Electromechanical servomechanisms affected by motor static eccentricity: Proposal of fault evaluation algorithm based on spectral analysis techniques

Maggiore

2018

IREASE

In the last years, the layout of servomechanisms used in the aeronautical field to actuate the flight controls has changed radically and, nowadays electromechanical actuators (EMAs) are increasingly replacing the older hydraulic powered actuator types. The definition of special monitoring procedures, based on the analysis of the system response and aiming to evaluate the evolution of faults, represents an important task of the modern system engineering taking into account that onboard actuators are typically safety critical items. The present paper proposes a new prognostic procedure centered on the characterization of the state of health of an EMA used in aircraft primary flight controls. This approach, based on the innovative use of a model-based fault detection and identification method (FDI), identifies the actuator actual state of wear of the actuator analyzing proper system operational parameters, able to put in evidence the corresponding degradation path, by means of a numerical algorithm based on spectral analysis techniques. The proposed FDI algorithm has been tested in case of EMA affected by two progressive failures (rotor static eccentricity and stator phase turn-to-turn short-circuit), showing an adequate robustness and a suitable ability to early identify EMA malfunctions with low risk of false alarms or missed failures.

Section: Introductionmentioning

confidence: 99%

Prognostics of Onboard Electromechanical Actuators: a New Approach Based on Spectral Analysis Techniques

Belmonte

Maggiore

2018

IREASE

“…The rotor static eccentricity is often due to wear phenomena that, typically, evolve more slowly with respect to electric progressive faults affecting BLDC motor; in particular, the time scales of short coils degradations are usually similar (at least as orders of magnitude) to other electrical aging degradations. These differences between their evolution time scales allows to estimate the rotor static eccentricity by using the methodologies, also based on spectral analysis, previously developed by the authors [1,2,4]. Given that these FDI methods for evaluate rotor static eccentricity could be influenced by short coils degradation, it will be case study for further investigation occurrences in other to improve identification and evaluation technique presented in this work.…”

Section: -4mentioning

confidence: 97%

“…Controller) and sensors failures, electric motor, mechanical or structural failures. As shown in [4], main failures in BLCD motors are due to progressive stator coil short-circuits (due to thermal effects that could compromise the insulation of the coil windings) and rotor static eccentricity (caused by bearing wears). Short-circuits usually start between a few coils belonging to the same phase (coil-coil failure) and then spreading to adjacent coils.…”

Section: Ema Failures and Performance Degradationsmentioning

confidence: 99%

Electromechanical actuators affected by multiple failures: Prognostic method based on spectral analysis techniques

Belmonte

AIP Conference Proceedings

Ferro

et al. 2017

Abstract. The proposal of prognostic algorithms able to identify precursors of incipient failures of primary flight command electromechanical actuators (EMA) is beneficial for the anticipation of the incoming failure: an early and correct interpretation of the failure degradation pattern, in fact, can trig an early alert of the maintenance crew, who can properly schedule the servomechanism replacement. An innovative prognostic model-based approach, able to recognize the EMA progressive degradations before his anomalous behaviors become critical, is proposed: the Fault Detection and Identification (FDI) of the considered incipient failures is performed analyzing proper system operational parameters, able to put in evidence the corresponding degradation path, by means of a numerical algorithm based on spectral analysis techniques. Subsequently, these operational parameters will be correlated with the actual EMA health condition by means of failure maps created by a reference monitoring model-based algorithm. In this work, the proposed method has been tested in case of EMA affected by combined progressive failures: in particular, partial stator single phase turn to turn short-circuit and rotor static eccentricity are considered. In order to evaluate the prognostic method, a numerical testbench has been conceived. Results show that the method exhibit adequate robustness and a high degree of confidence in the ability to early identify an eventual malfunctioning, minimizing the risk of fake alarms or unannounced failures.

“…In order to provide a versatile test bench on which evaluate the dynamic response of the actuator in different fault conditions, authors have developed a detailed numerical model (simulating the effects of several progressive faults) that has been assumed as a representative of the actual physical system [45].…”

Section: I1 Brief Summary Of the Proposed Fdi Approachmentioning

confidence: 99%

Optimization Techniques for Prognostics of On-Board Electromechanical Servomechanisms Affected by Progressive Faults

Berri

2019

IREASE

In relatively recent years, electromechanical actuators have gradually replaced systems based on hydraulic power for flight control applications. Electromechanical servosystems are typically operated by electrical machines that transfer rotational power to the controlled elements (e.g. the aerodynamic control surfaces) by means of gearings and mechanical transmission. Compared to electrohydraulic systems, electromechanical actuators offer several advantages, such as reduced weight, simplified maintenance and complete elimination of contaminant, flammable or polluting hydraulic fluids. On-board actuators are often safety critical; then, the practice of monitoring and analyzing the system response through electrical acquisitions, with the aim of estimating fault evolution, has gradually become an essential task of the system engineering. For this purpose, a new discipline, called Prognostics, has been developed in recent years. Its aim is to study methodologies and algorithms capable of identifying such failures and foresee the moment when a particular component loses functionality and is no longer able to meet the desired performance. In this paper, authors have introduced the use of optimization techniques in prognostic methods (e.g. model-based parametric estimation algorithms) and have proposed a new model-based fault detection and identification method, based on Genetic Algorithms optimization approach, able to perform an early identification of the aforesaid progressive failures, investigating its ability to identify timely symptoms alerting that a component is degrading.