The New Second and Higher Order Spectral Technique for Damage Monitoring of Structures and Machinery

The new second and higher order spectral technique, the cross-covariance of complex spectral components, is proposed for monitoring damage of structure and machinery Normalization of the proposed technique is also developed. It is shown by simulation that the proposed technique provides effectiveness gain for detecting of damage compared to the higher order spectra

Novel adaptation of the spectral kurtosis for vibration diagnosis of gearboxes in non-stationary conditions

In this paper, the adaptation of spectral kurtosis technology is proposed, demonstrated and experimentally validated. Raw data signals were collected from a single-stage gearbox run in different combinations of speed and load, after which time synchronous averaging was used to leave the classical residual signal once meshing harmonics were removed. Each data file is split into many individual realisations based on the time taken for the time synchronous average to converge on stable values, after which the short-time Fourier transform is used to calculate the spectral kurtosis for each realisation. The effects of adapting spectral kurtosis technology parameters such as the resolution and threshold used in creating a Wiener filter are evaluated, showing the effects on the consistent frequency bands identified throughout the realisations. Taking a baseline set of processing parameters, the probability of correct diagnosis was calculated using a three-stage decision-making technique incorporating the k-nearest neighbour and cluster analysis methods. Adaptation of the spectral kurtosis technology is then shown to dramatically improve the probability of correct diagnosis, highlighting that each speed and load case requires different resolution and threshold values to return the optimal result

Novel in-service combustion instability detection using the chirp fourier higher order spectra

Combustion instabilities, known as “rumble” and “screech” are the self-excited aerodynamic instabilities in the gas turbine combustor. They cause the premature failures of the gas turbine components, and, consequently, the failure of the gas turbine as a whole. Because of the complex physical effects underlying the rumble and the screech phenomena, it is difficult to eliminate them completely at the design stage. Therefore, special attention should be paid to the detection of the combustion instabilities in the gas turbine in order to prevent its prolonged operation in this mode. There are known techniques, which are able to detect the rumble and the screech in gas turbines. Most of them do not consider the combustion instabilities as non-linear and non-stationary events and, therefore, have lower detection efficiency. Novel technique for in-service combustion instability detection is implemented in this paper. This technique overcomes the limitations of the existing solutions

Novel health monitoring technology for in‐service diagnostics of intake separation in aircraft engines

Diagnostics and elimination of airflow separation effects draw essential attention of researchers in the areas of energy generation, civil engineering, and aerospace due to unwanted and harmful interaction of separated airflow with different structures. In aviation, distortion of the intake airflows of an aircraft engine, known as intake separation, not only reduces the efficiency of the engine due to decrease in air intake but also interacts with engine structural components, for example, blades, significantly increasing their vibration. This leads to fatigue and subsequent accelerated failure of these components. Therefore, health monitoring and diagnostics of the intake separation effects using structural health monitoring (SHM) framework are of high importance for ensuring both optimal engine performance and its safe operation. In the present paper, a novel health monitoring technology based on advanced signal processing, the integrated higher order spectral technique, is applied for the first time in worldwide terms for in‐service intake separation diagnostics in aircraft engine using casing vibration data

Novel technology based on the spectral kurtosis and wavelet transform for rolling bearing diagnosis

A novel diagnosis technology combining the benefits of spectral kurtosis and wavelet transform is proposed and validated for early defect diagnosis of rolling element bearings. A systematic procedure for feature calculation is proposed and rules for selection of technology parameters are explained. Experimental validation of the proposed method carried out for early detection of the inner race defect. A comparison between frequency band selection through wavelets and spectral kurtosis is also presented. It has been observed that the frequency band selected using spectral kurtosis provide better separation between healthy and defective bearings compared to the frequency band selection using wavelet. In terms of Fisher criterion the use of spectral kurtosis has a gain of 2.75 times compared to the wavelet

Novel Fault Diagnosis of Bearings and Gearboxes Based on Simultaneous Processing of Spectral Kurtoses

Diagnosis of bearings and gears, traditionally uses the envelope (i.e., demodulation) approach. The spectral kurtosis (SK) is a technique used to identify frequency bands for demodulation. These frequency bands are related to the structural resonances, excited by a series of fault-induced impulses. The novel approach for bearing/gear local fault diagnosis is proposed, based on division of bearing/gear vibration signals into specially defined short duration segments and simultaneous processing of SKs of all these segments for damage diagnosis. The SK-filtered vibrations are used for diagnostic feature extraction further subjected to the decision-making process, based on k-means and k-nearest neighbors. The important feature of the proposed approach is robustness to random slippage in bearings. The experimental validation of a bearing inner race local defects (1.2% relative damage size), and simulated gear vibration (15% relative pitting size), shows a very good diagnostic performance on bearing vibrations and gear vibrations to diagnose local faults. Novel diagnostic effectiveness comparison between the proposed technology and wavelet-based technology is performed for diagnosis of local bearing damage

International conference in electrical engineering and intelligent systems

The revised and extended papers collected in this volume represent the cutting-edge of research at the nexus of electrical engineering and intelligent systems. They were selected from well over 1000 papers submitted to the high-profile international World Congress on Engineering held in London in July 2011. The chapters cover material across the full spectrum of work in the field, including computational intelligence, control engineering, network management, and wireless networks. Readers will also find substantive papers on signal processing, Internet computing, high performance computing, and industrial applications.   The Electrical Engineering and Intelligent Systems conference, as part of the 2011 World Congress on Engineering was organized under the auspices of the non-profit International Association of Engineers (IAENG). With more than 30 nations represented on the conference committees alone, the Congress features the best and brightest scientific minds from a multitude of disciplines related to engineering. These peer-reviewed papers demonstrate the huge strides currently being taken in this rapidly developing field and reflect the excitement of those at the frontiers of this research