Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Summary

In this paper, an adaptive filter based on a normalized least mean square (NLMS) algorithm and a rotational speed estimation method are introduced to detect the electrical and hydraulic faults of the electro-hydrostatic actuator (EHA). The efficacy and feasibility of the aforementioned methods are verified through simulations and experiments.

Abstract

The electro-hydrostatic actuator (EHA) is a promising actuating apparatus used in flight control systems for more electric aircraft (MEA) due to its high power density and low maintenance. Since the reliability of the system decreases with increasing complexity, fault detection is becoming increasingly important. In this paper, an adaptive filter was designed based on a normalized least mean square (NLMS) algorithm, which could identify the resistance of the motor windings online to detect electrical faults in the EHA. Additionally, based on the analytical relationship between rotational speed and displacement, a rotational speed estimation method was designed. By comparing the actual rotational speed with the estimated one, hydraulic faults could be detected. To verify the efficacy of the aforementioned method, software was applied for the modeling and simulations, which included fault injection and detection. On this basis, an experimental platform was built and then subjected to a series of validation experiments. The results indicate that the fault detection method has the potential to detect electrical and hydraulic faults in an EHA.

Introduction

The electro-hydrostatic actuator (EHA) is a key component for flight control in more electric aircraft (MEA). The typical structure of an EHA is shown in Figure 1. Its compact structure guarantees high power density, low maintenance, and higher fault tolerance and safety compared with the traditional hydraulic servo actuator (HSA)¹. However, the current reliability of the EHA cannot meet the practical requirements of more electric aircraft². As a result, redundancy technology has been introduced into the design of the EHA. To maximize the effectiveness of the redundancy technology, the operating status of the system should be monitored by a fault detection method³. According to the location where the fault occurs, the fault modes of the EHA can be divided into servo controller faults and power control unit (PCU) faults. PCU faults can be further divided into sensor faults, electromechanical unit faults, and hydraulic unit faults. The fault mechanism of the servo controller has little relationship with the EHA body, and the fault probability of the sensor is much lower than that of the equipment component⁴. Therefore, we focus on the faults of the electromechanical unit and hydraulic unit in this paper.

Electromechanical unit faults include motor drive module faults and brushless DC motor (BLDCM) faults. Generally, the probability of a power drive electronics (PDE) fault (e.g., a short-circuit fault, an open-circuit fault) is relatively high. When a short-circuit fault occurs, the PDE current rises sharply in a short amount of time, causing severe consequences such as a motor shutdown or damage to the electrical components. Although the motor can maintain its working status after an open-circuit fault occurs, overcurrent and overvoltage for the other electrical components are still inevitable, and secondary faults may consequently happen⁵. As for the BLDCMs, the motor windings are most prone to faults from a short circuit or an open circuit⁶. The PDE in the electromechanical unit is connected in series with the corresponding motor windings. The fault detection method designed for the motor windings is also effective when dealing with faults in the PDE. Therefore, electromechanical unit faults, including both in the motor and the PDE, should be detected online.

Hydraulic unit faults include fault occurrences in the fixed-displacement piston pump, integrated valve block, and actuating cylinder⁷. The EHA's piston pump is composed of pistons, swash plates, and valve plates; damage to the seal and wear of the valve plate are the main forms of fault⁸. These two fault modes increase the leakage of the pump. Abnormal changes in the output flow and pressure follow and, eventually, lead to a decrease in the speed of the actuating cylinder and a reduction in the servo performance of the system. The fault modes of the integrated valve block include a pressurized reservoir fault, a check valve fault, a relief valve fault, and a mode selection valve fault. The pressurized reservoir usually adopts a self-boosting design with high reliability. When a fault occurs, however, insufficient charge pressure causes cavitation of the pump, resulting in abnormal output flow. Spring fatigue, component wear, and deformation are common fault modes in the check valves and relief valves. A check valve fault presents as a reverse leakage, which directly leads to abnormal flow. A relief valve fault leads to an invalid protection function, resulting in abnormal pressure. The common faults of the mode selection valve are failure of the return spring and broken wire coils. The former causes in-current switching of the working status, leading to abnormal movement of the actuating cylinder. An actuating cylinder fault results in a decrease in position control precision and dynamic performance. In summary, faults of the hydraulic units cause abnormal flow and pressure⁹. Since the flow and motor rotational speed are approximately proportional in an EHA system, the rotational speed can be monitored online to detect abnormal flow and pressure due to sudden faults.

