Wyniki wyszukiwania - BazTech

1

Post-stall damage identification and vibration analysis in the AMT Olympus micro gas turbine

Czarnecki Michał, Kołodziejczyk Radosław

Zeszyty Naukowe Politechniki Morskiej w Szczecinie

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2026

|

nr 86 (158)

72--82

EN

This study investigates post-stall damage identification and vibration characteristics of the AMT Olympus micro gas turbine following a compressor surge event. The engine, primarily used for UAV propulsion, experienced rotor damage and increased imbalance during acceptance testing under steady-state conditions. Experimental diagnostics included measurements of rotational speed, vibration levels at compressor and turbine bearing planes, pressure, and temperature. The results reveal a significant increase in vibration amplitudes correlated with rotational speed. A spectral analysis identified rotor rubbing phenomena and imbalance signatures, providing insight into damage progression. This study highlights the diagnostic value of steady-state vibration monitoring for early fault detection in micro gas turbines and recommends limiting operational speeds to below 88% of the maximum allowable rpm during testing to ensure safety. Additionally, design flaws in the starter clutch system have been identified, suggesting improvements to future engine reliability. These findings contribute to enhanced engine health monitoring and maintenance strategies for micro-class turbine engines in UAV applications.

2

Combined rotor faults diagnosis in induction motors using MVSA and Intra-Mode Variational Modal Decomposition (IM-VMD)

Ait Mellal Ismail, Lahbabi Salma, Dahi Khalid

Diagnostyka

|

2026

|

Vol. 27, No. 2

art. no. 2026205

EN

In modern industry, constant production and operation without stoppages is viewed as being vital in ensuring competitiveness and efficiency. To maintain continuity in production, industries make use of advanced diagnostic measures by using of different performance measures such as reliability and risk assessment. In addition, within the context of Industry 4.0, there are also smart devices and Internet of Things (IoT) that can be used to enhance monitoring and optimize production processes intelligently. In such a context, this study seeks to examine the problem of detecting combined faults in three-phase induction machines via vibration signals. The purpose of this research is to diagnose fault signatures as early as possible even in the presence of multiple interactions between the faults. To achieve this objective, an intra-mode variational mode decomposition (IM-VMD) approach that employs an embedded source separation strategy was applied for decomposing vibration signals into multiple intrinsic modes. Using this framework, it became possible to isolate those vibration components associated with faults and increase interpretability of signals in terms of time and frequency domains. The outcomes demonstrated an effective identification of fault signatures; the extracted vibration frequency components match the theoretical frequencies of the defects. More precisely, the coefficient of correlation between extracted frequency components and theoretically calculated ones equals 0.95 to 1. This finding suggests that the proposed algorithm provides reliable results that can be applied in practice for detecting combined faults in three-phase induction machines. It is also possible to highlight that by utilizing the introduced method, it becomes possible to detect fault signatures at the earliest stage of their appearing.

3

Longitudinal vibration analysis of non-uniform rods of any shapes based on differential transforms

Ghazaryan Davit, Avetisyan Armine Gevorg

Advances in Science and Technology. Research Journal

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2026

|

Vol. 20, no. 5

293--311

EN

A differential transforms based computational method is developed for the free longitudinal vibration analysis of non-uniform rods with smoothly varying cross-sections. The objective is to obtain a numerically stable formulation that accommodates changes in the cross-sectional profile without problem-specific re-derivation. Assuming the area variation is described by a differentiable function A(x), the governing variable-coefficient eigenvalue problem is mapped into the transform domain and expressed through recurrence relations, from which a characteristic polynomial in the frequency parameter is constructed to determine the natural frequencies. The required geometric input is provided solely through the differential spectrum of A(x). The method is validated against a broad set of benchmark problems from the literature, including polynomial, trigonometric, and exponential cross-sectional variations, and reproduces reference eigenfrequencies with agreement up to at least five significant digits, while requiring significantly fewer degrees of freedom than finite element discretizations for comparable accuracy. In addition, the formulation resolves previously reported inconsistencies and yields physically consistent fundamental modes across the examined boundary configurations. The approach is further demonstrated on composite cross-sectional variations beyond standard benchmark profiles.

