Fault detection is a key technology to test the practicality of electro-hydrostatic actuator, also known as EHA. This protocol possess an effective design experiment method for EHA fault detection. This protocol combines simulation and experiment fault detection algorithms that can detect the errors of EHA effectively and quickly.
To establish the EHA simulation model open the simulation software on a PC and set the parameters of the model as described in the text manuscript. Then give a position command, a sinusoid with an amplitude of 0.01 meter, and a frequency of two pi radius per second. Enter the modeling menu and click the model settings"button.
Set the simulation operation parameters starting with a start time of zero seconds, a stop time of six seconds, the type as variable step, and the solver as auto. Double click the fault injection switches to set the model to work in a non-fault condition. Click the run button to run the simulation and receive the non-fault condition results.
Run the drawing software to draw the curve of the piston rod misplacement. Double click the insert electromechanical fault switch to inject an electromechanical fault at three seconds, which sets the resistance to 1000 ums to simulate an open circuit fault of the motor windings. Repeat the simulation run as demonstrated earlier to attain the results for the electromechanical fault condition.
Run the drawing software to draw the curves of the piston rod displacement and the identified resistance. Turn the insert hydraulic fault switch to inject a hydraulic fault at three seconds which increases the leakage value to 2.5 times 10^9 cubic meters per second per pascal, to simulate a hydraulic unit fault. Then run the simulation model as demonstrated earlier to attain the results for the hydraulic fault condition.
Run the drawing software to draw the curves of the piston rod displacement and rotational speed estimation results. Position the PC, EHA, and Servo controller. Open the host software interface on the PC and establish communication between the Servo controller and the PC.Select the appropriate serial port from the software's visa resource name dropdown window.
Click the run button to start the software. Observe the receiving area and the corresponding curves of the software to determine whether the data receiving function is normal. Click the solenoid valve two button to observe whether the solenoid valve red light is lightened and determine whether the data transmission function is normal.
Provide drive power to the Servo controller and set the voltage to 50 volts dc. Click the EHA switch button on the software to set the EHA to a running state. Click the data log button to start data logging.
The recorded data will include various parameters, such as the actual position, the target position, the actual speed, the target speed, the bus current, and the voltage. Conduct a pre-run for the EHA and give position commands on the software. Which include a step of plus five and minus five millimeters.
Observe whether the EHA is actuating normally. Give a position command on the software a sinusoid with an amplitude 10 millimeters and a frequency of one hertz. Observe whether the identified resistance and the estimated rotational speed are consistent with the values under non-fault operating conditions.
Put the position command back to the original state, if the result is correct. Click the EHA switch button to stop the EHA and cut off the drive power. Then stop the host computer software and interrupt the communication between the Servo controller and the PC.Export the experimental data, analyze the data, and draw curves of the experimental results using drawing software.
Then proceed with result analysis as described in the text manuscript. In the simulation run, the actual and target position curve of the EHA piston rod in the non-fault condition operated normally with good dynamic characteristics. However, the position curve in the electromechanical fault injection could not track the target accurately.
The resistance identification algorithm demonstrated that before and after injection the identified value converged to the true value, indicating that the method achieved the desired effect. The actual and target position curves in the hydraulic fault injection condition could not track the target accurately. Before injection, the estimated rotational speed was very close to the actual rotational speed.
While after injection, a hydraulic fault could be determined according to the excessive error in the rotational speed. The experimental results were in accordance with the simulation results. The resistance identification algorithm showed that the identified value converged to the true value of 0.3 oms consistent with the simulation, indicating that the method achieved the desired effect.
The corresponding rotational speed estimate was close to the actual rotational speed, and rotational speed error essentially fluctuated in the acceptable range of zero to 2.5 RPS. Fault detection technology is a key to EHA redundancy and the health management. Which can pave the way for further practicality of EHA.
