Innovative Actuator Fault Identification Based on Back Electromotive Force Reconstruction

Quattrocchi, Gaetano; Berri, Pier Carlo; Vedova, Matteo Davide Lorenzo Dalla; Maggiore, Paolo

doi:10.3390/act9030050

Cited by 12 publications

(15 citation statements)

References 35 publications

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“…The scope of this work is concerned with applying the prognostic methodology for EMAs proposed in [25] by using the reconstructed signal for residual torque during actuation as the main prognostic indicator or "mapping signal". The methodology consists in equipping the real-life system with sensors that can measure some quantities which are then used to reconstruct other quantities that are used as inputs of an ANN.…”

Section: Scope Of the Workmentioning

confidence: 99%

“…In line with other studies, such as the already referenced [25], shallow artificial neural networks in feed-forward configuration were employed for this work.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Other proposed methods for estimating the health status of systems rely on the use of machine learning algorithms for analyzing one or more signals outputted by the system or reconstructed from output variables (as in the case of the back-electromotive force or BEMF), which are considered prognostic indicators; this approach is often described as hybrid since it leverages both machine learning techniques and knowledge of the physical system. The analysis is, thus, performed with specifically trained ANNs that may be trained on data obtained from simulated models, which use said prognostic indicators in order to estimate the health status of one or more components [25,26].…”

Section: Introductionmentioning

confidence: 99%

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A New Method for Friction Estimation in EMA Transmissions

et al. 2021

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The increasing interest for adopting electromechanical actuators (EMAs) on aircraft demands improved diagnostic and prognostic methodologies to be applied to such systems in order to guarantee acceptable levels of reliability and safety. While diagnostics methods and techniques can help prevent fault propagation and performance degradation, prognostic methods can be applied in tandem to reduce maintenance costs and increase overall safety by enabling predictive and condition-based maintenance schedules. In this work, a predictive approach for EMAs friction torque estimation is proposed. The algorithm is based on the reconstruction of the residual torque in mechanical transmissions. The quantity is then sampled and an artificial neural network (ANN) is used to obtain an estimation of the current health status of the transmission. Early results demonstrate that such an approach can predict the transmission health status with good accuracy.

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Section: Scope Of the Workmentioning

confidence: 99%

“…In line with other studies, such as the already referenced [25], shallow artificial neural networks in feed-forward configuration were employed for this work.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Method for Friction Estimation in EMA Transmissions

et al. 2021

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“…Due to the complex and multidisciplinary nature of the monitored systems, the FDI task on EMA systems is particularly challenging, as different failure modes interact and acceptable accuracy is hardly achieved. A wide choice of FDI techniques is nowadays available in literature: direct comparison of the system response with an appropriate monitoring model [4,5], spectral analysis of system-specific behaviors [6][7][8], artificial neural networks [9][10][11][12], or several combinations of some of these methods [13,14]. Typically, model-based approaches are more computationally expensive and require proper system knowledge but often give more accurate results than data-driven methods.…”

Section: Introductionmentioning

confidence: 99%

A novel model-based metaheuristic method for prognostics of aerospace electromechanical actuators equipped with PMSM

Matteo

Vedova

Berri

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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The prior knowledge of incipient failures of primary flight command electromechanical actuators (EMAs) with prognostic algorithms can be very beneficial. Indeed, early and proper detection and interpretation of the deterioration pattern can warn for replacing the servomechanism before the actual manifestation of the abnormal behaviour. Furthermore, such algorithms often exploit a model-based approach established on the direct comparison between the actual (High Fidelity) and the monitor (Low Fidelity) systems to identify fault parameters through optimization processes. The monitor model allows the acquisition of accurate and precise results with a contained computational effort. The authors developed a new simplified monitor model capable of faithfully reproducing the dynamic response of a typical aerospace EMA equipped with a Permanent Magnet Sinusoidal Motor (PMSM). This digital twin senses mechanical and electrical faults: friction, backlash, coil short circuit, static rotor eccentricity, and proportional gain. Fault detection and identification task are performed by comparing the output signal of the reference system (real or simulated) with the one obtained from the monitor model. After that, the Genetic Algorithm is chosen as the optimization algorithm to match the two signals by iteratively changing the fault parameters to detect the global minimum of a quadratic error function. Once a suitable fit is obtained, the corresponding optimization parameters are correlated with the considered progressive failures to evaluate the system’s health status. The high-fidelity reference models analysed in this work have been previously conceived, developed, implemented in Matlab-Simulink, and validated experimentally by researchers of the ASTRA group of the DIMEAS of Politecnico di Torino.

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“…Much literature has revealed the fault detection and diagnosis in actuators, such as the state observer based method [3], the Kalman filter method [4], and the artificial neural network [5]. Among a large number of fault detection methods for the control valve, the graph model is a promising one due to its strong reasoning ability, such as bond graphs [6].…”

Section: Introductionmentioning

confidence: 99%

Relationship Prediction Based on Graph Model for Steam Turbine Control Valve

Zhang

2021

Actuators

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The control valve is an important piece of equipment in the steam turbine, which frequently suffers from the fault of the dead zone. The graph model is a promising method for dead zone detection, yet establishing an accurate and completed graph topology is not an easy task due to limited mechanism knowledge. Hence, a graph model is proposed to predict the links in the graph and estimate the relationship between variables of related equipment of the control valve. The graph convolution is conducted on the uncompleted graph to learn the low-level representations of the graph nodes, and the score function is used to evaluate the probability of the existence of links between a pair of graph nodes. Results demonstrate a test accuracy of 99.2% for the link prediction, and follow the principles of thermodynamics in the steam turbine. Consequently, the proposed graph model is capable of estimating the relationships for the steam turbine control valve, and other inter-connected industrial systems.

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Innovative Actuator Fault Identification Based on Back Electromotive Force Reconstruction

Cited by 12 publications

References 35 publications

A New Method for Friction Estimation in EMA Transmissions

A New Method for Friction Estimation in EMA Transmissions

A novel model-based metaheuristic method for prognostics of aerospace electromechanical actuators equipped with PMSM

Relationship Prediction Based on Graph Model for Steam Turbine Control Valve

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