scholarly journals Innovative Strategy for Addressing the Challenges of Monitoring Off-Shore Wind Turbines for Condition-Based Maintenance

2021 ◽  
pp. 189-196
Author(s):  
Amin Al-Habaibeh ◽  
Ampea Boateng ◽  
Hyunjoo Lee
Keyword(s):  
Wind Energy ◽  
Condition Monitoring ◽  
Low Frequency ◽  
Weather Conditions ◽  
Condition Based Maintenance ◽  
Monitoring Strategy ◽  
Innovative Strategy ◽  
Sensory Data ◽  
Condition Monitoring System ◽  
Event Based

AbstractOff-shore wind energy technology is considered to be one of the most important renewable energy source in the 21st century towards reducing carbon emission and providing the electricity needed to power our cities. However, due to being installed away from the shore, ensuring availability and performing maintenance procedures could be an expensive and time consuming task. Condition Based Maintenance (CBM) could play an important role in enhancing the payback period on investment and avoiding unexpected failures that could reduce the available capacity and increase maintenance costs. Due to being at distance from the shore, it is difficult to transfer high frequency data in real time and because of this data transferring issue, only low frequency-average SCADA data (Supervisory Control And Data Acquisition) is available for condition monitoring. Another problem when monitoring wind energy is the massive variation in weather conditions (e.g. wind speed and direction), which could produce a wide range of operational alerts and warnings. This paper presents a novel case study of integrated event-based wind turbine alerts with time-based sensory data from the SCADA system to perform a condition monitoring strategy to categorise health conditions. The initial results presented in this paper, using vibration levels of the drive train, indicate that the suggested monitoring strategy could be implemented to develop an effective condition monitoring system.

Condition Monitoring of Lift Gear Boxes by Means of Sound Analysis

Volume 3 ◽  
2004 ◽  
Author(s):  
Mustafa Ozkirim ◽  
C. Erdem Imrak ◽  
Hakan Uzunoglu
Keyword(s):  
Condition Monitoring ◽  
Condition Based Maintenance ◽  
Oil Analysis ◽  
Electric Motors ◽  
Effective Technique ◽  
Sound Analysis ◽  
Fault Monitoring ◽  
Worm Gears ◽  
Potential Failure ◽  
Condition Monitoring System

The method of Condition Monitoring (CM) or Condition Based Maintenance (CBM) of machinery is straightforward since it aims to identify the changes in the condition of a machine during operation that will indicate some potential failure. This is achieved by utilizing various techniques such as Thermography, Oil Analysis and Ultrasonics. For all maintenance engineers’ diagnosis of gear defects by using sound analysis may be an effective technique in that it provides economic and continuous fault monitoring. In the study, in order to detect the defects in worm gears driven by electric motors, the samples of the sound vibrations emitted from the gears with and without defects are recorded by means of a sound recorder. The comparison of these sound records proved that the acoustic condition monitoring system is able to detect the faulty gears of the elevator drive units.

Utilization of CMS (Condition Monitoring System) for CBM (Condition-Based Maintenance)

2009 ◽  
Author(s):  
Hidetsugu Morota ◽  
Toshimitsu Miyazono ◽  
Tatsuro Okumura
Keyword(s):  
Performance Analysis ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Condition Based Maintenance ◽  
Plant Performance ◽  
Utilization Rate ◽  
Proof Of Concept ◽  
New Approach ◽  
Power Facility ◽  
Condition Monitoring System

CBM (Condition-Based Maintenance) is highly effective to make improvement utilization rate of the electric power facility. In case of CBM, it is often the case that vibration diagnosis for rotating components is direct measurement. CMS (Condition Monitoring System), which is tool for the plant performance analysis and diagnose component fault based on heat and mass balance calculations, is beneficial tool for CBM to proceed more effectively. The work is a suggestion of a new approach for CBM using CMS, provides proof-of-concept demonstration of effectiveness of a foreword looking approach to diagnostic and prognostic technology and shows effectiveness of CMS for CBM including application results.

Online Real-Time Monitoring System Through Using Adaptive Angular-Velocity VKF Order Tracking

2012 ◽  
Author(s):  
Ting-Chi Yeh ◽  
Min-Chun Pan
Keyword(s):  
Angular Velocity ◽  
Real Time ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Theoretical Derivation ◽  
Periodic Loading ◽  
Order Tracking ◽  
Computation Scheme ◽  
Condition Monitoring System ◽  
Operation Status

When rotary machines are running, acousto-mechanical signals acquired from the machines are able to reveal their operation status and machine conditions. Mechanical systems under periodic loading due to rotary operation usually respond in measurements with a superposition of sinusoids whose frequencies are integer (or fractional integer) multiples of the reference shaft speed. In this study we built an online real-time machine condition monitoring system based on the adaptive angular-velocity Vold-Kalman filtering order tracking (AV2KF_OT) algorithm, which was implemented through a DSP chip module and a user interface coded by the LabVIEW®. This paper briefly introduces the theoretical derivation and numerical implementation of computation scheme. Experimental works justify the effectiveness of applying the developed online real-time condition monitoring system. They are the detection of startup on the fluid-induced instability, whirl, performed by using a journal-bearing rotor test rig.

scholarly journals Intelligent Starting Current-Based Fault Identification of an Induction Motor Operating under Various Power Quality Issues

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 304
Author(s):  
Sakthivel Ganesan ◽  
Prince Winston David ◽  
Praveen Kumar Balachandran ◽  
Devakirubakaran Samithas
Keyword(s):  
Standard Deviation ◽  
Power Quality ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Classification Accuracy ◽  
Induction Motors ◽  
Quality Data ◽  
Starting Current ◽  
Quality Issues ◽  
Condition Monitoring System

Since most of our industries use induction motors, it is essential to develop condition monitoring systems. Nowadays, industries have power quality issues such as sag, swell, harmonics, and transients. Thus, a condition monitoring system should have the ability to detect various faults, even in the presence of power quality issues. Most of the fault diagnosis and condition monitoring methods proposed earlier misidentified the faults and caused the condition monitoring system to fail because of misclassification due to power quality. The proposed method uses power quality data along with starting current data to identify the broken rotor bar and bearing fault in induction motors. The discrete wavelet transform (DWT) is used to decompose the current waveform, and then different features such as mean, standard deviation, entropy, and norm are calculated. The neural network (NN) classifier is used for classifying the faults and for analyzing the classification accuracy for various cases. The classification accuracy is 96.7% while considering power quality issues, whereas in a typical case, it is 93.3%. The proposed methodology is suitable for hardware implementation, which merges mean, standard deviation, entropy, and norm with the consideration of power quality issues, and the trained NN proves stable in the detection of the rotor and bearing faults.

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