92 research outputs found
TOWARDS AN EFFICIENT HEALTH CONDITION MONITORING STRATEGY APPLIED FOR GEARING MECHANICAL SYSTEMS
No full textCracked teeth are a common phenomenon for gears. The dynamic behavior of gears in the presence of cracks and poor lubrication conditions that lead to rubbing between teeth are not well understood, which limits the development of precision and reliability of gear transmission. In this research, a comprehensive model is proposed to study the dynamic behavior of a one-stage spur gearbox with cracked teeth in a condition of poor lubrication. The inter-teeth rubbing generated from the sliding of one surface relative to another is caused by the variation of curvature radii of both mating teeth when the contact point is moving along the line of contact. Under the transmitted loading between mating teeth, the common elastic deformed area, computed by Hertz theory, keeps varying, generating a combination of modulated and constant amplitudes noises. Adding this friction-induced vibration to the impulsive periodic response is found to realistically mimic the actual behavior experimentally measured on a test rig specially developed for this investigation. Based on time-domain statistical indicators, the study concluded that the combination of both components of friction-induced noise with the primary impacting response was found to accurately and realistically simulate the dynamic behavior of the gearbox. Afterward, the experimental setup utilized in this investigation was modeled numerically in the initial part of the research to get a variety of operating circumstances. Those results are included in the dataset used in this work.Getting an adequate generalization capability by training traditional, deep 2-dimensional (2D) CNNs is a time-consuming operation, and this often requires massive datasets, exponentially increasing the processing challenges. Conversely, 1D CNNs have been suggested for use in various 1D signal processing tasks, such as gear condition monitoring. Recently developed methods aim to eliminate these drawbacks by focusing just on 1D signals, where they perform much better. This research addresses the challenge of failure identification in gears. It provides a solution based on combining raw and residual vibration data with a convolutional neural network (CNN) and recurrent neural network (LSTM). The described technique begins by training a one-dimensional convolutional neural network (CNN) using the raw vibration signals and then a long short-term memory (LSTM) network with the remaining vibration signals. Next, a deep learning structure for gear teeth fault identification is built by combining the two networks' findings on gear teeth fault characteristics. The provided approach is put through its paces by being applied to 41 unique gear fracture circumstances. The gear crack fault diagnostic result demonstrates that the provided technique achieves an accuracy of over 93% with little training data. The suggested approach provides more precise diagnostic outcomes than either the CNN or LSTM network alone. The reliability of the provided technique for gear fracture defect identification is shown by contrasting the results obtained using different sample sizes and methods. Further, a novel method for gear defect classification that integrates time-frequency analysis with image processing is proposed. This method may identify and categorize gear defects using the vibrating signals induced by cracked gear teeth. Empirical Mode Decomposition (EMD) and Principal Components Analysis (PCA) are used to deconstruct the signals into their principal components. To visualize the time- frequency connection of the primary components of the studied signal, the Short-Time Fourier Transform (STFT) is used to create the spectrogram for each element. Further, spectrogram pictures of primary components are converted into an array of features for each signal by extracting Image Moments. Then, a deep machine learning approach called a 2-dimensional convolutional neural network is used to achieve the classification (2-D CNN) utilizing image processing. As shown by the findings, the established method provides reliable classification, and the given deep structure can be readily expanded to include more sensor input signals for future gear crack failure diagnostics
Integrated Machine Learning Approaches for Comprehensive Bearing Health Monitoring and Fault Classification Using Multi-Sensory Data
No full textModern industries heavily rely on machines equipped with rolling-element (RE) bearings. However, these machines face substantial risks due to potential bearing faults, where even minor defects can lead to catastrophic failures. Shockingly, statistics reveal that up to 40-51%of induction motor failures can be attributed to bearing damage. Early fault detection through Condition Monitoring is therefore crucial. Over the last two decades, various machine learning (ML) techniques have been explored to detect defects in rolling element bearings. This research project focuses on optimizing ball-bearing fault detection through diverse ML techniques, supported by comprehensive experimental work.
