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Turbomachinery condition monitoring and fault detection in the Malaysian oil and gas industry is currently done by monitoring the parameters of the equipment, such as a gas turbine, based on limits provided by the original equipment manufacturer. This is performed in an attempt to avoid any unscheduled downtime and catastrophic failure of the machinery. However, this method has proven to be insufficient and ineffective in providing early information or warning regarding machine faults. This chapter presents a case study of a gas turbine that developed a blade fault in an oil and gas plant despite operating within its original equipment manufacturer limits. The parameters used for machinery condition monitoring were then analysed using a self-organising map; a two-dimensional graphical layout consists of neurons arranged in contact with one another. The results demonstrate that such a map is efficient in providing early warning regarding turbomachinery’s health conditions.

Acoustic Emission (AE) technology is probably one of the most recent entries in the field of machinery condition monitoring. This paper investigates the application of AE parameters for valve faults detection in reciprocating compressor. The defective valves were designed by emulating the actual valve corrosion with varying sizes such that defects could be applied onto the reciprocating compressor. A set of experiments was performed to acquire the AE signal. The primary source of AE signal was verified using waveform analysis. The AE parameters versus different operational and valve condition were illustrated individually. In addition, the significance of the change and sensitivity of AE parameters versus different experimental conditions was verified using MANOVA and coefficient of variance analysis. It is concluded that the AE signal parameters can be used to detect the valve faults in the reciprocating compressor with the consideration of the variation in the AE parameters sensitivity. Keywords: Reciprocating compressor, Valve fault detection, Acoustic emission parameters, MANOVA, Coefficient of variance

Support vector machines (SVMs) are frequently used in automated machinery faults diagnosis to classify multiple machinery faults by handling a high number of input features with low sampling data sets. SVMs are well known for fault detection that involves binary fault classifications only (i.e., healthy vs. faulty). However, when SVMs are used for multi-faults diagnostics and classification, they result in a drop in classification accuracy; this is because the adaptation of SVMs for multi-faults classifications requires the reduction of the multiple classification problem into multiple subsets of binary classification problems that result in many contradictory results from each individual SVM model. To overcome this problem, a novel SVM-DS (Dempster-Shafer evidence theory) model is proposed to resolve conflicting results generated from each SVM model and thus increase the classification accuracy. The analysis of results shows that the proposed SVM-DS model increased the accuracy of the fault diagnosis model from 76% to 94%, as SVM-DS continuously refines and eliminates all conflicting results from the original SVM model. The proposed SVM-DS model is found to be more accurate and effective in handling multi-faults diagnostic and classification problems commonly faced in the industries, as compared to the original SVM method.

Bearing fault diagnosis has a pivotal role in condition-based maintenance. Vibration spectra analysis has been proven to be the most efficient method for rotating machinery fault diagnosis. Vibration spectra can be analyzed by various signal processing tools (e.g. wavelet analysis, empirical mode decomposition, Hilbert-Huang transform). However, they involve human expertise in ensuring its maximum success. Machine learning tools (e.g. artificial neural networks (ANN), support vector machines (SVM)) can be an alternative for an automatic fault diagnosis. Researchers have studied the feasibility of ANN for automatic fault diagnosis since last decades. Most of the researchers reported positive finding in adapting ANN for automatic fault diagnosis. However, its accuracy is highly dependent on the neural networks structure such as number of nodes, hidden layers, and sigmoid function. This study proposed a hybrid algorithm used for automated bearing fault diagnosis based on ANN and Dempster-Shafer (DS) theory. The hybrid algorithm employed DS theory to improve the fault diagnosis results from ANN by eliminating conflicting results generated by ANN. Four conditions of bearing namely healthy condition and three types of faults included ball, inner race, and outer race faults classify by the proposed hybrid algorithm and artificial neural networks. The superiority of the hybrid algorithm was shown by comparing its result with the performance of ANN alone.

