# Online Learning Based Underwater Robotic Thruster Fault Detection
###### Abstract
However, the model parameters can change due to complicated underwater conditions and long-term operations [6; 16; 17; 18], which may result in unpredicted faults [19]. In order to address these issues, researchers have proposed a variety of solutions from several perspectives. For example, [PERSON] et al. [20] developed an independent component analysis method, together with wavelet packet decomposition and empirical mode decomposition, to analyze the effects of sea currents and vehicle thruster faults upon autonomous underwater vehicle (AUV) movement speed, which further determines whether the sources of faults are from the thruster itself or the sea current. A hidden Dirichlet distribution analysis model has been designed to detect the abnormality of the AUV vertical surface [19]. The AUV movement information and operation commands are coded into a string. The latent Dirichlet allocation (LDA) model is trained using the AUV data. Several topics are extracted from the historical data independently, in which there exists a topic with dominant probability at each moment. Afterwards, the operation performed by the AUV corresponds to the instantaneous topic (e.g., floating on the water, constant depth navigation, pitch navigation, and floating up). In practical applications, the state and operation instruction information of the AUV are first encoded into a state byte. Then, the topic most related to the current state in the LDA topic model generated by training is searched for, as the topic of the current moment, according to the nearest neighbor principle. This method no longer requires a systematic mathematical model, but the learning model is unsatisfactory when the training data are of a relatively small volume. For states that have never appeared in the training data, the LDA model recognizes them as the unknown states or as a combination of several known states that have appeared in the training data. It has been found that the ocean current shows more obvious impacts on the motion state of underwater vehicles in practical applications, while the impact on the state of the propeller itself is relatively smaller. As far as the fault detection of propulsion systems is concerned, methods based on propeller models might overcome the current influence, to a certain extent. In terms of modeling methods, propeller models include analytical and data-driven models. In analytical models, specific parameter information relating to the propulsion motor, transmission device, propeller, and so on, is required, which is generally impractical to obtain [6]. In addition, it is difficult to adjust the information online once the parameters of the analytical model have been determined. Therefore, it would be an ideal choice to build a propeller model based on data-driven methods. In [6], a propeller fault detection method based on an energy consumption model is proposed, using a data-driven strategy. First, the control variable (composed of a special excitation signal) is applied to the propeller in order to obtain the corresponding propeller current data. Then, a relationship model between the control voltage and the current of the thruster is built by using the LWPR. In some practical applications, the short-term energy consumption is calculated using both the current calculated by the model and the measured current. Then, the residual index of energy consumption is calculated, and a fault is detected when this exceeds the corresponding threshold. This method is able to adjust the model online by introducing regularization in the model; however, it is unable to optimize the amount of data used in online modeling. In practice, the change of an environmental or system state is periodic, and the optimal amount of modeling data often changes in different situations. Inappropriate modeling can introduce degraded system information, which is related to other stages in the model and, thus, may affect the accuracy of the model.