4

Experimental investigation of fracture orientation effects on bone dynamics behavior through vibration analysis

Jani Jignesh, Rachchh Nikunj

Advances in Science and Technology. Research Journal

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2026

|

Vol. 20, no. 2

153--170

EN

Conventional diagnostic tools for identifying fractures in bone, like X-rays and CT scans, are widely used, but involve ionizing radiation and lack sensitivity to physical degradation, like stiffness loss and damping in bones. To overcoe these limitations, non-invasive vibration-based diagnostic techniques are emerging as promising alternatives for evaluating fracture severity. This work aimed to assess the dynamic behavior of bone specimens with varying fracture orientations specifically oblique, longitudinal, and lateral to identify which orientation leads to the most severe mechanical degradation. Goat metacarpal bones were tested with four fracture types with an un- fractured reference bone. Controlled lateral impacts were applied using an instrumented hammer, and vibrational responses were recorded using an accelerometer. Data were analyzed using Fast Fourier Transform (FFT) to extract Frequency Response Functions (FRF), coherence, and phase characteristics. Each condition was subjected to three tests for consistencies in results. The results indicated that lateral fractures resulted in the greatest decrease in stiffness, lowest resonance frequency, and largest FRF magnitude, making them the worst fracture type in mechanical degradation. This method has implications for radiation-free fracture diagnosis, particularly of benefit to pregnant women and radiation-sensitive groups.

5

Vibration analysis of delaminated CNT-Reinforced CFRP composite plates

Imran Muhammad

Advances in Mechanical and Materials Engineering

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2026

|

Vol. 43, nr 1

159--177

EN

Fiber-reinforced polymer composite materials have gained extensive application in aerospace, automotive, marine, and civil infrastructure owing to their exceptional specific strength, stiffness, and design flexibility. However, delamination - a critical interlaminar failure mode compromises structural integrity and dynamic performance. This comprehensive study investigates the vibration behavior of carbon fiber-reinforced polymer (CFRP) composite plates subjected to varying delamination extents, laminate stacking sequences, and boundary constraints through integrated analytical and finite element methodologies. The governing differential equations are derived using the Rayleigh-Ritz energy method based on classical laminated plate theory, and numerical simulations are performed using ANSYS finite element software. The investigation examines delamination sizes ranging from 0% to 56.25% of plate area, three distinct stacking configurations ([0/90/45/90], [0/45], [0/90]), and all sides clamped (CCCC), simply supported (SSSS), cantilever (CFFF), and free edges (FFFF) boundary conditions. Results demonstrate that natural frequencies decrease systematically with increasing delamination size, with maximum reduction of 5-8% occurring for the largest delamination extent (56.25%) across all boundary condition.. Furthermore, CNT integration enhances both natural frequencies (up to 29.8% increase at 2.5 wt% CNT loading) and damping characteristics (42.1% improvement). These findings support improved design and vibration control of advanced composite structures.

PL

Polimerowe materiały kompozytowe wzmacniane włóknami zyskały szerokie zastosowanie w przemyśle lotniczym, motoryzacyjnym, morskim i lądowym ze względu na swoją wyjątkową wytrzymałość właściwą, sztywność i elastyczność projektowania. Jednakże de laminacja, krytyczne międzywarstwowe uszkodzenie, zagraża integralności strukturalnej i właściwościom dynamicznym. W niniejszym artykule przedstawiono wyniki kompleksowych badań drgań płyt kompozytowych wykonanych z polimeru wzmacnianego włóknami węglowymi poddanych różnym stopniom delaminacji, sekwencjom układania laminatów oraz ograniczeniom brzegowym, wykorzystując zintegrowane metody analityczne i metodę elementów skończonych. Równania różniczkowe zachowania płyt kompozytowych wyprowadzono za pomocą metody energetycznej Rayleigha-Ritza opartej na klasycznej teorii płyt laminowanych, a symulacje numeryczne przeprowadzono za pomocą oprogramowania ANSYS do analiz metodą elementów skończonych. W badaniach analizowano rozmiary delaminacji w zakresie od 0% do 56,25% powierzchni płyty, trzy różne konfiguracje ułożenia warstw ([0/90/45/90], [0/45], [0/90]) oraz warunki brzegowe: zaciskanie wszystkich boków (CCCC), swobodne podparcie (SSSS), podparte i ze swobodnymi krawędziami (FFFF). Wyniki ujawniły, że częstotliwości własne systematycznie zmniejszają się wraz ze wzrostem rozmiaru delaminacji, przy czym maksymalna redukcja wynosi 5–8% dla największego zakresu delaminacji (56,25%) we wszystkich rozpatrywanych warunkach brzegowych. Ponadto integracja nanorurek węglowych poprawia zarówno częstotliwości własne (wzrost do 29,8% przy zawartości nanorurek 2,5% wag.), jak i charakterystyki tłumienia (poprawa o 42,1%). Uzyskane wyniki wspierają ulepszone projektowanie i kontrolę drgań zaawansowanych struktur kompozytowych.

