## 1 Introduction  
The gradual transformation of more-hydraulic aircraft into more-electric aircraft is becoming more viable with the integration of more efficient technologies such as electro-mechanical actuators (EMA)--the electric equivalent of hydro-pneumatic/electro-hydrostatic actuators. This type of actuator, as the name suggests, is a combination of electric, electronic, and mechanical components. The electric motor of the actuator, usually a DC motor, provides the torque to the system, which can be converted into linear motion during further stages, typically by a ball screw or roller screw. The existing B787 Dreamliner is one such example of more-electric aircraft (MEA) that employs EMAs for different systems, such as the horizontal stabilizer trim, the mid-board spoilers, and the electric braking system (EBS) [1; 2; 3]. Undoubtedly, this has accrued several benefits such as weight reduction,no hydraulic leaks, and faster response to the controller [4] towards achieving environmentally friendly objectives set by the aircraft industry. However, the wider utilization of EMAs in aircraft systems is still subject to extensive evaluation of their performance and reliability under varying operating conditions that may inflict unexpected, as well as some commonly occurring faults, which merit in-depth study and analysis. There are several approaches proposed in literature that can be adapted and optimized to assess and evaluate the characteristic behaviour of EMAs under different fault conditions and enumerate their impact on the associated systems. For example, when supported with a condition-based maintenance (CBM) approach, the EMA integration into commercial airframes can be assured for enhanced reliability and performance through mechanisms that help in the early detection of anomalies and failures [5]. These mechanisms could employ digital tools, including modelling, that are not only time- and resource-efficient but also provide vital behavioral and structural assessments on the system under investigation to reinforce its predictive maintenance and health management regime. Leveraging digital twin (DT) technology coupled with machine learning (ML) is yet another more efficient and highly representative approach that helps enhance system reliability by providing an accurate and precise set of its health predictions [6; 7]. Equally significant is the concurrent and synergetic combination of the analytical model-based approach with the experimental test environment [8] that promises the development of EMA health monitoring technology to increase prognostics and health management (PHM) value for EMA systems, not only by improving their reliability, but also aiding mitigation of the impact of undesired EMA failures. The authors in [9] have advanced a step further with the application of different architectures of artificial neural networks (ANNs) to prognosticate EMAs. Although limited in scope, the approach [9] can facilitate more detailed parametric analysis for accurate EMA health assessment. Notwithstanding the foregoing, a fervent analysis of the aforementioned approaches reveals one key element missing--the qualitative validation approach. This approach, described in later sections of this paper, provides a rubric-based methodology that enables instant identification of fault modes affecting the EMA by weighting different sensor signals and evaluating their cumulative impact on the system. Apart from being a simplistic and efficient methodology, qualitative validation helps develop a better understanding of the issues, originating from varying fault modes, and further enhances the system reliability by optimizing EMA architecture at the design stage. It facilitates an effective and more accurate system health evaluation with vital parametric data collation by differentiating the performance outcome of each fault mode, and eventually constructing a realistic baseline to draw authentic comparison and analyses.