The paper presents diagnostics methodology that can identify the event of occurrence of fault in the actuator or the linkage system of the flight control actuation system driven by Linear Electromechanical Actuators (LEMA). The standard data analysis like motor current signature analysis (MCSA) is good at identifying the incipient faults within the elements of the actuators in situations where-in the actuators are driving control surfaces. But in back driven cases, where-in LEMA is driven back by control surfaces, the faults outside the LEMAs are difficult to be detected due to higher mechanical advantages of transmission elements like roller screws, gear train and linkage arms scaling down their effects before reaching the motor. One such event occurred in a ground test, wherein the jet vanes were sheared when back driven by excessive gas dynamic forces. Neither the motor current nor the LEMA position feedback data has any clue of the instance of occurrence of such shearing. The case study is discussed in detail and diagnostics solution for such failures is proposed. A new methodology to pin point the event of occurrence is arrived at based on ground static test data of four independent channels. The same is reassured for its applicability using lab experiments on three samples mimicking the failure. The method's applicability is also extended for extracting events in actual flight, by comparing the flight telemetry data with the mimicked lab level (dry runs) data. The methodology uses the analysis of LEMA motor current data to arrive at the vital diagnostic information. The current data of LEMA directly can't be interpreted due to non-stationary nature arising from variable speed and its pulsating form because of the pulse width modulation (PWM) switching, threshold voltages and closed loop dynamics of the servo. Hence the motor current is integrated using cumulative trapezoidal method. This integrated data is spline curve fitted to arrive at residuals vector. The Hadamard product is used on the residuals vector to amplify the information and suppress the noise. Further, normalizing is done to compare data across tests and samples. With this, necessary diagnostic information was extracted from static test data. The method is extended for extracting diagnostics information from actual flight using comparison analysis of, the test data in actual environment with mimicked lab level dry runs. It is also verified for applicability in faults directly driven by actuators in lab level experiments on three samples.
diagnostics, Flight control actuation systems, Electro mechanical actuators, Linear electro mechanical actuators
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