scholarly journals Condition Monitoring of Blade in Turbomachinery: A Review

2014 ◽  
Vol 6 ◽  
pp. 210717 ◽  
Author(s):  
Ahmed M. Abdelrhman ◽  
Lim Meng Hee ◽  
M. S. Leong ◽  
Salah Al-Obaidi
Keyword(s):  
Condition Monitoring ◽  
Fatigue Failure ◽  
Signal Analysis ◽  
Health Condition ◽  
Diagnostic Methods ◽  
Monitoring Techniques ◽  
Analysis Methods

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.

scholarly journals Vibration Condition Monitoring: Latest Trend and Review

2015 ◽  
Vol 773-774 ◽  
pp. 139-143
Author(s):  
K.H. Hui ◽  
L.M. Hee ◽  
M. Salman Leong ◽  
Ahmed M. Abdelrhman
Keyword(s):  
Decision Making ◽  
Fourier Transform ◽  
Condition Monitoring ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Machine Learning Techniques ◽  
Vibration Condition ◽  
The Past ◽  
Analysis Methods ◽  
Vibration Signal Analysis

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.

Steady State Detection in Industrial Gas Turbines for Condition Monitoring and Diagnostics Applications

2014 ◽  
Author(s):  
Cesar Celis ◽  
Érica Xavier ◽  
Tairo Teixeira ◽  
Gustavo R. S. Pinto
Keyword(s):  
Steady State ◽  
Condition Monitoring ◽  
Gas Turbines ◽  
Signal Analysis ◽  
Operating Regime ◽  
State Detection ◽  
Industrial Gas Turbines ◽  
Monitoring And Diagnostics ◽  
Operating Regimes

This work describes the development and implementation of a signal analysis module which allows the reliable detection of operating regimes in industrial gas turbines. Its use is intended for steady state-based condition monitoring and diagnostics systems. This type of systems requires the determination of the operating regime of the equipment, in this particular case, of the industrial gas turbine. After a brief introduction the context in which the signal analysis module is developed is highlighted. Next the state of the art of the different methodologies used for steady state detection in equipment is summarized. A detailed description of the signal analysis module developed, including its different sub systems and the main hypotheses considered during its development, is shown to follow. Finally the main results obtained through the use of the module developed are presented and discussed. The results obtained emphasize the adequacy of this type of procedures for the determination of operating regimes in industrial gas turbines.

Automatic Condition Monitoring and Early Fault Detection of Gearbox

2009 ◽  
Author(s):  
Dong Wang ◽  
Qiang Miao ◽  
Chengdong Wang ◽  
Jingqi Xiong
Keyword(s):  
Condition Monitoring ◽  
Wavelet Packet ◽  
Life Time ◽  
Health Condition ◽  
Evaluation Index ◽  
Dynamic Threshold ◽  
Time Data ◽  
Machinery Condition Monitoring ◽  
Maintenance Program ◽  
Gear Fault

Condition based maintenance (CBM) improves decision-making performances for a maintenance program through machinery condition monitoring. Therefore, it is a key step to trace machinery health condition for CBM. In this paper, a novel method is proposed to establish a health evaluation index named automatic evaluation index (AEI) and its corresponding dynamic threshold using Wavelet Packet Transform (WPT) and Hidden Markolv Model (HMM). In this process, WPT is used to decompose signal into detail signals and exhibits prominent gear fault features. In addition, HMM employed here is to recognize two concerned states of gear in the whole life validation, including normal gear state and early gear fault state. It is also important to build a dynamic threshold to differentiate the two states automatically. The proposed dynamic threshold not only renews by itself according to the history values of AEI but also easily and automatically detects occurrence of gear early fault. Finally, a set of whole life time data ending in gear failure is used to verify the proposed method effectively. Further, some related parameters included in this method are discussed and the obtained results show that condition monitoring performance of the proposed method is excellent in detection of gear failure.

Modeling and Parameter Identification for Condition Monitoring of Surface-Mount Permanent Magnet Machines Under Magnet Demagnetization

2020 ◽  
Author(s):  
Fanny Pinto Delgado ◽  
Ziyou Song ◽  
Heath F. Hofmann ◽  
Jing Sun
Keyword(s):  
Permanent Magnet ◽  
Condition Monitoring ◽  
High Performance ◽  
High Efficiency ◽  
Health Condition ◽  
Maintenance Cost ◽  
Parameter Estimator ◽  
Two Phase ◽  
Machine Model ◽  
Surface Mount

Abstract Permanent Magnet Synchronous Machines (PMSMs) have been preferred for high-performance applications due to their high torque density, high power density, high control accuracy, and high efficiency over a wide operating range. During operation, monitoring the PMSM’s health condition is crucial for detecting any anomalies so that performance degradation, maintenance/downtime costs, and safety hazards can be avoided. In particular, demagnetization of PMSMs can lead to not only degraded performance but also high maintenance cost as they are the most expensive components in a PMSM. In this paper, an equivalent two-phase model for surface-mount permanent magnet (SMPM) machines under permanent magnet demagnetization is formulated and a parameter estimator is proposed for condition monitoring purposes. The performance of the proposed estimator is investigated through analysis and simulation under different conditions, and compared with a parameter estimator based on the standard SMPM machine model. In terms of information that can be extracted for fault diagnosis and condition monitoring, the proposed estimator exhibits advantages over the standard-model-based estimator as it can differentiate between uniform demagnetization over all poles and asymmetric demagnetization between north and south poles.

Experience with advanced in-service condition monitoring techniques for GIS and transformers

1998 ◽  
Author(s):  
J. Pearson ◽  
B.F. Hampton ◽  
M.D. Judd ◽  
B. Pryor ◽  
P.F. Coventry
Keyword(s):  
Condition Monitoring ◽  
Service Condition ◽  
Monitoring Techniques
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