Corresponding fault detection methods aimed at the previously mentioned electromechanical unit faults and hydraulic unit faults need to be designed. The methods for fault detection in an electromechanical system mainly include state estimation and parameter identification¹⁰. A state observer is built on the basis of a mathematical model of the system that makes a state estimation and determines faults by analyzing the residual sequence generated by the observer. Alcorta et al. proposed a simple and novel nonlinear observer with two correction terms for vibration fault detection in commercial aircraft, which is highly effective¹¹. However, this type of method must solve the robustness problem of the observer. In other words, it must suppress the changes in the residual sequence caused by non-fault information such as model error or external disturbances. Moreover, this method often requires very accurate model information, which is usually difficult to collect in practical engineering applications.

The parameter identification method employs certain algorithms for identifying the important parameters in the system. When a fault occurs, the corresponding parameter value also changes. Therefore, faults can be detected by detecting a change in the parameters. The parameter identification method does not require the calculation of the residual sequence, so it can avoid the effect of disturbances on the detection accuracy. The adaptive filter has been widely used in parameter identification due to its easy implementation and stable performance, meaning it is a favorable and feasible method for electromechanical fault detection¹². Zhu et al. proposed a new multi-model adaptive estimation fault detection method based on kernel adaptive filters, which realizes the estimation of the real flight state value and the actuator fault detection online with good performance¹³.

Referring to the previous research, corresponding fault detection methods have been designed. The resistance of the windings abruptly changes when electrical faults occur, such as open-circuit faults or short-circuit faults. Therefore, an adaptive filter was designed based on an NLMS algorithm to identify the resistance of the windings, which can determine whether an electrical fault has occurred. Combining an adaptive filter with an NLMS algorithm to minimize the change of the parameter vector leads to a better and faster convergence effect¹⁴. For hydraulic unit faults, a rotational speed estimation algorithm was proposed based on the clear analytical relationship between the rotational speed of the pump and the position of the actuating cylinder. EHA hydraulic faults were detected online by comparing the estimated rotational speed with the actual speed in real time.

In this paper, a test method combining simulations and experiments was adopted. First, a mathematical model of the EHA was built, and a simulation for the proposed fault detection method was performed. The simulation included the verification of the detection methods in non-fault and fault injection conditions. Then, the fault detection method was realized in the real servo controller. Finally, the results of the simulations and experiments were analyzed and compared to evaluate the efficacy of the fault detection method.

Protocol

1. Establishment of the EHA simulation model Open the simulation software on a PC. Build the simulation model for the EHA (Figure 2), according to the mathematical equations of the EHA model15, and conduct a three-loop PI as the controller. Encapsulate the hydraulic module (Figure 2C), electrical module (Figure 2B), and controller (Figure 2B…

Representative Results

In the simulation, the actual position and target position curve of the EHA piston rod in the non-fault condition is shown in Figure 7. According to the curve, the system was operating normally, with good dynamic characteristics. The actual position and target position curve of the EHA piston rod in the electromechanical fault injection condition is shown in Figure 8. According to the curve, the system could not track the target accurately. The results of the re…

Discussion

When conducting these experimental steps, it was important to ensure the real-time capability of the algorithm in order to obtain accurate calculation results. The white noise in the signal acquisition process was adopted to simulate the characteristics of the actual sensor in order to make the simulation closer to reality. In the simulations and experiments, moving average filters were applied to reduce the fluctuation in the identified resistance and estimated rotational speed, which made the fault characteristics more…

Materials

LabVIEW	NI	NI LabVIEW 2018
Matlab/SIMULINK	MathWorks.Inc	R2020a
Personal Computer	Lenovo	Y7000 2020H
24V Switching Power Supply	ECNKO	S-250-24
Programmable Current Source	Greens Pai	GDP-50-30