M. Salman Leong

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.

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.

Vibration Analysis of Rub in Rotating Machinery

Rubbing is one of the most common faults that occurs in rotating machinery. This paper studies the vibration responses of a rotor system under the influence of blade induced rubbing. An experimental study was undertaken to simulate various conditions of rubbing in rotor system caused by rotating blades and its corresponding vibration responses are measured and analyzed. Experimental results showed that the effect and vibration responses caused by blade rubbing is enormous and therefore can be easily detected based on vibration spectrum analysis. Besides this, the severity of rubbing caused by different conditions of blades could also be estimated based on the magnitudes and patterns of vibration spectrum.

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.

Integration of Artificial Intelligence into Dempster Shafer Theory: A Review on Decision Making in Condition Monitoring

Machines are the heart of most industries. By ensuring the health of machines, one could easily increase the company revenue and eliminates any safety threat related to machinery catastrophic failures. In condition monitoring (CM), questions often arise during decision making time whether the machine is still safe to run or not Traditional CM approach depends heavily on human interpretation of results whereby decision is made solely based on the individual experience and knowledge about the machines. The advent of artificial intelligence (AI) and automated ways for decision making in CM provides a more objective and unbiased approach for CM industry and has become a topic of interest in the recent years. This paper reviews the techniques used for automated decision making in CM with emphasis given on Dempster-Shafer (D-S) evident theory and other basic probability assignment (BPA) techniques such as support vector machine (SVM) and etc.

Bearing faults identification and resonant band demodulation based on wavelet de-noising methods and envelope analysis

The vibration signals produced by rotating machinery contain useful information for condition monitoring and fault diagnosis. Fault severities assessment is a challenging task. Wavelet Transform (WT) as a multivariate analysis tool is able to compromise between the time and frequency information in the signals and served as a de-noising method. The CWT scaling function gives different resolutions to the discretely signals such as very fine resolution at lower scale but coarser resolution at a higher scale. However, the computational cost increased as it needs to produce different signal resolutions. DWT has better low computation cost as the dilation function allowed the signals to be decomposed through a tree of low and high pass filters and no further analysing the high-frequency components. In this paper, a method for bearing faults identification is presented by combing Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT) with envelope analysis for bearing fault diagnosis. The experimental data was sampled by Case Western Reserve University. The analysis result showed that the proposed method is effective in bearing faults detection, identify the exact faults location and severity assessment especially for the inner race and outer race faults.

Numerical investigations on axial and radial blade rubs in turbo-machinery

In the recent years, the clearance between the rotor blades and stator/casing had been getting smaller and smaller prior improving the aerodynamic efficiency of the turbomachines as demand in the engineering field. Due to the clearance reduction between the blade tip and the rotor casing and between rotor blades and stator blades, axial and radial blade rubbing could be occurred, especially at high speed resulting into complex nonlinear vibrations. The primary aim of this study is to address the blade axial rubbing phenomenon using numerical analysis of rotor system. A comparison between rubbing caused impacts of axial and radial blade rubbing and rubbing forces are also aims of this study. Tow rotor models (rotor-stator and rotor casing models) has been designed and sketched using SOILDSWORKS software. ANSYS software has been used for the simulation and the numerical analysis. The rubbing conditions were simulated at speed range of 1000rpm, 1500rpm and 2000rpm. Analysis results for axial blade rubbing showed the appearance of blade passing frequency and its multiple frequencies (lx, 2x 3x etc.) and these frequencies will more excited with increasing the rotational speed. Also, it has been observed that when the rotating speed increased, the rubbing force and the harmonics frequencies in x, y and z-direction become higher and severe. The comparison study showed that axial blade rub is more dangerous and would generate a higher vibration impacts and higher blade rubbing force than radial blade rub.