Condition Monitoring of Epoxy Mica Composite Insulation Used in Rotating Machines Employing Electric Modulus

2021 ◽  
Author(s):  
Nikhil Mondal ◽  
Soumya Chatterjee ◽  
Nasirul Haque ◽  
Sovan Dalai ◽  
Biswendu Chatterjee ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Electric Modulus ◽  
Rotating Machines ◽  
Mica Composite

IoT-Based Condition Monitoring and Automatic Control of Rotating Machines

2021 ◽  
pp. 29-39
Author(s):  
Rubia Ramesh Kumar ◽  
Saloni Sharma ◽  
Priyank Mehra ◽  
V. Berlin Hency ◽  
O.V. Gnana Swathika
Keyword(s):  
Automatic Control ◽  
Condition Monitoring ◽  
Rotating Machines

scholarly journals Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks

Machines ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 59 ◽  
Author(s):  
Kenisuomo C. Luwei ◽  
Akilu Yunusa-Kaltungo ◽  
Yusuf A. Sha’aban
Keyword(s):  
Data Fusion ◽  
Condition Monitoring ◽  
Real Life ◽  
General Concept ◽  
Rotating Machines ◽  
Classification Approach ◽  
Vibration Data ◽  
Artificial Neural ◽  
Machine Health ◽  
Artificial Neural Network Ann

The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount of time is taken to accurately detect and classify rotor-related anomalies which often negate the drive to achieve a truly robust maintenance decision-making system. Earlier studies have attempted to address these limitations by classifying the poly coherent composite spectra (pCCS) features generated at different machine speeds using principal components analysis (PCA). As valuable as the observations obtained were, the PCA-based classifications applied are linear which may or may not limit their applicability to some real-life machine vibration data that are often associated with certain degrees of non-linearities due to faults. Additionally, the PCA-based faults classification approach used in earlier studies sometimes lack the capability to self-learn which implies that routine machine health classifications would be done manually. The initial parts of the current paper were presented in the form of a thorough search of the literature related to the general concept of data fusion approaches in condition monitoring (CM) of rotation machines. Based on the potentials of pCCS features, the later parts of the article are concerned with the application of the same features for the exploration of a simplified two-staged artificial neural network (ANN) classification approach that could pave the way for the automatic classification of rotating machines faults. This preliminary examination of the classification accuracies of the networks at both stages of the algorithm offered encouraging results, as well as indicates a promising potential for this enhanced approach during field-based condition monitoring of critical rotating machines.

Integrated Approach for Vibration-Based Condition Monitoring of Rotating Machines

2015 ◽  
Author(s):  
Jyoti K. Sinha
Keyword(s):  
Condition Monitoring ◽  
Experimental Studies ◽  
Integrated Approach ◽  
Rotating Machines ◽  
Rotating Machine ◽  
Research Studies

Conventional Vibration-based Condition Monitoring (VCM) is well known and well accepted in industries to identify the fault(s), if any, in rotating machine since decades. However over the last 3 decades, significant advancement in both computational and instrumentation technologies has been noticed which resulted in number of research studies to find the alternate and efficient methods for fault(s) diagnosis. But most of the research studies may not be leading to an Integrated Modern VCM (IMVCM). It may be because of mainly 2 reasons; (a) the recent proposed methods in the literature are based on numerically simulated studies and a very limited experimental studies and (b) none of the recent studies applied on all kind of faults. In this paper, a summary of a couple of methods proposed and published earlier by author to meet the requirement of the IMVCM is presented.

scholarly journals Intelligent Condition Monitoring Techniques for Early Fault Diagnosis of Rotating Machines – A Review

2021 ◽  
Vol 23 (08) ◽  
pp. 549-566
Author(s):  
Vinod Kumar ◽  
◽  
S.S Dhami ◽  
Deepam Goyal ◽  
◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Condition Based Maintenance ◽  
Current Status ◽  
Sudden Increase ◽  
Rotating Machines ◽  
Monitoring Techniques ◽  
High Speeds ◽  
Production Safety ◽  
Catastrophic Failures ◽  
Future Condition

Condition-based maintenance is always an important strategy of maintenance to prolong the effective life of rotating machines as they run on high speeds with a variety of loads in some cases under severe conditions. If the monitoring of the current condition is not done accurately then rotating machines such as turbines, engine, bearing, shafts, gearbox, motors, and compressors leads to catastrophic failures with some serious consequences on the rate of production, safety, loss of manpower and sudden increase in repairing cost. Condition-based maintenance is also be called predictive type maintenance is a far superior technique as compared to preventive maintenance and run-to-break maintenance. In predictive maintenance current status of the machine while in operation, being monitored carefully and based on a brief analysis of the future condition of the machine predicted. This paper going to review the various intelligent condition monitoring techniques developed or used by various researchers to monitor the health of rotating machines and able to predict the faults at the earliest time.

scholarly journals Orthogonal On-Rotor Sensing Vibrations for Condition Monitoring of Rotating Machines

2021 ◽  
Author(s):  
Yuandong Xu ◽  
◽  
Xiaoli Tang ◽  
Guojin Feng ◽  
Dong Wang ◽  
...  
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Signal To Noise Ratio ◽  
Fault Detection And Diagnosis ◽  
Vibration Measurement ◽  
High Signal ◽  
Rotating Machines ◽  
Signal To Noise ◽  
Mems Accelerometer ◽  
Noise Ratio

Thanks to the fast development of Micro-Electro-Mechanical Systems (MEMS) technologies, MEMS accelerometers show great potentialities for machine condition monitoring. To overcome the problems of a poor signal to noise ratio, complicated modulation, and high costs of vibration measurement and computation using conventional Integrated Electronics Piezo-Electric (IEPE) accelerometers, a triaxial MEMS accelerometer based On-Rotor Sensing (ORS) technology was developed in this study. With wireless data transmission capability, the ORS unit can be mounted on a rotating rotor to obtain both rotational and transverse dynamics of the rotor with a high signal to noise ratio. The orthogonal outputs lead to a construction method of analytic signals in the time domain, which is versatile in fault detection and diagnosis of rotating machines. Two case studies based on an induction motor were carried out, which demonstrated that incipient bearing defect and half broken rotor bar can be effectively diagnosed by the proposed measurement and analysis methods. Comparatively, vibration signals from transitional on-casing accelerometers are less capable of detecting such faults. This demonstrates the superiority of the ORS vibrations in fault detection of rotating machines.

scholarly journals The Architecture of Rotating Machines Condition-Monitoring Using Model-Based System Engineering Approach

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Guerroum Mariya ◽  
Zegrari Mourad ◽  
Elmahjoub Abdelhafid Ait ◽  
Alaoui Ali El ◽  
Saadi Janah ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
System Engineering ◽  
Rotating Machines ◽  
Engineering Approach ◽  
Model Based
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