Industrial Engineering

Experimental study on the rheological characteristics and viscosity-enhanced factors of super-viscous heavy oil
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Research Article
Experimental study on the rheological characteristics and viscosity-enhanced factors of super-viscous heavy oil
By Yang Chen, Jin Luo, Meiyu Zhang, Minglan He
To reveal the viscosity-enhanced mechanism of super-viscous heavy oil and improve the recovery rate of super-viscous heavy oil, the four components, elemental composition, rheological properties, and effects of asphaltenes and resin on the viscosity of super-viscous heavy oil from well TH12434 in Tahe Oilfield, China have been analyzed from macro and microscopic perspectives by Anton Paar rotational rheometer, gas chromatography-mass spectrometry and scanning cryo-EM to solve the problems of poor fluidity and high asphaltene content. The experimental results showed that in the temperature range of T= 40-100°C, the viscosity of super-viscous heavy oil decreases sharply from 352000 mPa∙s to 1620 mPa∙s, and the super-viscous heavy oil exhibits clear thermo-sensitivity. With T= 100°C and shear rate ranging from γ= 0-800 s-1, the viscosity of super-viscous heavy oil decreases sharply from 45000 mPa∙s to 956 mPa∙s, and the oil sample shows typical pseudoplasticity. The baseline of super-viscous heavy oil analysis by gas chromatography shows too high, and more than 80 % of super-viscous heavy oil compounds have a matching degree of less than 70 % with standard compounds, indicating that the super-viscous heavy oil had poor heterogeneity and many impurities. It is observed by scanning cryo-EM that the micromorphology of super-viscous heavy oil is large granular, strong continuity, asphaltene micromorphology presents an obvious layered structure, the layer spacing is 637.7 nm, and its asphaltene molecules form an order-like or crystal-like association structure through several unit sheet layers, resulting in high viscosity of super-viscous heavy oil. Based on the analysis results of the influencing factors of the viscosity of super-viscous heavy oil, a theoretical basis for the selection of viscosity reduction technology for super-viscous heavy oil the efficient exploitation in Tahe Oilfield, China could be provided.
November 7, 2023
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By Youming Wang, Zhao Xiao, Gongqing Cao
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Fault diagnosis and health management of bearings in rotating equipment based on vibration analysis – a review
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A review on wind turbines gearbox fault diagnosis methods
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Applied Mathematics

Maintenance, Reliability and Condition Monitoring

Classification of a cracked-rotor system during start-up using Deep learning based on convolutional neural networks
Research Article
Classification of a cracked-rotor system during start-up using Deep learning based on convolutional neural networks
This article addresses an improvement of a classification procedure on cracked rotors through Deep learning based on convolutional neural networks (CNNs). At first, a cracked rotor-bearing system is modeled by the finite element method (FEM), then throughout its start-up, the related time-domain responses are calculated numerically. In the following, as a pre-processing stage, continuous wavelet transform (CWT) and Short-time Fourier transform (STFT) are applied on the three various health conditions, i.e. without crack, shallow-cracked, and relatively deep-cracked shafts. The plots of CWT’s coefficients and STFT’s in these various classes are used as the input dataset in Deep learning based on CNNs and the three classes are introduced as the output. AlexNet with 25 layers is employed as the network. The results of the testing phase demonstrated that not only this expanded method has a reasonable capacity in the classification of cracked and healthy rotors, but it also can classify cracked rotors with different crack depths with a negligible error.
December 15, 2021
Applied Mathematics
Robust vibration-based faults diagnosis machine learning model for rotating machines to enhance plant reliability
Research Article
Robust vibration-based faults diagnosis machine learning model for rotating machines to enhance plant reliability
Plant availability and reliability can be improved through a robust condition monitoring and fault diagnosis model to predict the current status (healthy or faulty) of any machines and critical assets. The model can then predict the exact fault for the faulty asset so that remedial maintenance can be carried out in a planned plant outage. Nowadays, the artificial intelligence (AI)-based machine learning (ML) model seems to be current trend to meet these requirements. Hence, the paper is also proposing such vibration-based faults diagnosis ML model through an experimental rotating rig. Here, the 2-Steps approach is used with the ML model to easy the industrial operation and maintenance process. The Step-1 provides the information about the asset health status such as healthy or faulty. The Step-2 then identifies the exact nature of fault to aid the decision making for the fault rectification and maintenance activities to avoid the risk of failure and enhance the reliability.
June 30, 2021
Applied Mathematics
A numerical study of rotor eccentricity and dynamic load in induction machines for motor current analysis based diagnostics
Research Article
A numerical study of rotor eccentricity and dynamic load in induction machines for motor current analysis based diagnostics
The asymmetry in the manufacturing and assembling is the common issue of rotor systems. Different degrees of errors are inevitable in alternating current (AC) motors, which causes degraded performances. Furthermore, around 80 % of mechanical faults link to rotor eccentricity. The eccentricity faults (EFs) generate excessive mechanical stress and then lead to fatigue in the other parts of the motor. Motor current signal analysis (MCSA) can be used to diagnose induction machine (IM) faults. As the EF leads to an unequal air gap when the rotor rotates, the inductance of IM also responds to the EF. Moreover, the dynamic load is a typical situation due to residual dynamic unbalance and misalignment. To study how EFs and dynamic load affect the stator current. The current model of symmetrical motor, asymmetrical motors with three-level EFs and with dynamic load are investigated numerically. The correctness of models is verified through experimental study. The results show the level of EF affects the sideband peak values significantly in the stator current spectrum. These findings will provide a foundation for the accurate diagnosis of motor health conditions.
August 6, 2021
Applied Mathematics
Crack identification for bridge condition monitoring using deep convolutional networks trained with a feedback-update strategy
Research Article
Crack identification for bridge condition monitoring using deep convolutional networks trained with a feedback-update strategy
Orthotropic steel bridge decks and steel box girders are key structures of long-span bridges. Fatigue cracks often occur in these structures due to coupled factors of initial material flaws and dynamic vehicle loads, which drives the need for automating crack identification for bridge condition monitoring. With the use of unmanned aerial vehicle (UAV), the acquirement of bridge surface pictures is convenient, which facilitates the development of vision-based bridge condition monitoring. In this study, a combination of convolutional neural network (CNN) with fully convolutional network (FCN) is designed for crack identification and bridge condition monitoring. Firstly, 120 images are cropped into small patches to create a basic dataset. Subsequently, CNN and FCN models are trained for patch classification and pixel-level crack segmentation, respectively. In patch classification, some non-crack patches that contain complicated disturbance information, such as handwriting and shadow, are often mistakenly identified as cracks by directly using the CNN model. To address this problem, we propose a feedback-update strategy for CNN training, in which mistaken classification results of non-crack data are selected to update the training set to generate a new CNN model. By that analogy, several different CNN models are obtained and the accuracy of patch classification could be improved by using all models together. Finally, 80 test images are processed by the feedback-update CNN models and FCN model with a sliding window technique to generate crack identification results. Intersection over union (IoU) is calculated as an index to quantificationally evaluate the accuracy of the proposed method.
August 6, 2021
Applied Mathematics
Maintenance, Reliability and Condition Monitoring

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