scholarly journals Mil-Std 1553 Cable Modeling for Fault Detection

2020 ◽  
Vol 9 (4) ◽  
pp. 1648-1655
Keyword(s):  
Fault Detection ◽  
Public Transportation ◽  
High Speed ◽  
Power Transmission ◽  
Detection System ◽  
Short Circuit ◽  
Detection Algorithm ◽  
Open Circuit ◽  
Test Signal ◽  
Prototype Development

Amongst major industries, the aircraft industry has gained momentum not only in public transportation, but also in defence, business and space sectors. The electrical, mechanical and electronic systems of an aircraft are all interconnected by different types of cables like hook up wires, cables for high speed data transmission, cables for power transmission, fire resistant cables, co-axial cables etc , with each type of cable having its own specifications. Military Standard 1553 (Mil-Std 1553) is one such cable primarily used for on-board aircraft sub-system communication and monitoring. Mil-Std 1553 protocol defines the physical and electrical properties of the cable. Mil-Std 1553 is a dual redundant bus, that is, there are two channels for a single bus communication. Mil-Std 1553 is prone to faults like opens or shorts because of its continuous wear and tear in aircraft environment. If a faulty cable is operated, then it possesses a high risk to the aircraft system .As of now ,there is no automatic fault detection system employed on Mil-Std 1553. Hence there is a need for automatic fault detection system on Mil-Std 1553 cables before the entire system collapses. In this regard, modeling of Mil-Std 1553 is very important since the developed model can be used for testing of the fault detection algorithm and further prototype development. Here, the Mil-Std 1553 cable has been modeled using SIMULINK/MATLAB. The cable is modeled under two different scenarios: considering only the Test Signal , considering both Test Signal and Data Signal. The cable is modeled considering all its electrical characteristics for three conditions, namely, No Fault condition, Open circuit condition and Short circuit condition. PI section is used as an elemental block for modeling of Mil-Std 1553.

Fluttering Amplitude Amplification by Utilizing Flapping Moment in Flutter-Driven Triboelectric Nanogenerator

2021 ◽  
Author(s):  
Yi Zhang ◽  
Ka Chung Chan ◽  
Sau Chung Fu ◽  
Christopher Yu Hang Chao
Keyword(s):  
Flow Velocity ◽  
High Speed ◽  
Aerodynamic Force ◽  
Short Circuit ◽  
Short Circuit Current ◽  
Open Circuit ◽  
Energy Output ◽  
Small Scale ◽  
Triboelectric Nanogenerator ◽  
Peak Value

Abstract Flutter-driven triboelectric nanogenerator (FTENG) is one of the most promising methods to harvest small-scale wind energy. Wind causes self-fluttering motion of a flag in the FTENG to generate electricity by contact electrification. A lot of studies have been conducted to enhance the energy output by increasing the surface charge density of the flag, but only a few researches tried to increase the converting efficiency by enlarging the flapping motion. In this study, we show that by simply replacing the rigid flagpole in the FTENG with a flexible flagpole, the energy conversion efficiency is augmented and the energy output is enhanced. It is found that when the flag flutters, the flagpole also undergoes aerodynamic force. The lift force generated from the fluttering flag applies a periodic rotational moment on the flagpole, and causes the flagpole to vibrate. The vibration of the flagpole, in turn amplifies the flutter of the flag. Both the fluttering dynamics of the flags with rigid and flexible flagpoles have been recorded by a high-speed camera. When the flag was held by a flexible flagpole, the fluttering amplitude and the contact area between the flag and electrode plates were increased. The energy enhancement increased as the flow velocity increased and the enhancement can be 113 times when the wind velocity is 10 m/s. The thickness of the flagpole was investigated. An optimal output of open-circuit voltage reaching 1128 V (peak-to-peak value) or 312.40 V (RMS value), and short-circuit current reaching 127.67 μA (peak-to-peak value) or 31.99 μA (RMS value) at 12.21 m/s flow velocity was achieved. This research presents a simple design to enhance the output performance of an FTENG by amplifying the fluttering amplitude. Based on the performance obtained in this study, the improved FTENG has the potential to apply in a smart city for driving electronic devices as a power source for IoT applications.

scholarly journals A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4688 ◽  
Author(s):  
André Eugênio Lazzaretti ◽  
Clayton Hilgemberg da Costa ◽  
Marcelo Paludetto Rodrigues ◽  
Guilherme Dan Yamada ◽  
Gilberto Lexinoski ◽  
...  
Keyword(s):  
Fault Detection ◽  
Monitoring System ◽  
Environmental Variables ◽  
Energy Use ◽  
Short Circuit ◽  
Environmental Parameters ◽  
Open Circuit ◽  
Ann Model ◽  
Pv System ◽  
Fault Detection And Classification

Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant.

scholarly journals Evaluation of the Correlation Coefficient as a Prognostic Indicator for Electromechanical Servomechanism Failures

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Matteo D. L. Dalla Vedova ◽  
Paolo Maggiore ◽  
Lorenzo Pace ◽  
Alessio Desando
Keyword(s):  
Fault Detection ◽  
Short Circuit ◽  
Prognostic Indicator ◽  
Detection Algorithm ◽  
Anomalous Behavior ◽  
False Alarms ◽  
Test Environment ◽  
Electromechanical Actuator ◽  
Degradation Pattern ◽  
Identification Technique

