Lumped parameters multi-fidelity digital twins for prognostics of electromechanical actuators

The growing affirmation of on-board systems based on all-electric secondary power sources is causing a progressive diffusion of electromechanical actuators (EMA) in aerospace applications. As a result, novel prognostic and diagnostic approaches are becoming a critical tool for detecting fault propagation early, preventing EMA performance deterioration, and ensuring acceptable levels of safety and reliability of the system. These approaches often require the development of various types of multiple numerical models capable of simulating the performance of the EMA with different levels of fidelity. In previous publications, the authors already proposed a high-fidelity multi-domain numerical model (HF), capable of accounting for a wide range of physical phenomena and progressive failures in the EMA, and a low-fidelity digital twin (LF). The LF is directly derived from the HF one by reducing the system degrees of freedom, simplifying the EMA control logic, eliminating the static inverter model and the three-phase commutation logic. In this work, the authors propose a new EMA digital twin, called Enhanced Low Fidelity (ELF), that, while still belonging to the simplified types, has particular characteristics that place it at an intermediate level of detail and accuracy between the HF and LF models. While maintaining a low computational cost, the ELF model keeps the original architecture of the three-phase motor and the multidomain approach typical of HF. The comparison of the preliminary results shows a satisfactory consistency between the experimental equipment and the numerical models

Design and development of innovative FBG-based fiber optic sensors for aerospace applications

In recent years aeronautical systems are becoming increasingly complex, as they are often required to perform various functions. New intelligent systems are required capable of self-monitoring their operation parameters, able to estimate their health status, and possibly perform diagnostic or prognostic functions. For these purposes, these systems frequently need to acquire several different signal types; although it is sometimes possible to implement virtual sensor techniques, it is usually necessary to implement dedicated sensing hardware. On the other hand, the installation of the required sensors can, however, significantly increase the complexity, the weight, the costs and the failure rate of the entire system. To overcome these drawbacks, new types of optical sensors, minimally invasive for measuring the system parameters and having a high spatial resolution and a minimum added complexity are now available. Fiber Bragg Gratings (FBGs) sensors are suitable for measuring various technical parameters in static and dynamic mode and meet all these requirements. In aerospace, they can replace several traditional sensors, both in structural monitoring and in other system applications, including mechatronic systems diagnostics and prognostics. This work reports the results of our experimental research aimed at evaluating and validating different FBG installation solutions such as deformation, bending, vibration, and temperature sensors. These were compared with numerical simulations results and measurements performed with traditional strain gauges and accelerometers

Diagnostics of electro-mechanical actuators based upon the back-EMF reconstruction

Electrical systems are gradually replacing the more traditional hydraulic and pneumatic solutions for the transmission of secondary energy for onboard aircraft equipment. Therefore fault detection and health management strategies properly conceived for electrical devices are becoming a highly relevant topic for research and development in the aerospace disciplines. One possible practical implementation of these methodologies would be the identification of parameters for diagnostic and prognostic monitoring, which are highly sensitive to incipient faults but, at the same time, are less influenced by operating conditions (external loads, command input, temperatures, etc.). In this paper, the authors evaluated the effectiveness of counter-electromotive force (back-EMF) coefficient as a prognostic parameter, emphasizing a novel sampling approach that significantly lower the computational effort required while maintaining a good back-EMF coefficient curve reconstruction. The approach is virtual sensor-like, using only already available data for the correct operation of the BLDC motor. The proposed method was tested by evaluating the back-EMF coefficient reconstruction as a function of some progressive failures typical of EMA motors, such as inter-turn partial shorts and rotor static eccentricity. Its robustness to external disturbances has been tested by evaluating different actuation commands and operating conditions. As expected, the back-EMF signal shows a marked dependence on the considered failure modes and, at the same time, a suitable insensitivity to the other external factors

Innovative actuator fault identification based on back electromotive force reconstruction

The ever increasing adoption of electrical power as secondary form of on-board power is leading to an increase in the usage of electromechanical actuators (EMAs). Thus, in order to maintain an acceptable level of safety and reliability, innovative prognostics and diagnostics methodologies are needed to prevent performance degradation and/or faults propagation. Furthermore, the use of effective prognostics methodologies carries several benefits, including improved maintenance schedule capability and relative cost decrease, better knowledge of systems health status and performance estimation. In this work, a novel, real-time approach to EMAs prognostics is proposed. The reconstructed back electromotive force (back-EMF), determined using a virtual sensor approach, is sampled and then used to train an artificial neural network (ANN) in order to evaluate the current system status and to detect possible coils partial shorts and rotor imbalances

Fault Detection and Identification Method Based on Genetic Algorithms to Monitor Degradation of Electrohydraulic Servomechanisms