Reciprocating compressor is one of the most popular classes of machines use with wide applications in the industry. However, valve failures in this machine often results unplanned shutdown. Therefore, the effective valve fault detection technique is very necessary to ensure safe operation and to reduce the unplanned shutdown. This paper propose an artificial intelligence (AI) model to detect valve condition in reciprocating compressor based on acoustic emission (AE) parameters measurement and artificial neural network (ANN). A set of experiments were conducted on an industrial reciprocating air compressor with several operational conditions including good valve and faulty valve to acquire AE signal. A fault detection model was then developed from the combination of healthy-faulty data using ANN tool box available in MATLAB. The results of the model validation demonstrated accuracy of valves condition classification exceeding 97%. Eventually, the authors intend to do more efforts for programming this model in smart portable device which can be one of the innovative engineering technologies in the field of machinery condition monitoring in the near future.

Blade fault is one of the most causes of failure in turbo machinery. This paper discussed the time frequency analysis for blade rubbing detection from casing vibration signal. Feasibility of Short Time Fourier Transform (STFT), Wigner-Ville distribution (WVD) and Choi-Williams distribution (CWD) were examined for blade rub detection in a multi stage blade system through an experimental data. Analysis results of the experimental data showed that these time frequency analysis methods have some inevitable deficiencies in segregating the blade passing frequency (BPF) components of the three rotor stage signals. However, CWD demonstrated a better time-frequency resolution in analyzing the multi stage rotor system signal.

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.

This paper presents a case study in diagnosing a frequent fatigue failure of a reduction gear used in power generation in oil and gas industry. The reduction gear was connected to a main oil pump (MOP) via a coupling which in turn draw oil from oil reservoir via a series of connecting pipes. An initial investigation conducted by the plant personnel found that the root cause of the frequent gear failure was caused by the recurring misalignments between the reduction gear and the MOP which had resulted in many unscheduled downtimes. Misalignment of the machines recurred times and again in just a short period of time right after the completion of machines alignment works. In view of the complexity of the problem, a vibration Operational Deflection Shape (ODS) analysis of the entire machine train was deployed to obtain a more comprehensive insight into the problem. Results of the ODS analysis revealed that the actual root cause of them is alignment could be traced to pipe strain or misalignment of the pipes that connected to the MOP. Pipe strain was evidenced from the excessive out of phase between the two-connecting pipe flange that routed to MOP. Pipe strain exerts excessive pulling force that caused misalignment to MOP and the entire machines train. The condition of misalignment aggravated as the X2forcing frequency of misalignment coincided with the natural frequency of the MOP itself. MOP resonance had thus triggered a feedback loop that enabled the exchange of vibration energy between resonance and pipe strain. In short, pipe strain had initiated misalignment in MOP and subsequently triggered MOP resonance that fed more vibration energy into the connecting pipe creating a much larger pipe strain. The vicious cycle continues until failures eventually occurred in the reduction gear that connected to the MOP. The remedial works undertaken was thus to eliminate the pipe strain by replacing a section of a rigid pipe with a flexible braided pipe. This enables the system to be more tolerable to pipe strain and also decouples pipe strain from triggering misalignment and resonance in MOP. The remedy measure had successfully resolved the frequent gear failures and also reduced the overall vibration level of the entire machine train by almost half., {"references":["ASME B31.3 - 2006 . (2006). Process Piping. American ASME Code for Pressure Piping, B31: Process Piping .","American Petroleum Institute, 2009. API Recommended Practice 686, Second Editions Decemb er 2009: Recommended Practice for Machinery Installation and Installation Design.","Dave W. F . (2006). Paip Strain. Orbit Magazine . Vol.26 No.3 .","Yates, K . (2009). Steam Turbine Pipe Strain Case Study.. Texas A&M University. Turbomachinery Laboratories. A vailable electronically from http://hdl.handle.net/1969.1/163081. Doi:10.21423/R1GM0T","Ashok K., Mohan. K., Rahul. C. (2016) Piping Stress Considerations and Practical Remedies to Overcome Nozzle Misalignment and Vibration Related Issue. Vth Intern ational Symposium on \"Fusion of Science & Technology\", New Delhi, India, January 18 - 22, 2016."]}

A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence the fault classification performance. Feature selection plays a vital role in selecting the most representative feature subset for the machine learning algorithm. In contrast, the trade-off relationship between capability when selecting the best feature subset and computational effort is inevitable in the wrapper-based feature selection (WFS) method. This paper proposes an improved WFS technique before integration with a support vector machine (SVM) model classifier as a complete fault diagnosis system for a rolling element bearing case study. The bearing vibration dataset made available by the Case Western Reserve University Bearing Data Centre was executed using the proposed WFS and its performance has been analysed and discussed. The results reveal that the proposed WFS secures the best feature subset with a lower computational effort by eliminating the redundancy of re-evaluation. The proposed WFS has therefore been found to be capable and efficient to carry out feature selection tasks.

