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عدد المساهمات : 18992 التقييم : 35482 تاريخ التسجيل : 01/07/2009 الدولة : مصر العمل : مدير منتدى هندسة الإنتاج والتصميم الميكانيكى
| موضوع: بحث بعنوان Fault Diagnosis in Rotating Systems Based on Vibration Analysis الخميس 19 مايو 2022, 8:39 am | |
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أخواني في الله أحضرت لكم بحث بعنوان Fault Diagnosis in Rotating Systems Based on Vibration Analysis Mohamed B. Abd-Elbary, Ahmed G. Embaby and Fawkia R. Gomaa Department of Production Engineering and Mechanical Design, Shebin Alkoum Faculty of Eng., Menoufia University, Egypt. (Corresponding author: e.mohamed.badr@gmail.com)
و المحتوى كما يلي :
ABSTRACT Vibration is one of the major parameters to consider in condition monitoring of rotating systems. If an undetected fault is noticed in the rotating system, then, at best, the issue will not be too significant and can be remedied cheaply and quickly; at worst case, it may result in down-time, expensive damage, injury, or even life loss, therefore early fault identification is a critical factor in ensuring and extending the working life of the rotating systems. By measurement and analysis of the vibration of rotating machinery, it is possible to detect and locate important faults such as mass unbalance, misalignment, bearing failure, gear faults and rotor cracks. This article is aimed to guide the researchers to implement identification, diagnosis and remedy techniques of common fault types using vibration analysis and outlines many important techniques used for condition monitoring of rotating systems such as fast Fourier transform, frequency domain decomposition method, wavelet transform, stochastic subspace identification and deep learning. Keywords: fault diagnosis; vibration analysis; rotating system. Conclusion Vibration experts and developers have done great efforts to create functions that solve the few limitations of vibration analysis, however, there are still some issues that we are unable to see through vibration analysis such as; Very High frequency: Common sensors have a maximum frequency of 10 to 15 kHz. If one does not invest in special sensors, higher frequencies will be invisible to the equipment. Ultra-low frequencies: Although it is possible to measure very low frequencies, they are often ignored because they require long samples which are not done in a normal route. Lubricant condition: This is one of the biggest limitations of vibration analysis. The condition of the lubricant cannot be evaluated by this technique, you can only suspect the lack of it. Many vibration analysis techniques are presented to explore their capabilities, advantages, and disadvantage in diagnosing and monitoring rotating systems. The following points can be concluded: 1. The identification of the bearing faults by using frequency analysis is difficult because, it is not suitable for non-stationary signal analysis. 2. The identification of the bearing faults is possible by using envelope analysis. However, the envelope analysis has a major drawback consisting of the requirement of a preliminary research of the resonance frequencies. 3. The identification of the bearing faults is possible by using Short Time Fourier Transform (STFT). However, the problem with STFT is that it provides constant resolution for all frequencies since it uses the same window for the entire signal. Therefore, once the window function is chosen, the time and frequency resolution are fixed. So, there is a trade‐off to choose a proper window function between the time resolution and the frequency resolution: a longer window will lead to a higher frequency resolution with a lower time frequency and vice versa. 4. The identification of the bearing faults is possible by using Empirical Mode Decomposition (EMD). Unfortunately, there are two problems in EMD, which are the selection of the suitable decomposition level and its intrinsic mode functions (IMF) which contains the necessary information for faults diagnosis. 5. Deep learning for fault diagnosis had been paid less attention. Because of these difficulties:(1) for images, the characteristics of recognition objects are relatively fixed, but faults are changeable, such as patterns variability and shape variability;(2) as fault has no fixed pattern, whether deep learning can capture a useful "hierarchical grouping" or" part-whole decomposition" of the fault data is unknown; (3) the detection mechanism and ability based on deep learning is not yet well explored, especially for the incipient faults not any observable changes, which is a bottle neck that traditional methods suffering.
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