6

Energooszczędne wykrywanie wad kół pojazdów kolejowych przy użyciu analizy drgań opartej na transformacie Hilberta i grupowaniu histogramów

Dziedzic Jakub, Melnik Rafał

Przegląd Techniczny : Gazeta Inżynierska

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2025

|

nr 1

15--21

PL

Zapewnienie bezpieczeństwa i niezawodności systemów transportu kolejowego w dużym stopniu zależy od wczesnego wykrywania i monitorowania usterek zestawów kołowych. Jeśli nie zostaną sprawdzone, niedoskonałości, takie jak płaskie miejsca na kołach, mogą zwiększyć poziom drgań, przyspieszyć zmęczenie podzespołów i doprowadzić do poważnych incydentów bezpieczeństwa. Tradycyjne podejścia do diagnozowania stanu kół często opierają się na inspekcjach wykonywanych manualnie lub sprzęcie wymagającym dużych zasobów, co może być trudne do wdrożenia w dużych flotach sieciach zarządzanych zdalnie. Artykuł prezentuje energooszczędną metodę wykrywania defektów kół kolejowych przy użyciu analizy wibracji. Wykorzystano transformację Hilberta do przetwarzania sygnałów oraz klasteryzację histogramową w celu identyfikacji nieprawidłowości. Rozwiązanie to jest bardziej efektywne energetycznie i precyzyjne w porównaniu z tradycyjnymi metodami. Skupiono się na analizie drgań jako wskaźnika stanu technicznego kół, co ma istotne znaczenie dla bezpieczeństwa i utrzymania infrastruktury kolejowej. Wyniki pokazują, że przez skupienie się na konkretach i wykorzystanie prostych, ale skutecznych analiz statystycznych, system może niezawodnie odróżniać sprawne koła od wadliwych. Przyczynia się to do opłacalnych strategii konserwacji, skraca przestoje i zwiększa bezpieczeństwo na kolei. Na koniec omawiamy przyszłe ulepszenia, w tym integrację z ramami konserwacji predykcyjnej, adaptacyjne techniki progowe i potencjalne włączenie bardziej zaawansowanych metod przetwarzania, jeśli pozwolą na to budżety energetyczne.

EN

Ensuring the safety and reliability of railway transport systems depends heavily on the early detection and monitoring of wheelset defects. If left unchecked, imperfections such as flat spots on wheels can increase vibration levels, accelerate component fatigue, and lead to serious safety incidents Traditional approaches to diagnosing wheel condition often depend on manual inspections or resource-intensive equipment, which can be challenging to deploy across large fleets or remote networks. This paper presents an energy-efficient, computationally lean method for detecting characteristic vibration patterns associated with common wheelset defects. The proposed approach employs the Hilbert transform to extract specific dynamic responses in time domain of vibration signals of vibration signals and then utilizes histogram-based clustering to identify deviations in peak distribution that indicate mechanical defects. Unlike more complex machine learning approaches that demand significant power and computational resources, our method is designed to operate autonomously on low-power, self-contained devices with limited external support. It achieves robust performance in a vehicle speed range of 10–40 km/h and track conditions, detecting subtle anomalies after accumulating modest amounts of data from short measurement windows. The results show that by focusing on the specific and exploiting simple but effective statistical analyses, the system can reliably differentiate healthy from defective wheels. This contributes to cost-effective maintenance strategies, reduces downtime, and enhances railway safety. Finally, we discuss future improvements, including integration with predictive maintenance frameworks, adaptive thresholding techniques, and potentially incorporating more advanced processing methods if energy budgets allow.