Electro Hydraulic Actuators (EHAs) keep their role as the leading solution for the control of current generation primary flight control systems: the main reason can be found in their high power to weight ratio, much better than other comparable technologies. To enhance efficiency and reliability of modern EHAs, it is possible to leverage the diagnostics and prognostics disciplines; these two tools allow reducing life cycle costs without losing reliability, and provide the bases for health management of integrated systems, in compliance with regulations. This paper is focused on the development of a fault detection algorithm able to identify the early signs of EHA faults, through the recognition of their precursors and related degradation patterns. Our methodology provides the advantage of anticipating incoming failures, triggering proper alerts for the maintenance team to schedule adequate corrective actions, such as the replacement of the degraded component. A new EHA model-based fault detection and identification (FDI) method is proposed; it is based on deterministic and heuristic solvers able to converge to the actual state of wear of the tested actuator. Three different progressive failure modes were chosen as test cases for the proposed FDI strategy: clogging of the first stage of the flapper-nozzle valve, spool-sleeve friction increase, and jack-cylinder friction increase. A dedicated simulation model was created for the purpose. The results highlighted that the method is adequate in robustness, since EHA malfunctions were identified with a low occurrence of false alarms or missed failures

Preliminary Analysis on Environmental and Intrinsic Factors on FBG-Based Vibration Sensors

In recent years, optical-based sensors have sparked interest for the many advantages over traditional, electrical-based sensors, such as EMI insensitivity, ease of multiplexing on a single line, resilience to hostile environment and very compact size and global weight saving due to signal cables reduction. Considering said properties, optical sensors offer a compelling alternative to traditional sensing elements. One type of optical sensor is the Fiber Braggs Gratings sensors (FBG), which is a type of sensor that reflects a very narrow band of wavelengths, called Bragg wavelength, while being transparent for others; this behavior is achieved by local variations of the core refractive index. The Bragg wavelength can be easily correlated with physical changes in the sensor itself, due to either physical strain or temperature variation. It should be noted that the achievable measurement accuracy is thus comparable to the Bragg wavelength. However, for any practical application, FBGs need to be bonded to a support or surface; in this case, there is a lack of understanding of the effects of temperature and humidity variations on the combined sensor-glue system. In this work, a setup, intended to characterize the sensitivity of the fiber-glue combination to humidity and temperature will be presented

A sensor fusion strategy based on a distributed optical sensing of airframe deformation applied to actuator load estimation

Real-time health monitoring of mechatronic onboard systems often involves model-based approaches comparing measured (physical) signals with numerical models or statistical data. This approach often requires the accurate measurement of specific physical quantities characterizing the state of the real system, the command inputs, and the various boundary conditions that can act as sources of disturbance. In this regard, the authors study sensor fusion techniques capable of integrating the information provided by a network of optical sensors based on Bragg gratings to reconstruct the signals acquired by one or more virtual sensors (separately or simultaneously). With an appropriate Fiber Bragg Gratings (FBGs) network, it is possible to measure directly (locally) several physical quantities (e.g. temperature, vibration, deformation, humidity, etc.), and, at the same time, use these data to estimate other effects that significantly influence the system behavior but which, for various reasons, are not directly measurable. In this case, such signals could be "virtually measured" by suitably designed and trained artificial neural networks (ANNs). The authors propose a specific sensing technology based on FBGs, combining suitable accuracy levels with minimal invasiveness, low complexity, and robustness to EM disturbances and harsh environmental conditions. The test case considered to illustrate the proposed methodology refers to a servomechanical application designed to monitor the health status in real-time of the flight control actuators using a model-based approach. Since the external aerodynamic loads acting on the system influence the operation of most of the actuators, their measurement would be helpful to accurately simulate the monitoring model's dynamic response. Therefore, the authors evaluate the proposed sensor fusion strategy effectiveness by using a distributed sensing of the airframe strain to infer the aerodynamic loads acting on the flight control actuator. Operationally speaking, a structural and an aerodynamic model are combined to generate a database used to train data-based surrogates correlating strain measurements to the corresponding actuator load

Commensal Bacteria and Expression of Two Major Intestinal Chemokines, TECK/CCL25 and MEC/CCL28, and Their Receptors

Background: CCL25/TECK and CCL28/MEC are CC chemokines primarily expressed in thymic dendritic cells and mucosal epithelial cells. Their receptors, CCR9 and CCR10, are mainly expressed on T and B lymphocytes. In human, mouse, pig and sheep CCL25 and CCL28 play an important role in the segregation and the compartmentalization of the mucosal immune system. As evidenced by early comparisons of germ-free and conventional animals, the intestinal bacterial microflora has a marked effect on host intestinal immune functions. However, little is known about the impact of bacterial colonization on constitutive and induced chemokine expressions as well as on the generation of anti-inflammatory mechanisms. [br/] Methodology/Principal Findings: Therefore, we decided to focus by qPCR on the mRNA expression of two main gut chemokines, CCL25 and CCL28, their receptors CCR9 and CCR10, the Tregs marker Foxp3 and anti-inflammatory cytokines TGF-beta and IL-10 following colonization with different bacterial species within the small intestine. To accomplish this we used an original germ-free neonatal pig model and monoassociated pigs with a representative Gram-negative (Escherichia coli) or Gram-positive (Lactobacillus fermentum) commensal bacteria commonly isolated from the neonatal pig intestine. Our results show a consistent and marked effect of microbial colonization on the mRNA expression of intestinal chemokines, chemokine receptors, Foxp3 and TGF-beta. Moreover, as evidenced by in vitro experiments using two different cell lines, the pattern of regulation of CCL25 and CCL28 expression in the gut appears complex and suggests an additional role for in vivo factors. [br/] Conclusions/Significance: Taken together, the results highlight the key role of bacterial microflora in the development of a functional intestinal immune system in an elegant and relevant model for human immune system development