The experimental work entails multiple steps, including the preparation of a varied set of bearings and the enhancement of equipment with advanced sensors. These experiments resulted in a rich dataset comprising vibrations, currents, and sound – vital for ML model training. The experiments spanned two different machines, incorporating variations in speed and applied forces. Analysis of the experimental data unveiled the significant influence of defect shapes on bearing responses. Notably, at lower speeds, defect shapes prominently affected vibrations, with rectangular defects displaying logarithmic growth while circular defects exhibited exponential behavior. With increasing speed, the behavior of bearings with different defect shapes tended to converge. Current emerged as a robust choice for fault detection, maintaining consistent behavior regardless of defect size and shape, while sound closely resembled vibrations, with slight variations in the case of circular defective bearings.
The second part of this research is dedicated to developing ML fault detection models. Three models, utilizing ensemble techniques, were created. These models, employing Decision Trees (DT), Random Forest (RF), and XGBoost, achieved prediction accuracies of 82%, 91%, and 92%, respectively. A feature importance analysis identified CRSF and SF as dominant parameters. Furthermore, a sound-to-vibration transformation ML model was introduced. This model, built on a 1D Operational U-Net (Op-UNet) framework, is capable of synthesizing realistic vibration signals from sound measurements across different working conditions, fault types, and severities achieving a striking minimum accuracy of 97%. The models and datasets presented in this research signify a significant advancement in bearing health condition monitoring
MODEL-BASED DIAGNOSTICS OF SIMULTANEOUS TOOTH CRACKS IN SPUR GEARS
No full textThis study aims at developing a numerical model that could be used to simulate the effect of tooth cracks on the vibration behavior of spur gears. Gears are a key component that is widely used in various rotating equipment in order to transmit power and change speed. Any failure of this vital component may cause severe disturbance to production and incur heavy financial losses. The tooth fatigue crack is amongst the most common causes of gear failure. Early detection of tooth cracks is crucial for effective condition-based monitoring and decision making. The scope of this work was widened to include the influence of multiple simultaneous tooth cracks on the time and frequency domain responses at various locations and with different severity levels.
As cracks significantly alter the gear mesh stiffness, a finite element analysis was performed to determine the stiffness variation with respect to the angular position for different combinations of crack lengths. A simplified six degrees of freedom nonlinear lumped parameter model of a one-stage gearbox was developed to simulate the vibration response of faulty spur gears with the consideration of inter-tooth friction. Four different multiple crack scenarios were proposed and studied. The performances of various statistical fault detection indicators were investigated. The vibration simulation results of the gearbox obtained using MATLAB were verified with those stated in the published research articles. It was observed that as the severity of a single crack increased, the values of the time-domain statistical indicators increased, with different rates. However, the number of cracks had an adverse effect on the values of all the performance indicators, except the RMS indicator. The number and amplitude of the sidebands in the frequency spectrum were also utilized to detect the severity of the faults in each scenario. It was observed that, in the case of consecutive tooth cracks, the number of spectrum peaks and the number of cracks were well consistent in the frequency range of 4 to 5 kHz. The main finding of this study was that the peak spectral amplitude is the most sensitive indicator to the number and severity of cracks
EXPERIMENTAL DETECTION OF LOCALIZED SURFACE DEFECTS IN BALL BEARINGS USING VIBRATION ANALYSIS
No full textExperimental Detection of Localized Surface Defects in Ball Bearings Using Vibration Analysis
Bearings play a crucial role in the functioning of rotating machinery. Any failure
of this critical component may cause severe disturbance to production and can lead to
human injures. Condition Monitoring tools are needed to assure the healthy state of
rolling element bearings during the operation. This study aimed at the monitoring of
bearing health condition, based on vibration measurement. For that purpose, an
appropriate test rig was designed, manufactured and tuned to accommodate the test
bearings. In particular, an innovative solution for rapid mounting and dismounting of
bearings on the supporting shaft was tested and used successfully in the first phase. In a
second phase, tiny circular defects were seeded on outer and inner rings of similar
bearings, by using the principle of Electro-Discharge Machining. The defects sizes in this
investigation ranged from 0.35 mm up to 3 mm. All tested bearings had the same testing
conditions. In the last phase of the project, a healthy bearing (without any defect) was
installed on the test rig, and vibration measurements were taken to serve as reference data
later. Each damaged bearing was installed on the test rig, and vibration measurements
were performed again. Several MATLAB codes were used for recording and analyzing the experimental data. The results obtained from this work clearly show that different
parameters could be extracted from time domain and frequency domain. These
parameters were found to be sensitive to the growth of defects sizes in different extent,
which allows the assessment of bearing health condition. In time domain, the most
sensitive parameters were found to be the kurtosis and the peak amplitude. In addition to
the conventional time-domain parameters, which are commonly used by vibration