7

Badanie wpływu pracy maszyn na drgania konstrukcji wsporczej w hali produkcyjno-magazynowej

Grosel Jacek, Pakos Wojciech

Przegląd Budowlany

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2025

|

R. 96, nr 5

38--42

PL

W artykule przedstawiono wyniki analizy dynamicznej konstrukcji wsporczej, której nadmierne drgania wywoływane są pracą maszyn. Wykonano pomiary drgań w różnych punktach konstrukcji przy zróżnicowanych parametrach pracy. Analiza wyników wykazała, że głównym źródłem drgań są wstrząsacze pracujące z f ≈ 25 Hz. Stwierdzono, że drgania w kierunku pionowym są około 8 razy większe niż w kierunkach poziomych. Drgania pomostów były około 4,6 razy wyższe niż drgania konstrukcji stalowej. Konieczne jest dalsze monitorowanie, a także modernizacja systemu monitorującego proces produkcyjny.

EN

This paper presents the results of a dynamic analysis of a support structure. The analysis was performed to identify excessive vibrations caused by the operation of machinery. Vibration measurements were taken at different points of the structure with different operating parameters. Analysis of the results showed that the main source of vibration is the shakers operating at f ≈ 25 Hz. Vibrations in the vertical direction were about 8 times higher than in the horizontal directions. The vibration of the platforms was about 4,6 times higher than the vibration of the steel structure. It is essential to continue monitoring and upgrade the system that monitors the production process.

8

Analysis of Vibrations of the IRB 2400 Industrial Robot

Gierlak Piotr, Pietruś Paulina, Szybicki Dariusz

Pomiary Automatyka Robotyka

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2025

|

R. 29, nr 2

53--57

EN

This article provides an analysis of low-frequency vibrations in the IRB 2400 industrial robot using motion amplification technology based on image analysis. This technology allows visualisation of the vibration of the entire robot and analysis of the vibrations of the robot points that can be selected after the image acquisition process has been performed. Impulse force generated with a modal hammer was used to induce robot vibrations. A vibration analysis has been performed that takes into account the different positions of the robot arm. The analysis indicated a strong relationship between the system response and the robot arm position and the robot’s interaction with the environment. The results obtained will be used to plan a robotic mechanical machining process, taking into account the minimisation of robot vibrations.

PL

W artykule przedstawiono analizę drgań niskoczęstotliwościowych robota przemysłowego IRB 2400 z zastosowaniem technologii wzmocnienia ruchu, bazującej na analizie obrazu. Technologia ta pozwala na wizualizację drgań całego robota oraz analizę drgań punktów robota, które można wybrać po przeprowadzeniu procesu akwizycji obrazu. Do wzbudzania drgań robota stosowano wymuszenie impulsowe generowane z zastosowaniem młotka modalnego. Przeprowadzono analizę drgań uwzględniającą różne pozycje ramienia robota. Analiza wskazała silną zależność odpowiedzi układu od pozycji ramienia robota oraz od siły interakcji robota z otoczeniem. Uzyskane wyniki zostaną zastosowane do planowania procesu zrobotyzowanej obróbki mechanicznej z uwzględnieniem minimalizacji drgań robota.

9

An exploration of vibration based damage detection techniques for composite materials

Pathan Firoj U., Patil Milind M., Baviskar Prasad R., Aher Vishnu S.

International Journal of Applied Mechanics and Engineering

|

2025

|

Vol. 30, no. 3

97--113

EN

Structural damage monitoring is inevitable for the structures to perform during their intended service life adroitly. In the present review, literature related to techniques for diagnosing vibration-intensive damages have been evaluated in order to determine the material characteristics, such as stiffness and damping. Also, extensive review has been presented in the for damage detection in composite materials. The review encompasses the literature published in last 42 years, i.e., 1982 to 2024. The literature review is classified into sections as damage detection workflow, composite materials, damage detection techniques, and advanced damage detection techniques. The usage of strain energy, mode-shapes, waveform dimension, wavelet transform and updating finite element models in detection of damage are also discussed. Further, an overview of concepts, techniques, and advancement in vibration-induced damage detection are presented. The limitations of each technique are explained. An insight on advanced techniques and tools from genetic algorithm and artificial neural network regarding their employability to detect the damage is provided. This work portrays the damage detection methodologies.

