**5. Conclusions and future works**

The main objective of this work was to develop an intelligent AUV rescue system (AIRS) for detecting failure to minimize loss of an autonomous underwater vehicle (AUV). We combined three main ideas to construct the AIRS, including sensors algorithm, classifier conducting, and airbag system. Complex data acquisition was done by the 10-DOF sensor module with sensor calibration and extended Kalman filter (SCEKF), where Euler orientation fused with gravity and magnetic field are state variables, can benefit to get the precise attitude from the AUV. After SCEKF processed, we extracted the features of these signals from the 10-DOF sensor module and selected these features by principal component analysis (PCA) method. The results were incorporated with feature classifier, short-term classifier, and long-term classifier in order to recognize 10 types of AUV conditions. According to the experimental test in Section 3, we have shown that the 20 experimental data sets are categorized into "malfunction" or "functional condition" categories. The outcomes of the proposed classification with features extracted, whose failure detection accuracy is 97%, were more accurate than those of the ANN without features extracted. These results confirmed that the technology of the AIRS was feasible and that the proposed methods were accurate. Furthermore, we designed an inflatable mechanism, which fills CO2 in the airbags to generate buoyancy for AUV during failure detection.

The attitude estimation and classification applied in the underwater environment are a new field. Considering future work, we will try to extend more condition types of AUV for classifying more complex situations and accomplish the airbag system for setting on the AUV. We can have the ability to construct a variety of different models for the AUV's fault simulation, such as underwater turbulence or underwater creatures' interference. Providing a more comprehensive AIRS, to not have to do the experiment in the real underwater environment, also can improve the performance and convenience of installation to the AIRS. With this technology, we can install the appropriate number of gas cylinders and airbags in accordance with the different displacements of AUV to avoid the loss of AUV; this can even be used in rescuing vessels to reduce shipwreck in the future, thereby minimizing loss of life and property. This study will have outstanding contributions for the next generation of underwater vehicles. We are looking forward to the application of the SIRS being used widely in the future.
