Experimental diagnosis of multiple faults on a rotor-stator system by fast Fourier transform and wavelet scalogram

This paper presents the recent application of the scalogram of Continuous Wavelet Transform (CWT) as a vibration monitoring and signal processing tool for a rotor dynamic response under parametric excitation. The experimental test data of coupled lateral-torsional vibrations of a rotor-stator system with transverse crack was obtained through a data acquisition set-up interfaced with Rotor-Kit-4 (RK-4). Analysis was executed on rotor deflection, orbit, frequency and time-frequency spectrum of the RK-4 experimental data. The scalograms of CWT were used experimentally to represent the aperiodic occurrence of rub between the rotor-stator and crack features. Variation in 3-D scalogram peaks in the presence of rub and crack were unique and were used to distinguish quasi-periodic motion from other types of motion. An unbalanced cracked rotor gave a higher frequency amplitude response compared to an unbalanced rotor with rub under the same conditions. Irregularities in orbit orientation near sub-harmonic resonances were observed in the test data. Multiple rebounds inside the orbit loop were unique rub indicators. Conspicuous horizontal components of the higher harmonics were observed near the critical speed when a crack existed. CWT established inherent feature patterns that discriminated unbalance, rub and a crack

A general study of the transient stability of synchronous machines

Imperial Users onl

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries

On-line computer control of turbine generators using state estimation and optimal feedback

Imperial Users onl

Flux switching machine design for high-speed geared drives

Electrical machines capable of high-speed operation are key technology used in many modern applications, such as gas turbine electrical systems, high-speed fly-wheels, turbochargers, and computer numerical control (CNC) machines. The use of geared high-speed machines to replace low-speed high torque drives has not been adequately researched to-date. The rationale of this thesis is to investigate a candidate high speed machine, namely flux switching machines to be used together with new types of core material with mechanical gearing to deliver high-torque and low speeds. Modern developments in advanced material technology have produced new magnetic materials capable of dealing with high resulting in very low losses in high speed machines. However, such metals typically have low mechanical strength, and they are found to be brittle. In order to manufacture electromechanical device with such new materials, it has to be reinforced with a mechanically strong structure. The use of multiple types of magnetic materials referred as a MMLC has been proposed in this thesis for high-speed machine design. In this research, a generic method using magnetic equivalent circuit to model flux switching machines (FSMs) is investigated. Moreover modeling, based on machine dimensions for multiphase FSMs having any pole and slot number has been introduced. The air-gap permeance modeling to simplify the magnetic circuit calculation of FSMs was also investigated in this thesis. It is shown that the permeability of magnetic material can be adjusted with the use of MMLC material. Using this feature, the FSM mathematical model is used to show the impact on electromagnetic performance using MMLCs and is shown to be beneficial. In order the evaluate the weight benefits of using geared high speed FSMs, the planetary gear systems are studies and their design constraints have been identified. An abstract form of weight estimation for given torque and speed requirements has been developed and validated using commercially available planetary gear specifications. FSMs together with gear boxes have been considered and it is shown that significant weight savings can be achieved at higher diameter and at high speeds

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Non invasive parameter identification of power plant characteristics based on recorded network transient data

Synchronous generators are the most widely used machines in power generation. Identifying their parameters in a non invasive way is very challenging due to the inherent nonlinearity of power plant performance. This thesis proposes a parameter identification method using particle swarm optimisation (PSO) for the identification of synchronous machine, excitation system and turbine parameters. The PSO allows a generator model output to be used as the objective function to give a new, more efficient method of parameter identification. This thesis highlights the effectiveness of the proposed method for the identification of power plant parameters, using both simulation and real recorded transient data. The thesis also considers the effectiveness of the method as the number of parameters to be identified is increased, and the effect of using differing forms of disturbances on parameter identification.EThOS - Electronic Theses Online ServiceEngineering and Physical Sciences Research Council : Parsons Brinckerhoff : Newcastle UniversityGBUnited Kingdo