In order to identify incipient failures due to a progressive wear of a primary flight command electromechanical actuator, several approaches could be employed; the choice of the best ones is driven by the efficacy shown in fault detection/identification, since not all the algorithms might be useful for the proposed purpose. In other words, some of them could be suitable only for certain applications while they could not give useful results for others. Developing a fault detection algorithm able to identify the precursors of the abovementioned electromechanical actuator (EMA) failure and its degradation pattern is thus beneficial for anticipating the incoming malfunction and alerting the maintenance crew such to properly schedule the servomechanism replacement. The research presented in the paper was focused to develop a fault detection/identification technique, able to identify symptoms alerting that an EMA component is degrading and will eventually exhibit an anomalous behavior, and to evaluate its potential use as prognostic indicator for the considered progressive faults (i.e. frictions and mechanical backlash acting on transmission, stator coil short circuit, rotor static eccentricity). To this purpose, an innovative model based fault detection technique has been developed merging several information achieved by means of Fast Fourier Transform (FFT) analysis and proper "failure precursors" (calculated by comparing the actual EMA responses with the expected ones). To assess the performance of the proposed technique, an appropriate simulation test environment was developed: the results showed an adequaterobustness and confidence was gained in the ability to early identify an eventual EMA malfunctioning with low risk of false alarms or missed failures.

scholarly journals A differential operation principle of the automatic bus transfer system pickup unit as a way of decreasing the emergency clearing time.a

2022 ◽  
Vol 1211 (1) ◽  
pp. 012009
Author(s):  
Nikolay Danilov ◽  
Sergey Tsyruk ◽  
Alexandr Timonin ◽  
Karam Sharafeddine
Keyword(s):  
Power Supply ◽  
High Speed ◽  
Short Circuit ◽  
Open Circuit ◽  
Synchronous Motors ◽  
New Device ◽  
Study Results ◽  
Automatic Bus Transfer ◽  
Operation Algorithm ◽  
Emergency Events

Abstract A proper choice of the design and operation algorithm of emergency control devices like high-speed bus transfer (HSBT) is only possible proceeding from a study and analysis of steady-state and transient processes in emergency modes of operation (short-circuit faults, power supply disconnection, or phase open-circuit fault). The numerical experiments for studying such modes that were carried out, using the Matlab Simulink software package, on the mathematical models of an industrial power supply system involving synchronous motors connected to it made it possible to synthesize a new differential HSBT pickup unit featuring a high-speed response to emergency events. In doing so, special attention was paid to an analysis of transient operation modes with the aim of minimizing the probability of false actuations. The obtained study results have found practical application in the HSBT devices installed at the facilities of PJSC MOSENERGO. The experience gained from the operation of a new device jointly with high-speed circuit breakers produced by the Tavrida-Elektrik state-owned corporation has demonstrated essential advantages in comparison with the conventional HSBT designs.

Four-way Gysel power divider/combiner with back-to-back configuration for dual-band operation

2017 ◽  
Vol 10 (2) ◽  
pp. 265-270
Author(s):  
Elham Moradi ◽  
Ali-Reza Moznebi ◽  
Kambiz Afrooz ◽  
Masoud Movahhedi
Keyword(s):  
Design Theory ◽  
Power Transmission ◽  
Short Circuit ◽  
Impedance Matching ◽  
Bottom Layer ◽  
Open Circuit ◽  
Power Divider ◽  
Dual Band ◽  
Line Theory ◽  
Form Design

In this paper, a four-way dual-band Gysel power divider (GPD)/combiner based on a back-to-back microstrip structure method is proposed and investigated. A two-layer substrate is adopted to implement this PD. In order to divide the input signal into four equivalent signals, the input and four output ports of the proposed PD are placed on the top and the four external isolation resistors are placed on the bottom layer of the substrate. Furthermore, the dual-band response is achieved by adding a short-circuit stub and an open-circuit stub to the structure. Then, the theoretical closed-form design formulas are derived based on the considered conditions and circuit transmission line theory. Finally, for verification purpose, a prototype PD is designed, fabricated, and measured which works at dual frequencies of 1 and 2 GHz simultaneously. The good agreement between simulation and measurement results, which show good impedance matching, isolation, as well as power transmission, verifies the correctness of the design theory.

scholarly journals Machine Learning as an Efficient Diagnostic Tool for Fault Detection and Localization in Solar Photovoltaic Arrays

10.29007/34bz ◽  
2019 ◽  
Author(s):  
Masoud Alajmi ◽  
Sultan Aljahdali ◽  
Sultan Alsaheel ◽  
Mohammed Fattah ◽  
Mohammed Alshehri
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Fault Detection ◽  
Solar Energy ◽  
Hot Spot ◽  
Low Cost ◽  
Short Circuit ◽  
Renewable Energy Sources ◽  
Open Circuit ◽  
Detection And Localization

Solar energy, one of many types of renewable energy, is considered to be an excellent alternative to non-renewable energy sources. Its popularity is increasing rapidly, especially because fuel energy consumes and depletes finite natural resources, polluting the environment, whereas solar energy is low- cost and clean. To produce a reliable supply of energy, however, solar energy must also be consistent. The energy we derive from a photovoltaic (PV) array is dependent on changeable factors such as sunlight, positioning of the array, covered area, and status of the solar cell. Every change adds potential for the creation of error in the array. Therefore, thorough research and a protocol for fast, efficient location and correction of all kinds of errors must be an urgent priority for researchers.For this project we used machine learning (ML) with voltage and current sensors to detect, localize and classify common faults including open circuit, short circuit, and hot-spot. Using the proposed algorithm, we have improved the accuracy of fault detection, classification and localization to 100%. Further, the proposed method can execute all three tasks (detection, classification, and localization) simultaneously.

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