practitioners, two new time-domain parameters were introduced for the first time in this
research. They were named as SIANA and INTHAR. Both of them demonstrated high
sensitivity to the detection of growth in bearing defects sizes. In the frequency domain,
the second and third harmonics of ball pass frequencies on inner and outer rings were
found to be the most sensitive parameters. In general, the indicators extracted from the
frequency domain seem to be more sensitive than time domain parameters to the
evolution of degradation inside the bearing. The Envelope Detection (ED) was also used
in this study as a possible technique to track the increase of damage extent inside
bearings. Compared with direct spectrum, this approach allowed for better visualization
of BPFO and BPFI. Furthermore, the use of ED was found to filter the electrical
frequencies on the signal, which were hiding the real signature of defects
Numerical Simulation of Dynamic Response For Misalignment In Coupled Shafts
No full textPreceded by unbalance, misalignment is the second most common fault in
rotating machinery. The impact of misalignment fault on equipment can be severe and
may considerably shorten the machine’s lifetime. This dissertation discusses the
unbalance, parallel and angular misalignment forces on rotative machines’ vibration
spectra. Numerical simulation model development is used to obtain the time and
frequency responses of the rotor-coupling-bearing system. The parallel and angular
misalignment response are synchronized with the 1X amplitude of the unbalance
displacement. Moreover, the parallel misalignment fault magnifies the 2X amplitude
while the angular misalignment response is captured at 2X and 4X amplitudes of the
displacement response. Effects of changing the model’s rotational speed, misalignment
level, and coupling type are examined for both parallel and angular misalignments
Effet des imperfections initiales sur la stabilité dynamique et la réponse des plaques rectangulaires
No full textRevue historique -- But et sommaire de la recherche -- Équations temporelles du mouvement -- Définition du problème -- Les équations de base -- Les conditions aux limites -- Solution des équations de base -- Solution asymptotique des équations de mouvement -- Construction de la solution asymptotique en première approximation -- Réponse stationnaire -- Passage de vibrations forcées à vibrations paramétriques
Pole Placement of a Nonlinear Electromagnetic System by the Receptance Method
No full textThis paper presents the problem of pole placement for the control of a nonlinear electromagnetic system using the receptance method. A pair of identical magnets and coils are mathematically modeled to create the nonlinear stiffness in the electromagnetic system. The nonlinear stiffness can be varied by adjusting the input electrical current of the coils. The transfer function of the open-loop nonlinear system is obtained at a low level of excitation, in which the system is weakly nonlinear. By doing so, the evaluation of the mass, spring, and damper matrices, which are generally required, is avoided. In further steps, to show the system's nonlinear behavior, the excitation level is raised and the open-loop receptances are measured at various levels. The nonlinear system's poles are assigned using the linear feedback control method and the Sherman-Morrison formula at various levels of excitation. The system's response is dependent on the amplitude, thus, to get the feedback gains, an iterative approach is required. At various excitation levels and positions of the magnets with respect to the coils, the performance of the nonlinear control has been investigated. When the excitation level varies, feedback control can adapt to the changes in the amplitude and the distance, and the performance of the active control system is well maintained.</p
Model Updating Based on Physics Informed Machine Learning on Welded Stiffened Structure
No full textA stiffened structure is integral to complex structures such as ships. An advantage of the stiffened plate is that it has a greater load-carrying capacity. Therefore, structural health monitoring of stiffened structures is essential in a ship structure. The stiffened structure is modeled as a coupled system with a plate and stiffener forming the subsystems coupled with welded joints. The complex coupled system is modeled using finite element analysis. The coupling springs between a plate and beam could replicate the modal characteristics of welded joints in a stiffened structure. Since the welding characteristics could change depending on the operating conditions, the coupling spring was varied to account for the uncertainty in the weld strength. A hyperspace of coupling springs, one longitudinal and two torsional, was spanned using the Latin hypercube sampling technique following a uniform distribution. The eigenvalue problem for the stiffened structure was solved, and the modal characteristics of the system were determined. Dynamic features of the system, such as natural frequency, mode shape and frequency response function (FRF), were extracted. A metamodel for the system was developed using a Gaussian process emulator (GPE). The dataset was generated for a driving point response since this location's response magnitude was high. A validation study carried out on the metamodel indicated a good prediction of the weld strength. Future work would include a study to span more sensing locations and damping.</p
AN EXPERIMENTAL AND NUMERICAL INVESTIGATION OF THE COMBINATION OF DIFFERENT DAMPER TYPES FOR IMPROVED CONTROL OF VIBRATION
No full textEliminating and reducing unwanted vibrations required a good knowledge of the dynamic systems fundamental components; mass, spring, and damper. Meanwhile, dampers are responsible for reducing the vibrations amplitudes and the time needed by a structure to reach its steady state. This research is focused on studying a combination of different dampers through computational and experimental approaches. Furthermore, parametric studies are conducted to investigate the parameters that affect each damper's damping behavior.
