Ahmed M. Abdelrhman

Wavelet Analysis: Mother Wavelet Selection Methods

Wavelet analysis, being a popular time-frequency analysis method has been applied in various fields to analyze a wide range of signals covering biological signals, vibration signals, acoustic and ultrasonic signals, to name a few. With the capability to provide both time and frequency domains information, wavelet analysis is mainly for time-frequency analysis of signals, signal compression, signal denoising, singularity analysis and features extraction. The main challenge in using wavelet transform is to select the most optimum mother wavelet for the given tasks, as different mother wavelet applied on to the same signal may produces different results. This paper reviews on the mother wavelet selection methods with particular emphasis on the quantitative approaches. A brief description of the proposed new technique to determine the optimum mother wavelet specifically for machinery faults diagnosis is also presented in this paper.

Condition Monitoring of Blade in Turbomachinery: A Review

Blade faults and blade failures are ranked among the most frequent causes of failures in turbomachinery. This paper provides a review on the condition monitoring techniques and the most suitable signal analysis methods to detect and diagnose the health condition of blades in turbomachinery. In this paper, blade faults are categorised into five types in accordance with their nature and characteristics, namely, blade rubbing, blade fatigue failure, blade deformations (twisting, creeping, corrosion, and erosion), blade fouling, and loose blade. Reviews on characteristics and the specific diagnostic methods to detect each type of blade faults are also presented. This paper also aims to provide a reference in selecting the most suitable approaches to monitor the health condition of blades in turbomachinery.

Application of Wavelet Analysis in Blade Faults Diagnosis for Multi-Stages Rotor System

Blade fault is one of the most common faults in turbomachinery. In this article, a rotor system which consists of multiple stages of blades was developed. A variety of blade fault conditions were investigated and its vibration responses were measured. The feasibility of wavelet analysis for multi-stages blade fault diagnosis was tested using simulated signals as well as experimental data. The use of wavelet analysis as the tool to detect multi stages blade faults was studied. Some probable solutions to improve multi stages blade fault diagnosis by wavelet analysis were also suggested.

Vibration analysis of multi stages rotor for blade faults diagnosis

Blade fault is one of the most common faults in turbomachinery. In this article, a rotor system consists of multiple rows of blade was developed. The effectiveness of conventional FFT spectrum and wavelet analysis in the diagnosis of multi stage blade rubbing faults is examined at different stages, variety of blade fault conditions, and different blades rubbing severity. Blade fault caused impacts and the use of wavelets as analysis tool to detect the blade faults was studied. Results showed that, vibration spectrum can clearly depict the location and the stage of blade rubbing, while it is difficult to be identified in wavelet analysis. The limitations of wavelet analysis for multi stage blade fault diagnosis were identified. Some probable solutions to improve wavelet time-frequency representation in blade fault diagnosis were also presented.

A Review of Vibration Monitoring as a Diagnostic Tool for Turbine Blade Faults

Vibration monitoring is widely recognized as an effective tool for the detection and diagnosis of incipient failures of gas turbines. This paper presents a review of vibration based methods for turbine blade faults. Methods typically involved analysis of blade passing frequencies, and extraction of dynamic signals from the measured vibration response. This includes frequency analysis, wavelet analysis, neural networks and fuzzy logic and model based analysis. The literature reviewed showed that vibration could detect most types of blade faults on the basis that dynamic signals are correctly extracted using the most appropriate signal processing method.

Time-Frequency Signal Analysis in Machinery Fault Diagnosis: Review

Growing demand of machines such as gas turbine, pump, and compressor in power generation, aircraft, and other fields have yielded the transformation of machine maintenance strategy from corrective and preventive to condition-based maintenance. Real-time fault diagnosis has grabbed attention of researchers in looking for a better approach to overcome current limitation. The parameters of health condition in machinery could be monitored thus faults could be detected and diagnosed by using signal analysis approach. Since some fault signals are non-stationary or time dependent in nature, therefore time-frequency signal analysis is crucial for machinery fault diagnosis. Common time-frequency signal analysis methods are such as short time Fourier transform (STFT), wavelets analysis, empirical mode decomposition (EMD), Hilbert-Huang transform (HHT), etc. This review provides a summary of the basic principle of signal analysis, the most recent researches, and some advantages and limitations associated to each types of time-frequency signal analysis method.

A Review of Acoustic Emission Technique for Machinery Condition Monitoring: Defects Detection & Diagnostic

Acoustic emission (AE) measurements are one of many non-destructive testing methods which had found applications in defects detection in machines. This paper reviews the state of the art in AE based condition monitoring with particular emphasis on rotating and reciprocating machinery applications. Advantages and limitations of the AE technique in comparison to other condition monitoring techniques in detecting common machinery faults are also discussed.

Vibration Condition Monitoring: Latest Trend and Review

Vibration analysis has proven to be the most effective method for machine condition monitoring to date. Various effective signal analysis methods to analyze and extract fault signature that embedded in the raw vibration signals have been introduced in the past few decades such as fast Fourier transform (FFT), short time Fourier transform (STFT), wavelets analysis, empirical mode decomposition (EMD), Hilbert-Huang transform (HHT), etc. however, these is still a need for human to interpret vibration signature of faults and it is regarded as one of the major challenge in vibration condition monitoring. Thus, most recent researches in vibration condition monitoring revolved around using Artificial Intelligence (AI) techniques to automate machinery faults detection and diagnosis. The most recent literatures in this area show that researches are mainly focus on using machine learning techniques for data fusion, features fusion, and also decisions fusion in order to achieve a higher accuracy of decision making in vibration condition monitoring. This paper provides a review on the most recent development in vibration signal analysis methods as well as the AI techniques used for automated decision making in vibration condition monitoring in the past two years.

Detection of twisted blade in multi stage rotor system

This paper studies the detection of twisted blade in a multi stages rotor system. Experimental study was undertaken to simulate twisted blade conditions in a three stages rotor system. The feasibility of vibration analysis as the technique to detect twisted blade based on the rotor operating frequency and its blade passing frequency was investigated in this study. Experimental results show that twisted blade can be easily detected by looking into the pattern of the vibration spectrum and its individual peaks.