Lim Meng Hee

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.

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.

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.

Analysis of Residual Wavelet Scalogram for Machinery Fault Diagnosis

Wavelet analysis is a very useful tool for machinery faults diagnosis. However, actual application of wavelet analysis for machinery fault diagnosis in the field is still relatively rare. This is partly due to the fact that visual interpretation of wavelet results is often difficult and very challenging. This paper investigates an effective method to present wavelet analysis results in order to simplify the interpretation of wavelet analysis result for machinery faults diagnosis. Analysis of residual wavelet scalogram was proposed in this study as a mean to display and extract key faults signatures from raw sensor signals. Simulated signals were generated to test the feasibility of the proposed method. Test results showed that the proposed wavelet method provides a simple and more effective way to diagnose machinery faults.

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.

Segregation of Close Frequency Components Based on Reassigned Wavelet Analysis for Machinery Fault Diagnosis

Vibration signals of rotating machinery often contain many closely located frequency components. While Fast Fourier Transform (FFT) analysis of the signals can identify exact frequency components in the vibration spectrum easily, conventional wavelet analysis is generally incapable of discriminating closely located frequency components in vibration signals due to overlapping and interference appearing in wavelet results. Wavelet transforms based on wavelet reassignment algorithm to improve time-frequency resolution display is presented in this chapter. The proposed reassigned (modified) Morlet wavelet was tested using simulated signal and experimental data obtained from a multi-stage blades rotor test rig. This study showed that this method was capable of segregating close BPF components which were otherwise lumped together in conventional wavelet analysis display. The reassigned Morlet wavelet analysis was shown to be useful for multi stage blade rubbing diagnosis as well as other general condition monitoring applications such as those for gear and bearing faults diagnosis.