scholarly journals Innovative Actuator Fault Identification Based on Back Electromotive Force Reconstruction

Actuators ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 50 ◽  
Author(s):  
Gaetano Quattrocchi ◽  
Pier C. Berri ◽  
Matteo D. L. Dalla Vedova ◽  
Paolo Maggiore
Keyword(s):  
Electromotive Force ◽  
Current System ◽  
Electrical Power ◽  
Performance Estimation ◽  
Electromechanical Actuators ◽  
Maintenance Schedule ◽  
Force Reconstruction ◽  
Safety And Reliability ◽  
And Performance ◽  
Artificial Neural Network Ann

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.

scholarly journals A New Method for Friction Estimation in EMA Transmissions

Actuators ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 194
Author(s):  
Gaetano Quattrocchi ◽  
Alessandro Iacono ◽  
Pier C. Berri ◽  
Matteo D. L. Dalla Dalla Vedova ◽  
Paolo Maggiore
Keyword(s):  
Health Status ◽  
Good Accuracy ◽  
Friction Torque ◽  
Electromechanical Actuators ◽  
Current Health Status ◽  
Early Results ◽  
Torque Estimation ◽  
And Performance ◽  
Artificial Neural Network Ann ◽  
Maintenance Schedules

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.

Remote Thermal Performance Monitoring and Diagnostics: Turning Data Into Knowledge

2013 ◽  
Author(s):  
Xiaomo Jiang ◽  
Craig Foster
Keyword(s):  
Anomaly Detection ◽  
Gas Turbines ◽  
Performance Monitoring ◽  
Combined Cycle ◽  
Integrated System ◽  
Site Specific ◽  
Performance Improvements ◽  
Maintenance Schedule ◽  
Effectiveness Monitoring ◽  
And Performance

Gas turbine simple or combined cycle plants are built and operated with higher availability, reliability, and performance in order to provide the customer with sufficient operating revenues and reduced fuel costs meanwhile enhancing customer dispatch competitiveness. A tremendous amount of operational data is usually collected from the everyday operation of a power plant. It has become an increasingly important but challenging issue about how to turn this data into knowledge and further solutions via developing advanced state-of-the-art analytics. This paper presents an integrated system and methodology to pursue this purpose by automating multi-level, multi-paradigm, multi-facet performance monitoring and anomaly detection for heavy duty gas turbines. The system provides an intelligent platform to drive site-specific performance improvements, mitigate outage risk, rationalize operational pattern, and enhance maintenance schedule and service offerings via taking appropriate proactive actions. In addition, the paper also presents the components in the system, including data sensing, hardware, and operational anomaly detection, expertise proactive act of company, site specific degradation assessment, and water wash effectiveness monitoring and analytics. As demonstrated in two examples, this remote performance monitoring aims to improve equipment efficiency by converting data into knowledge and solutions in order to drive value for customers including lowering operating fuel cost and increasing customer power sales and life cycle value.

scholarly journals Non-isolated load sharing direct current system operation with use of the auctioneering diodes

2019 ◽  
Vol 28 ◽  
pp. 01037 ◽  
Author(s):  
Maciej Kozak
Keyword(s):  
Power Distribution ◽  
Distribution System ◽  
Heat Dissipation ◽  
Current System ◽  
Electrical Power ◽  
Experimental System ◽  
Laboratory System ◽  
Simulation Studies ◽  
Power Distribution System ◽  
Electrical Systems

The paper presents the background and results of numerical simulation and experimental research of a system using auctioneering diodes used to distribute the electrical power between two power converters connected with intermediate circuits in parallel, direct connection. Presented non-isolated power distribution system which utilizes blocking diodes placed in DC branches are used in the selected ship's electrical systems, however, they create problems related to control and handling ground faults. Another issue occurring during the operation of this type of systems is increased heat dissipation while diodes switching. Selected problems related to the operation of experimental system have been identified by means of simulation studies and experiments carried out in a 11 kVA laboratory system and the theoretical basis along with results are provided in the article.

scholarly journals Characterization and Performance Estimation of a MEMS Spirometer

2016 ◽  
Vol 168 ◽  
pp. 1020-1023 ◽  
Author(s):  
Sahar Habibiabad ◽  
Yeşim Serinağaoğlu Doğrusöz ◽  
Mustafa İlker Beyaz
Keyword(s):  
Performance Estimation ◽  
And Performance

scholarly journals Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory

Actuators ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 27
Author(s):  
Sehrish Malik ◽  
DoHyeun Kim
Keyword(s):  
Smart Factory ◽  
Prediction Algorithm ◽  
Utilization Rate ◽  
Task Management ◽  
Management Mechanism ◽  
Machine Maintenance ◽  
Machine Utilization ◽  
And Performance ◽  
Artificial Neural Network Ann ◽  
Prediction Mechanism

The prediction mechanism is very crucial in a smart factory as they widely help in improving the product quality and customer’s experience based on learnings from past trends. The implementation of analytics tools to predict the production and consumer patterns plays a vital rule. In this paper, we put our efforts to find integrated solutions for smart factory concerns by proposing an efficient task management mechanism based on learning to scheduling in a smart factory. The learning to prediction mechanism aims to predict the machine utilization for machines involved in the smart factory, in order to efficiently use the machine resources. The prediction algorithm used is artificial neural network (ANN) and the learning to prediction algorithm used is particle swarm optimization (PSO). The proposed task management mechanism is evaluated based on multiple scenario simulations and performance analysis. The comparisons analysis shows that proposed task management system significantly improves the machine utilization rate and drastically drops the tasks instances missing rate and tasks starvation rate.

Application and performance estimation of Mg/CO2 engine on Mars

2021 ◽  
Author(s):  
Mengzhe Li ◽  
Chunbo Hu ◽  
Zhiqin Wang ◽  
Yue Li ◽  
Jiaming Hu ◽  
...  
Keyword(s):  
Performance Estimation ◽  
And Performance

Performance prediction of axial flow compressors using stage characteristics and simultaneous calculation of interstage parameters

2001 ◽  
Vol 215 (1) ◽  
pp. 89-98 ◽  
Author(s):  
T. W. Song ◽  
T. S. Kim ◽  
J. H. Kim ◽  
S. T. Ro
Keyword(s):  
Gas Turbine ◽  
System Analysis ◽  
Axial Flow ◽  
Performance Estimation ◽  
Predicting Performance ◽  
Functional Formulation ◽  
Compressor Inlet ◽  
Multistage Compressors ◽  
And Performance ◽  
Axial Flow Compressors

A new method for predicting performance of multistage axial flow compressors is proposed that utilizes stage performance curves. The method differs from the conventional sequential stage-stacking method in that it employs simultaneous calculation of all interstage variables (temperature, pressure and flow velocity). A consistent functional formulation of governing equations enables this simultaneous calculation. The method is found to be effective, i.e. fast and stable, in obtaining solutions for compressor inlet and outlet boundary conditions encountered in gas turbine analyses. Another advantage of the method is that the effect of changing the angles of movable stator vanes on the compressor's operating behaviour can be simulated easily. Accordingly, the proposed method is very suitable for complicated gas turbine system analysis. This paper presents the methodology and performance estimation results for various multistage compressors employing both fixed and variable vane setting angles. The effect of interstage air bleeding on compressor performance is also demonstrated.

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