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

Abdelrhman, Ahmed M.; Leong, M. Salman; Saeed, Somia Alfatih M.; Obiadi, Salah M. Ali Al-Obiadi Al

doi:10.4028/www.scientific.net/amm.229-231.1459

Cited by 22 publications

(10 citation statements)

References 24 publications

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“…For the frequency analysis, the most common technique is the frequency spectrum analysis technique. This means the conversion of the vibration signals from time domain into the frequency domain [84]. By analysing these frequencies, the location and failure types can be detected.…”

Section: Hot Gas Component Monitoringmentioning

confidence: 99%

A Review of NDT/Structural Health Monitoring Techniques for Hot Gas Components in Gas Turbines

Mevissen

Meo

2019

Sensors

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The need for non-destructive testing/structural health monitoring (SHM) is becoming increasingly important for gas turbine manufacturers. Incipient cracks have to be detected before catastrophic events occur. With respect to condition-based maintenance, the complex and expensive parts should be used as long as their performance or integrity is not compromised. In this study, the main failure modes of turbines are reported. In particular, we focus on the turbine blades, turbine vanes and the transition ducts of the combustion chambers. The existing monitoring techniques for these components, with their own particular advantages and disadvantages, are summarised in this review. In addition to the vibrational approach, tip timing technology is the most used technique for blade monitoring. Several sensor types are appropriate for the extreme conditions in a gas turbine, but besides tip timing, other technologies are also very promising for future NDT/SHM applications. For static parts, like turbine vanes and the transition ducts of the combustion chambers, different monitoring possibilities are identified and discussed.

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Section: Hot Gas Component Monitoringmentioning

confidence: 99%

A Review of NDT/Structural Health Monitoring Techniques for Hot Gas Components in Gas Turbines

Mevissen

Meo

2019

Sensors

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“…Houxi et al [11] employed the information entropy and support vector machine (SVM) method to for valve fault diagnosis in reciprocating compressors. Many studies llustrated that AE technique can detect the fault in the initial stage at lower speed while conventional vibration technique is not able [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Automated Valve Fault Detection Based on Acoustic Emission Parameters and Artificial Neural Network

et al. 2019

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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.

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“…Nevertheless, in propelled rotating machines, the most common on field-operating system fault is due to a damage on the propeller (or blade) body, see for instance [Shajiee et al, 2014;Oberholster and Heyns, 2006;Abdelrhman et al, 2013;Abdelrhman et al, 2012;Zeng et al, 2019]. Even when there exit many statistical methods on fault diagnosis based on data analysis, such as deep learning [Helbing and Ritter, 2018], Kalman filtering theory [Cho et al, 2018], bi-stable stochastic resonance theory [He et al, 2019], fuzzy logic and intelligent systems [Kuo, 1995], etcetera, we can still invoke fundamental statistical tools, like box-plot diagrams, for fault diagnosis to propeller rotating machines.…”

Section: Introductionmentioning

confidence: 99%

An experimental platform for statistical fault diagnosis in propelled mechanical systems: an academic exercise

Acho

Caballero

2019

CAP

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Statistical data analysis for fault diagnosis in mechanical systems is a fundamental tool, for instance, in applied mechanical engineering. In order to capture a feasible data set, a well designed electronic instrumentation and excitation system signal stages are mandatory. Hence, one objective of this paper is to develop a lowcost vibration sensor based on an inductive LC-tank oscillator (a resonant inductive-capacitive electronic circuit carefully designed to produce an harmonic electrical signal), and then to tune an effective excitation system signal to our experimental platform. This platform uses a propelled drone motor mounted on a beam structure to emulate a propelled rotating machine. Essentially, two data set were acquired. One for the healthy behaviour of the developed system, and the other for a programmed faulty scenario. This defective case was realized by introducing a small mechanical fault in one blade extreme of the mechanical propelled system. To note, this faulty scenario is almost impossible to de-duce by just seen the raw data. The other objective of this paper is to analyze the obtained data sets by utilizing a statistical data analysis tool. Then, by employing box-plot diagrams, the healthy and faulty cases become evidenced. Finally, and due to we are proposing a low-cost academic experimental platform for fault diagnosis based on data analysis, our platform's toll was around 120 euros. Hence, this platform results applicable to teach data analysis from dynamical systems.

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

Cited by 22 publications

References 24 publications

A Review of NDT/Structural Health Monitoring Techniques for Hot Gas Components in Gas Turbines

A Review of NDT/Structural Health Monitoring Techniques for Hot Gas Components in Gas Turbines

Automated Valve Fault Detection Based on Acoustic Emission Parameters and Artificial Neural Network

An experimental platform for statistical fault diagnosis in propelled mechanical systems: an academic exercise

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