10

Diagnostics of vibrations due to looseness fault and unbalance in rotating machinery with neural network

Saeed Samed, Reddy Babu

Diagnostyka

|

2025

|

Vol. 26, No. 4

art. no. 2025403

EN

Current work aims at the development and evaluation of Neural Network (NN) model for diagnosing faults due to unbalanced mass and structural looseness in an induction motor setup. These experiments were conducted with and without structural looseness. This data is processed in MATLAB software and is then used to train NN model to detect the unbalance, and structural looseness faults in the setup. The performance of the model trained is evaluated by model performance metrics, which showed the model predicts the presence of above faults with high accuracy. The, Kruskal Willis algorithm is used in MATLAB software to get the feature importance scores, so that, the number of predictors/features can be reduced. It is found that two mutually perpendicular radial accelerations of the setup have significant importance, and hence, a new NN model is trained with the reduced number of predictors/features. It was found that there is a slight reduction in the model’s performance, therefore, to increase the performance, another model is trained with the two mutually perpendicular radial accelerations, and their resultants. This increased the performance of the model considerably, hence making it suitable to deploy for the detection of unbalance, and structural looseness faults.

11

Energy-efficient detection of railway wheel defects using Hilbert transform-based vibration analysis and histogram clustering

Dziedzic Jakub, Melnik Rafał

Polish Technical Review

|

2024

|

No. 4

37--47

EN

Ensuring the safety and reliability of railway transport systems depends heavily on the early detection and monitoring of wheelset defects. If left unchecked, imperfections such as flat spots on wheels can increase vibration levels, accelerate component fatigue, and lead to serious safety incidents Traditional approaches to diagnosing wheel condition often depend on manual inspections or resource-intensive equipment, which can be challenging to deploy across large fleets or remote networks. This paper presents an energy-efficient, computationally lean method for detecting characteristic vibration patterns associated with common wheelset defects. The proposed approach employs the Hilbert transform to extract specific dynamic responses in time domain of vibration signals of vibration signals and then utilizes histogram-based clustering to identify deviations in peak distribution that indicate mechanical defects. Unlike more complex machine learning approaches that demand significant power and computational resources, our method is designed to operate autonomously on low-power, self-contained devices with limited external support. It achieves robust performance in a vehicle speed range of 10–40 km/h and track conditions, detecting subtle anomalies after accumulating modest amounts of data from short measurement windows. The results show that by focusing on the specific and exploiting simple but effective statistical analyses, the system can reliably differentiate healthy from defective wheels. This contributes to cost-effective maintenance strategies, reduces downtime, and enhances railway safety. Finally, we discuss future improvements, including integration with predictive maintenance frameworks, adaptive thresholding techniques, and potentially incorporating more advanced processing methods if energy budgets allow.

PL

Zapewnienie bezpieczeństwa i niezawodności systemów transportu kolejowego w dużym stopniu zależy od wczesnego wykrywania i monitorowania usterek zestawów kołowych. Jeśli nie zostaną sprawdzone, niedoskonałości, takie jak płaskie miejsca na kołach, mogą zwiększyć poziom drgań, przyspieszyć zmęczenie podzespołów i doprowadzić do poważnych incydentów bezpieczeństwa. Tradycyjne podejścia do diagnozowania stanu kół często opierają się na ręcznych inspekcjach lub sprzęcie wymagającym dużych zasobów, co może być trudne do wdrożenia w dużych flotach lub sieciach zdalnych. Artykuł prezentuje energooszczędną metodę wykrywania defektów kół kolejowych przy użyciu analizy wibracji. Wykorzystano transformację Hilberta do przetwarzania sygnałów oraz klasteryzację histogramową w celu identyfikacji nieprawidłowości. Rozwiązanie to jest bardziej efektywne energetycznie i precyzyjne w porównaniu z tradycyjnymi metodami. Skupiono się na analizie drgań jako wskaźnika stanu technicznego kół, co ma istotne znaczenie dla bezpieczeństwa i utrzymania infrastruktury kolejowej. Wyniki pokazują, że poprzez skupienie się na konkretach i wykorzystanie prostych, ale skutecznych analiz statystycznych, system może niezawodnie odróżniać sprawne koła od wadliwych. Przyczynia się to do opłacalnych strategii konserwacji, skraca przestoje i zwiększa bezpieczeństwo kolei. Na koniec omawiamy przyszłe ulepszenia, w tym integrację z ramami konserwacji predykcyjnej, adaptacyjne techniki progowe i potencjalne włączenie bardziej zaawansowanych metod przetwarzania, jeśli pozwolą na to budżety energetyczne.