Detection of a Cis eQTL Controlling BMCO1 Gene Expression Leads to the Identification of a QTG for Chicken Breast Meat Color

Classical quantitative trait loci (QTL) analysis and gene expression QTL (eQTL) were combined to identify the causal gene (or QTG) underlying a highly significant QTL controlling the variation of breast meat color in a F2 cross between divergent high-growth (HG) and low-growth (LG) chicken lines. Within this meat quality QTL, BCMO1 (Accession number GenBank: AJ271386), encoding the β-carotene 15, 15′-monooxygenase, a key enzyme in the conversion of β-carotene into colorless retinal, was a good functional candidate. Analysis of the abundance of BCMO1 mRNA in breast muscle of the HG x LG F2 population allowed for the identification of a strong cis eQTL. Moreover, reevaluation of the color QTL taking BCMO1 mRNA levels as a covariate indicated that BCMO1 mRNA levels entirely explained the variations in meat color. Two fully-linked single nucleotide polymorphisms (SNP) located within the proximal promoter of BCMO1 gene were identified. Haplotype substitution resulted in a marked difference in BCMO1 promoter activity in vitro. The association study in the F2 population revealed a three-fold difference in BCMO1 expression leading to a difference of 1 standard deviation in yellow color between the homozygous birds at this haplotype. This difference in meat yellow color was fully consistent with the difference in carotenoid content (i.e. lutein and zeaxanthin) evidenced between the two alternative haplotypes. A significant association between the haplotype, the level of BCMO1 expression and the yellow color of the meat was also recovered in an unrelated commercial broiler population. The mutation could be of economic importance for poultry production by making possible a gene-assisted selection for color, a determining aspect of meat quality. Moreover, this natural genetic diversity constitutes a new model for the study of β-carotene metabolism which may act upon diverse biological processes as precursor of the vitamin A

In vivo imaging of prodromal hippocampus CA1 subfield oxidative stress in models of Alzheimer disease and Angelman syndrome

Hippocampus oxidative stress is considered pathogenic in neurodegenerative diseases, such as Alzheimer disease (AD), and in neurodevelopmental disorders, such as Angelman syndrome (AS). Yet clinical benefits of antioxidant treatment for these diseases remain unclear because conventional imaging methods are unable to guide management of therapies in specific hippocampus subfields in vivo that underlie abnormal behavior. Excessive production of paramagnetic free radicals in nonhippocampus brain tissue can be measured in vivo as a greaterâ thanâ normal 1/T1 that is quenchable with antioxidant as measured by quenchâ assisted (Quest) MRI. Here, we further test this approach in phantoms, and we present proofâ ofâ concept data in models of ADâ like and AS hippocampus oxidative stress that also exhibit impaired spatial learning and memory. ADâ like models showed an abnormal gradient along the CA1 dorsalâ ventral axis of excessive free radical production as measured by Quest MRI, and redoxâ sensitive calcium dysregulation as measured by manganeseâ enhanced MRI and electrophysiology. In the AS model, abnormally high free radical levels were observed in dorsal and ventral CA1. Quest MRI is a promising in vivo paradigm for bridging brain subâ field oxidative stress and behavior in animal models and in human patients to better manage antioxidant therapy in devastating neurodegenerative and neurodevelopmental diseases.â Berkowitz, B. A., Lenning J., Khetarpal, N., Tran, C., Wu, J. Y., Berri, A. M., Dernay, K., Haacke, E. M., Shafieâ Khorassani, F., Podolsky, R. H., Gant, J. C., Maimaiti, S., Thibault, O., Murphy, G. G., Bennett, B. M., Roberts, R. In vivo imaging of prodromal hippocampus CA1 subfield oxidative stress in models of Alzheimer disease and Angelman syndrome. FASEB J. 31, 4179â 4186 (2017). www.fasebj.orgâ Berkowitz, Bruce A., Lenning, Jacob, Khetarpal, Nikita, Tran, Catherine, Wu, Johnny Y., Berri, Ali M., Dernay, Kristin, Haacke, E. Mark, Shafieâ Khorassani, Fatema, Podolsky, Robert H., Gant, John C., Maimaiti, Shaniya, Thibault, Olivier, Murphy, Geoffrey G., Bennett, Brian M., Roberts, Robin, In vivo imaging of prodromal hippocampus CA1 subfield oxidative stress in models of Alzheimer disease and Angelman syndrome. FASEB J. 31, 4179â 4186 (2017)Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154241/1/fsb2fj201700229r.pd