]. In this paper, input nodes and output nodes are generated flexibly by receiving signals from all sensors. The input node is the number of sensors, and the output node is the number of parts to be diagnosed. That is, all the parts that can be measured by the sensors can be diagnosed. The generated neural network model is simulated using a Deep Belief Network (DBN) [46], Long Short Term Memory (LSTM) [47], and the proposed ODLM, which are learning models used in real-time processing.