12

An AutoML-based comparative evaluation of machine learning methods for detection of eccentricity faults in induction motors by using vibration signals

Irgat Eyüp, Ünsal Abdurrahman

Metrology and Measurement Systems

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2024

|

Vol. 31, nr 4

831--848

EN

Induction motors (IMs) are the most widely used electrical machines in industrial applications. However, they are subject to various mechanical and electrical faults. Eccentricity faults are among the common mechanical faults of IMs. This study compares the performance of four commonly used machine learning (ML) methods, including k-nearest neighbours (k-NN), decision tree (DT), support vector machine (SVM), and random forest (RF) along with the statistical features in detecting eccentricity faults of IMs with an automated machine learning (AutoML) model. The aim of using AutoML in this study is to fully automate the process of detection of eccentricity faults of IMs by selecting the classifier with the highest accuracy rate and shortest computation time along with the most effective feature(s). The eccentricity fault analysed in this study was experimentally implemented in the laboratory. Three-axis vibration signals were collected for healthy and eccentricity-faulty IMs. In the proposed study the three-axis vibration signals are pre-processed to determine the statistical features that are used as input to the ML methods. The proposed study offers the best ML method among the four studied algorithms and the need for expert knowledge of ML and eccentricity fault detection. The proposed AutoML model offers the DT method along with the z-axis rms feature for the highest accuracy rate and the shortest computation time in detecting the eccentricity fault.

13

Gas turbine vibration monitoring based on real data and neuro-fuzzy system

Nail Bachir, Djaidir Benrabeh, Tibermacine Imad Eddine, Napoli Christian, Haidour Nabil, Abdelaziz Rabehi

Diagnostyka

|

2024

|

Vol. 25, No. 1

art. no. 2024108

EN

The gas turbine is considered to be a very complex piece of machinery because of both its static structure and the dynamic behavior that results from the occurrence of vibration phenomena. It is required to adopt monitoring and diagnostic procedures for the identification and localization of vibration flaws in order to ensure the appropriate operation of large rotating equipment such as gas turbines. This is necessary in order to avoid catastrophic failures and deterioration and to ensure that proper operation occurs. Utilizing an approach that is based on spectrum analysis, the purpose of this study is to provide a model for the monitoring and diagnosis of vibrations in a GE MS3002 gas turbine and its driven centrifugal compressor. This will be done by utilizing the technique. Following that, the collection of vibration measurements for a model of the centrifugal compressor served as a suggestion for an additional method. This method is based on the neuro-fuzzy approach type ANFIS, and it aims to create an equivalent system that is able to make decisions without consulting a human being for the purpose of detecting vibratory defects. In spite of the fact that the compressor that was investigated has flaws, this procedure produced satisfactory results.

14

Analyzing frequency response analysis experimentally for bone healing detection: examining the potential of vibrational evaluations

Kadhim Abbas Jawad, Aubad Mohammed Jawad, Al-Juboori Ameen M.

