ROBUST FAULT ANALYSIS FOR PERMANENT MAGNET DC MOTOR IN SAFETY CRITICAL APPLICATIONS

Robust fault analysis (FA) including the diagnosis of faults and predicting their level of severity is necessary to optimise maintenance and improve reliability of Aircraft. Early diagnosis of faults that might occur in the supervised process renders it possible to perform important preventative actions. The proposed diagnostic models were validated in two experimental tests. The first test concerned a single localised and generalised roller element bearing fault in a permanent magnet brushless DC (PMBLDC) motor. Rolling element bearing defect is one of the main reasons for breakdown in electrical machines. Vibration and current are analysed under stationary and non-stationary load and speed conditions, for a variety of bearing fault severities, and for both local and global bearing faults. The second test examined the case of an unbalance rotor due to blade faults in a thruster, motor based on a permanent magnet brushed DC (PMBDC) motor. A variety of blade fault conditions were investigated, over a wide range of rotation speeds. The test used both discrete wavelet transform (DWT) to extract the useful features, and then feature reduction techniques to avoid redundant features. This reduces computation requirements and the time taken for classification by the application of an orthogonal fuzzy neighbourhood discriminant analysis (OFNDA) approach. The real time monitoring of motor operating conditions is an advanced technique that presents the real performance of the motor, so that the dynamic recurrent neural network (DRNN) proposed predicts the conditions of components and classifies the different faults under different operating conditions. The results obtained from real time simulation demonstrate the effectiveness and reliability of the proposed methodology in accurately classifying faults and predicting levels of fault severity.the Iraqi Ministry of Higher Education and Scientific Researc

Performance monitoring of aircraft PMSM based on soft computing technique

Rigorous fault analysis (FA) referring to fault detection its level is important for maintenances to simplify as well as improves performance. The purpose of this study was to present a technique for the electronic diagnosis of switch defects in regular magnetic concurrent motors (PMSM) For Aircraft application. The performance level of the bridges of the thyristor and the device excitation diode is tracked in healthy and defective service processes. Extracted functionality in various procedures that use Decomposition of the Multi-Scale Wavelet (MSWD) to remove useful functionality. The functionalities of the MSWD are utilized to train the Autoregressive of the Nonlinear with Exogenous Model (NARX) that was Sequence of the method controlled to determine the level of the fault in an open circuit forming through a switch. Both models were evaluated and built based on simulated data, where the findings demonstrated a preferable efficacy in the diagnosis of different kinds of fault

WLAN performance evaluation in different wireless access techniques (DCF, PCF, HCF)

IEEE 802.11x Wireless Local-Area-Network (WLAN) considered a powerful solution for the last mile wireless broadband(BB) access. WLAN becomes important element in 4G and 5G mobile networks because it can provide services to mobile users in areas not covered by eNBs. However, the 802.11 legacy protocol doesn’t support delay-sensitive services like VoIP because it adopts the best-effort method. In 2001 IEEE 802.11e standard was proposed to deploy QoS with new access techniques introduction. There are many parameters related to MAC layer which affect the WLAN network performance from the prospective of delay, and throughput. This study presents performance evaluation of voice traffic and FTP traffic in IEEE802.11 legacy protocol WLAN and IEEE802.11e WLAN via OPNET computer simulation. Network performance will be tested against different MAC access protocols and different MAC parameters

The Study of Optical and Electrical Properties of Nanostructured Silicon Carbide Thin Films Grown by Pulsed-Laser Deposition

In this paper, nanostructured silicon carbide (SiC) thin films are deposited onto glass substrate using pulsed laser deposition technique. Electrical and optical characterizations such as conductivity, resistivity, transmission, Seeback effect, absorption, absorption coefficient, energy band gap, and extinction coefficient as a function of photon energy, and the effect of thin films thickness on transmission are carried out to characterize the prepared samples. Results showed that the prepared SiC thin film is an n-type semiconductor with an indirect bandgap of ~3 eV, 448 nm cutoff wavelength, 3.4395 × 104 cm−1 absorption coefficient and 0.154 extinction coefficient. The surface morphology of the SiC thin films is studied using scanning electron microscope at a substrate temperature of 400 °C and it is found that the grain size of the prepared SiC thin film is about 30 nm. As such, the nano thin films optical and structural characteristics enable the films to be used as gases sensors in many optoelectronic devices such as the environment and ultraviolet photodiode

Aircraft Actuator Performance Analysis Based on Dynamic Neural Network

Monitoring the condition of the aircraft actuators in various operating and environmental circumstances, this paper presents a method for measuring the surface roughness of aircraft actuators. The proposed method starts with the current and vibration signal as failure indicators and a dual-tree complex wavelet transformation (DTCWT) to generate the necessary features. Time-delay neural networks (TDNNs) have been developed for real-time performance monitoring to categorize problems and determine their severity. The simulation results show that the suggested method can accurately identify various faults