Gradio

AUV Fault Predictor

This demo uses a Random Forest model to classify one of five AUV fault states from 16 features.

Team Members: Jasmine Morales, Joseph Ladino, Sarah Eisenstein, Destiny Moua.

Fault Type Probabilities

Example Selector

pwm1_mean (microseconds [100Hz])

1179.032 1204.603

depth_min (meters)

-0.064 0.104

press_mean (pascals)

641.503 669.019

press_min (pascals)

596.5 614.5

voltage_std (volts)

0.02 0.026

roll_mean (degrees)

-2.438 4.986

roll_max (degrees)

5.543 6.947

pitch_mean (degrees)

-2.92 2.885

pitch_min (degrees)

-12.258 -1.002

a_x_std (meters/second^2)

4.634 7.767

a_y_std (meters/second^2)

2.333 3.606

a_z_std (meters/second^2)

3.323 5.811

w_row_mean (degrees/second)

-1.08 1.281

w_pitch_std (degrees/second)

3.06 9.421

w_yaw_mean (degrees/second)

-24.387 4.699

w_yaw_max (degrees/second)

-2.87 30.37