Autonomous underwater vehicle fault diagnosis dataset.

ABSTRACT: 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

SUBMITTER: Ji D 

PROVIDER: S-EPMC8529076 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature