M. Alamyal

Bio: M. Alamyal is an academic researcher from Newcastle University. The author has contributed to research in topics: Fault (power engineering) & Fault detection and isolation. The author has an hindex of 2, co-authored 2 publications receiving 37 citations.

Detection of Induction Machine Winding Faults Using Genetic Algorithm

Abstract: In this paper, an identification technique for fault detection of induction machines using Genetic Algorithm (GA) is investigated. The condition monitoring technique proposed in this paper indicates the presence of a winding fault and provides information about its nature and location. The data required for the proposed method are motor terminal voltages, stator currents and rotor speed obtained during steady state operation. The data is then processed off-line using an induction motor model in conjunction with GA to determine the effective motor parameters. The proposed technique is demonstrated using experimental data obtained from a 1.5 kW wound rotor three-phase induction machine with both stator and rotor winding faults considered. Results confirm the effectiveness of GA to properly identify the type and location of the fault without the need for knowledge of various fault signatures.

Detection of induction machine winding faults using genetic algorithm

Abstract: In this paper, an identification technique for fault detection of induction machines using Genetic Algorithm (GA) is investigated. The condition monitoring technique proposed in this paper indicates the presence of a winding fault and provides information about its nature and location. The data required for the proposed method are motor terminal voltages, stator currents and rotor speed obtained during steady state operation. The data is then processed off-line using an induction motor model in conjunction with GA to determine the effective motor parameters. The proposed technique is demonstrated using experimental data obtained from a 1.5 kW wound rotor three-phase induction machine with both stator and rotor winding faults considered. Results confirm the effectiveness of GA to properly identify the type and location of the fault without the need for knowledge of various fault signatures.

Model-Based Fault Detection and Identification for Prognostics of Electromechanical Actuators Using Genetic Algorithms

Abstract: Traditional hydraulic servomechanisms for aircraft control surfaces are being gradually replaced by newer technologies, such as Electro-Mechanical Actuators (EMAs). Since field data about reliability of EMAs are not available due to their recent adoption, their failure modes are not fully understood yet; therefore, an effective prognostic tool could help detect incipient failures of the flight control system, in order to properly schedule maintenance interventions and replacement of the actuators. A twofold benefit would be achieved: Safety would be improved by avoiding the aircraft to fly with damaged components, and replacement of still functional components would be prevented, reducing maintenance costs. However, EMA prognostic presents a challenge due to the complexity and to the multi-disciplinary nature of the monitored systems. We propose a model-based fault detection and isolation (FDI) method, employing a Genetic Algorithm (GA) to identify failure precursors before the performance of the system starts being compromised. Four different failure modes are considered: dry friction, backlash, partial coil short circuit, and controller gain drift. The method presented in this work is able to deal with the challenge leveraging the system design knowledge in a more effective way than data-driven strategies, and requires less experimental data. To test the proposed tool, a simulated test rig was developed. Two numerical models of the EMA were implemented with different level of detail: A high fidelity model provided the data of the faulty actuator to be analyzed, while a simpler one, computationally lighter but accurate enough to simulate the considered fault modes, was executed iteratively by the GA. The results showed good robustness and precision, allowing the early identification of a system malfunctioning with few false positives or missed failures.

Detection of stator winding fault in induction motors using a motor square current signature analysis (MSCSA)

Abstract: In this paper the short-circuit fault inter-turn on the stator of an induction motor is analysed by an online diagnostic method. For the diagnostic method it is proposed the motor square current signature analysis (MSCSA). This method uses the results of spectral analysis of the instantaneous square stator current. The effects of stator square current spectrum are described and the related frequencies determined. This method is similar to the instantaneous power signature analysis, however has the advantage of just require one current sensor. Several simulation and experimental results are presented in order to illustrate the characteristics of the proposed method.

Inter-turn short circuit stator fault identification for induction machines using computational intelligence algorithms

Abstract: Under the umbrella of the Computational Intelligence (CI) the performance of a two algorithms: Particle swarm Optimization (PSO) and Bacterial Foraging Optimization (BFO), when used for inter-turn short circuit stator winding fault of induction machine, is investigated in this paper. The proposed condition monitoring technique uses time domain terminal data in conjunction with the optimization algorithm and an induction machine model to indicate the presence of a fault and provide information about its nature and location. The proposed technique is evaluated using experimental data obtained from a 1.5 kW wound rotor three-phase induction machine. PSO and BFO are shown to be effective in identifying the type and location of the fault without the need for prior knowledge of various fault signatures.

A Smart Electromechanical Actuator Monitor for New Model-Based Prognostic Algorithms

Abstract: Prognostic algorithms able to identify precursors of incipient failures of primary flight command electromechanical actuators (EMAs) are beneficial for the anticipation of the incoming fault: an early and correct interpretation of the degradation pattern, in fact, can trig an early alert of the maintenance crew, who can properly schedule the servomechanism replacement. Given that very often these algorithms exploit a model-based approach (e.g. directly comparing the monitor with the real system or using it to identify the fault parameters by means of optimization processes), the design and development of appropriate monitoring models, able to combine simplicity, reduced computational effort and a satisfactory level of sensitivity and accuracy, becomes a fundamental and unavoidable step of the prognostic process. To this purpose, the authors propose a new simplified EMA Monitor Model able to accurately reproduce the dynamic response of the Reference Model in terms of position, speed and equivalent current, even with the presence of various incipient faults; its ability in reproducing the effects of several EMA faults is a good starting point for the implementation of a robust and accurate GA-based optimization, leading to a reliable and early fault isolation

Induction machine Winding Faults Identification using Bacterial Foraging Optimization technique

Abstract: The performance of a stochastic search algorithm, Bacterial Foraging Optimization (BFO), when used for fault identification of induction machine stator and rotor winding faults, is investigated in this paper. The proposed condition monitoring technique uses time domain terminal data in conjunction with the optimization algorithm and an induction machine model to indicate the presence of a fault and provide information about its nature and location. The proposed technique is evaluated using experimental data obtained from a 1.5 kW wound rotor three-phase induction machine. BFO is shown to be effective in identifying the type and location of the fault without the need for prior knowledge of various fault signatures.