This paper presents a case study in managing the dilemma of whether to resume or stop the operation of a power generation gas turbine with suspected blade faults. Vibration analysis is undertaken on the vibration signal of the gas turbine, to obtain an insight into the health condition of the blades before any decision is made on the operation of the machine. Statistical analysis is applied to study the characteristics of the highly unstable blade pass frequency (BPF) of the gas turbine and to establish the baseline data used for blade fault assessment and diagnosis. Based on the excessive increase observed on specific BPF amplitudes in comparison to the statistical baseline data, rubbing at the compressor blade is suspected. An immediate overhaul is therefore warranted, and the results from the inspection of the machine confirm the occurrence of severe rubbing at the compressor blades and labyrinth glands of the gas turbine. In conclusion, statistical analysis of BPF amplitude is found to be a viable tool for blade fault diagnosis in industrial gas turbines.

Artificial intelligence (AI) has played an increasingly important role in condition monitoring and machinery fault diagnosis in power generation plants. However, the accuracy and reliability of any AI-based machinery fault diagnosis is highly dependent on the quality and quantity of the input data fed to the AI model. The hypothesis of this paper is that AI-based fault diagnosis can be further improved by taking into account all the available sensor inputs of the machine. In short, the more sensor inputs fed into the AI model, the more accurate and reliable the outcome of the fault diagnosis. This paper proposes an application of Dempster-Shafer (DS) evidence theory for sensor fusion to improve the accuracy of decision-making in machinery fault diagnosis, by fusing all the available vibration signals measured on different axes and locations of the test machine. Vibration signals from different axes and locations of a machinery faults simulator were collected by multiple accelerometers simulating various machinery health conditions, namely healthy, unbalance, misalignment and foundation looseness. The accuracy of fault diagnosis using a different number of sensor inputs was then investigated. Analysis results showed that by combining more sensor inputs using a DS-based algorithm can improve fault detection accuracy from an average of 63% to 83%. In conclusion, the multi-sensor fusion algorithm can be applied to increase the accuracy and reliability of AI-based fault diagnosis.

It is becoming increasingly difficult to ignore the importance of equipment aging in critical industries such as power generation plants, oil and gas plants, etc. since many system components are approaching the end of their designed operational lifetime. New installations of system components in these critical industries will be extremely costly whilst any increase in demand will be too small to warrant totally new facilities. Aging management can be an alternative for this aging equipment to ensure its safety and reliability. This review aims to provide a brief understanding of equipment aging, aging detection and recent research into aging management.

Machinery faults can be detected by various signal processing tools; however, they require human expertise to achieve maximum success. Machine learning tools can help to achieve automatic machinery-faults diagnosis. This paper provides a brief review of the most common machine learning tools.

This paper presents a novel method to diagnose turbine blade faults by analysing wavelet result based on fingerprint recognition method. The study focuses on applying the fingerprint features extraction method to extract faults information from wavelet displays. Fingerprint recognition method studied in this paper is known as fingerprint bifurcations points. Experimental results show that this method could potentially be used as a features recognition method to identify the different types of machinery faults such as rubbing and rotor unbalance.

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.

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.

Blade faults are ranked among the most frequent causes for gas turbine failures. This paper provides a review on the types of blade faults as well as its pertinent detection methods. In this paper, blade faults are categorized into five major groups according to their nature and characteristics namely, blade rubbing, blade fatigue failures, blade deformation, blade fouling, and blade root related problems such as cracked root and loose blade. This paper aims to provide an overview on the characteristics of each type of blade fault as well as its best detection methods available to date.

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.