Two dampers were designed, manufactured, modeled, and tested through this study. Firstly, a hybrid damper was developed by integrating two damping technologies; Viscous Fluid Damper (VFD) and Particle Impact Damper (PID). The VFD used in this study was a Mono-tube commercial viscous damper used in the automobile suspension system. On the other hand, the PID part consisted of a circular plastic enclosure filled with Stainless Steel 15mm diameter bearing balls. The Fluid Impact Hybrid Damper (FIHD) was designed by attaching the PID part to the VFD piston rod. A shaker testing setup was developed to drive the hybrid dampers piston rod into a sinusoidal dynamic load with a 1-8 Hz frequency range. The number of balls was changed three times (5, 10, and 15) to examine this parameter effect on the FIHDs damping effect. In addition, a Finite Element Model (FEM) of the FIHD was developed using LS-Dyna explicit solver. The FEM of the FIHD simulated the elastoplastic collisions between the balls and the walls using a piecewise-linear plasticity material model. Results were presented using Frequency Response Function (FRF) to show the damping effect in a set of force-independent results. The evaluated FRF of the two approaches (Experiment and FEM model) showed a noticeable reduction in amplitude at the systems natural frequency (2 Hz). In addition to the hybrid damper, this study also investigated a damper that belongs to the semi-active countermeasures known as Magnetorheological fluid (MRF) damper. MRF dampers damping effect is controlled using a magnetic field produced by an excitation system. In an MRF damper, a smart fluid is used as the damper fluid instead of using the classic hydraulic oil. The excitation system components were designed and manufactured based on dimensions reported in a previous study. The excitation system's magnetic field (MF) density value was obtained both experimentally and numerically using Comsol FE software. The MF study aimed to address the parameters that affect the magnetic field density, and thus, the MRF damping effect. Eventually, a Computational Fluid Dynamic (CFD) Analysis is conducted on the MRF damper. The CFD analysis describes the fluid flow between the compression and rebound champers through the internal orifices. Averaged Navier-Stokes equations are solved by the SIMPLE method, and the RNG k-? is used to model turbulence when the fluid passes through the orifice. The viscosity of the MRF was evaluated experimentally using a viscosity meter when applying different values of magnetic flux. The magnetic flux values were changed along with changing the excitation current values from 0 A to 5 A with a 1 A increment. Rebound and compression forces were observed from the static pressure contour plot. Based on the damping coefficients obtained from different viscosities values, the results showed that the damping values are exponentially increasing when increasing viscosity
”The world has always demanded from me extraordinary things, which was needless” : father Sadok Wincenty Barącz (1814-1892) shown in light of historical sources
No full textSadok Wincenty Barącz, known in Polish historiography as the historian of the Order of Preachers and the Armenians in Poland, was himself a descendant of Polish‑Armenians. Although he was brought up in the Armenian Catholic rite, he decided to follow the Roman Catholic priest and became a Dominican friar. His activity took place in the second half of the 19th century in various monasteries all over Galicia. Search for historical sources concerning his biography and historical work is a very difficult task, as they are scattered throughout archives and libraries in Poland and Ukraine. The author of this article made an attempt to determine their state of presentation. During the examination of Barącz’s legacy he was able to find many of his works in manuscript form, including previously unknown texts, the enormous correspondence (mostly letters received by Barącz) and the autobiography
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