Autonomous underwater vehicle fault diagnosis dataset

The dataset contains 1225 data samples for 5 fault types (labels). We divided the dataset into the training set and the test set through random stratified sampling. The test set accounted for 20% of the total dataset. Our experimental subject is ‘Haizhe’, which is a small quadrotor AUV developed in the laboratory. For each fault type, ‘Haizhe’ was tested several times. For each time, ‘Haizhe’ ran the same program and sailed underwater for 10–20 s to ensure that state data was long enough. The state data recorded in each test were then used as a data sample, and the corresponding fault type was the true label of the data sample. The dataset was used to validate a model-free fault diagnosis method proposed in our paper [1] and the complete dynamic model of ‘Haizhe’ AUV was reported in [2].


Specifications
Ocean and Maritime Engineering Specific subject area Fault Diagnosis of Autonomous Underwater Vehicles Type of data

Value of the Data
• Our paper proposed a diagnosis model for AUV, which could learn the potential pattern between state data and fault type from the dataset. We hope that more researchers will pay attention to our approach and propose a better diagnosis model. The submitted dataset is recorded in the experiment, which can be used as a standard dataset to verify the performance of the diagnosis model. Although, the submitted dataset is not big enough, we will gradually collect more samples. • Those who research on fault diagnosis of autonomous underwater vehicle or want to analyze the correlation between state data and fault type can benefit from this dataset. • The submitted dataset includes training set and test set. Researchers can train their diagnosis model from training set and use the test set to validate the performance of the trained model. In addition, researchers can also use statistical knowledge or machine learning for data mining on this dataset.

Data Description
In the submitted dataset, the folder (named "Dataset") contains two folders (one is named "train", another is named "test"). "train" means training dataset, while "test" means test dataset.
Each fault type folder contains data samples. And each sample is a.csv file, which records state data of 'Haizhe' over a certain period of time. The name of fault type folder represents the true label of the sample. There are 17 columns in a.csv file. The name and description of each column is listed as below: • time: The absolute time for 'Haizhe' to record data.
• pwm1: Duration (in microseconds) of high level in 100 Hz PWM wave. It is the control signal used to control the Motor 1. • press: The pressure value (in Pa) measured by depth sensor.
• voltage: The voltage value (in V) of battery.
• roll: The roll angles (in degrees) measured by nine-axis IMU.
• pitch: The pitch angles (in degrees) measured by nine-axis IMU.
• yaw: The yaw angles (in degrees) measured by nine-axis IMU.
• a_x: The acceleration (in m/s 2 ) along the x-axis in the body coordinate frame, measured by nine-axis IMU. • a_y: The acceleration (in m/s 2 ) along the y-axis in the body coordinate frame, measured by nine-axis IMU. • a_z: The acceleration (in m/s 2 ) along the z-axis in the body coordinate frame, measured by nine-axis IMU. • w_row: The angular velocity (in degrees/s) of rotation around the x-axis in the body coordinate frame, measured by nine-axis IMU. • w_pitch: The angular velocity (in degrees/s) of rotation around the y-axis in the body coordinate frame, measured by nine-axis IMU. • w_yaw: The angular velocity (in degrees/s) of rotation around the z-axis in the body coordinate frame, measured by nine-axis IMU.
The models of the vehicle are developed in another work which may be published in the journal of Ocean Engineering. The paper's title is "Dynamic Modeling of Quadrotor AUV Using a Novel CFD Simulation".
Note : It is kindly remind that any paper that uses the Data should cite [2] and the above paper if it is published.

Experimental Design, Materials and Methods
We used 'Haizhe' AUV as the experimental subject. Fig. 1 is the assembly diagram. Fig. 2 is the actual prototype. 'Haizhe' is installed with 4 brushless motors (SUNNYSKY A2212 KV980 II), 4 propellers (Three-bladed Propeller Outer Diameter: 55 mm,Thread Pitch: 80mm), 4 electronic speed control (HOBBYWING Skywalker 20A), 1 depth sensor (MS5803-01 BA), 1 nine-axis inertial    Figure 3 shows a complete data collection test of the 'Haizhe' AUV. Firstly, the fault type was set and recorded for the 'Haizhe'. Then, the initializer was executed to check whether each component could work properly or not. After that, 'Haizhe' executed the main program and turned on the function of data recording. And then 'Haizhe' began sailing underwater. It is worth noting that different behavioral responses would be generated under the influence of different fault types , but the main program did not change. After completing the main program, 'Haizhe' would stop data recording and automatically rose to the surface. Finally, the file system of 'Haizhe' would save the state data as a text file (also called a data sample).
We set five common fault types for 'Haizhe', including normal state, slight damage to the propeller, severe damage to the propeller, failure of the depth sensor, and load increase. For each fault type , 'Haizhe' was tested several times. For each time, 'Haizhe' ran the same program and sailed underwater for 10-20 s to ensure that state data was long enough. The state data recorded in each test were then used as a data sample, and the corresponding fault type was the true label of the data sample.
In the experiment, 'Haizhe' had only one fault type at the same time, and there was no multifailure concurrency. The fault of depth sensor was not the hardware damage but artificially added a bias item when reading the pressure value, which made the calculated depth value deviate from the true value. For example, when the true depth was 0.5 m, the depth calculated by the sensor was 0.6 m.

Ethics Statement
We declare that the manuscript adheres to Ethics in publishing standards and the submitted dataset is the real data recorded in the experiment, and there is no act of stealing other people's data or modifying data.

Declaration of Competing Interest
We declare that we have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.