Diagnostyka

|

2024

|

Vol. 25, No. 3

art. no. 2024315

EN

Assessment of bone healing is essential for efficient orthopedic treatment. This work investigates the feasibility of assessing frequency response experimentally for bone healing detection, with a particular emphasis on the use of vibrational assessments. Detailed experimental studies were carried out to determine the ability of frequency response analysis to assess bone healing. Mechanical excitation was delivered to cracked bone samples at various frequencies, and the vibrational responses of the displacement and accelerations were measured. The experimental setting includes testing five samples, to cover a wide range of possibilities. The obtained vibrational, such phase, magnitude, and coherence, were examined to find common patterns and changes linked with the healing process. The results showed that frequency response analysis has the potential to identify bone healing, as unique vibrational responses were seen in healed samples under cyclic load for different turns (0, 1000, 2000, 3000, and 4000). The findings demonstrate the sensitivity of vibrational evaluations in capturing the mechanical properties and healing condition of bone tissue. Furthermore, the presence of cracks impacts both structural integrity and natural frequency. Natural frequency decreases as the number of cycles increases. The highest frequency reduction occurred at the first mode shape and maximum cycle number, indicating considerable fracture behaviour changes. Natural frequency can be used to assess bone health; higher stiffness and frequency are associated with smaller crack size.

15

Investigation of the influence of debonding shapes and debonding locations on vibration behavior of sandwich structure

Demircioğlu Ufuk, Çakır Mutlu Tarık

Composites Theory and Practice

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2024

|

R. 24, nr 1

39--48

EN

Sandwich structures are employed in many different fields including automobile, marine, and aircraft structures. However, debonding may take place at the core-face sheet interface, reducing the stiffness of the structure. Debonding may occur for a variety of reasons, including initial manufacturing faults, changes in service loads, tool drops, and foreign object impacts. It is critical to comprehend how debonding zones impact the vibration of sandwich structures because decreases in the natural frequencies (NF) could lead to a structure vibrating at resonance and lead to structural failure. This paper investigates the influence of debonding shapes and debonding locations on the free-vibration behavior of sandwich structures. Different sandwich structures that have varied debonding shapes at various locations are modeled using COMSOL MULTIPHYSICS. Debonding is modeled by using the CZM model. Validation studies were performed to validate the current study. After the validation study, free vibration analysis of all the sandwich structures was performed and the first six NF were obtained from the simulations. The results show the influence of the debonding shapes and debonding locations on the NF of the sandwich structures. From the results, it was observed that both the debonding shapes and debonding locations significantly change the NF of the sandwich structures. The debonding shapes cause a reduction and an increase depending on the debonding location. It was also revealed that both debonding shapes and debonding locations have a significant effect on the vibration behavior of sandwich structures. Using this method, the debonding shape and location, delamination shape, and location can be predicted using machine learning algorithms. This study includes free vibration analysis of sandwich structures with different debonding shapes and locations, and the results show that natural frequencies change depending on the debonding shapes and locations. This information can be implemented in machine learning for use in the field of damage detection and utilized to predict the shape and location of delamination in sandwich structures.

16

Structural Health Monitoring for Dębica Railway Steel Arch Bridge in Poland

Nguyen Duc Cong, Salamak Marek, Katunin Andrzej, Poprawa Grzegorz

Civil and Environmental Engineering Reports

|

2024

|

Vol. 34, no. 2

216--223

EN

The study presents the vibration-based SHM system for the Dębica railway bridge located in Poland. The railway bridge owner was concerned about the excessive and self-excited vibrations of the hangers, the vibration measurement of 8 hangers per span in a total of two spans being monitored. The dynamic responses in both the transverse and longitudinal directions for each hanger under different load events over a nine-month period were recorded and introduced in this paper. The tension force and stress on each hanger are estimated through the natural frequency of the experimental vibration analysis. The proposed approaches could be used to develop a smart alarm system integrated into a vibration-based data-driven SHM system for heavy railway bridges.

17

Vibration analysis of asymmetric sandwich rotating FG porous discs coated with agglomerated nanocomposite facesheets

Jazi Farnoosh Rasooli, Amir Saeed, Arshid Ehsan

Archives of Civil and Mechanical Engineering

|

2024

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Vol. 24, no. 4

art. 201, 1--20

EN

The present research conducts free vibration analysis of annular rotating discs made from functionally graded porous materials, and nanocomposite reinforced carbon nanotubes face sheets. Pores distribution in the porous core is considered based on three different patterns, namely Nonsymmetric, Symmetric, and Monotonous ones across the thickness, and also, carbo nanotube dispersion in the face sheets is investigated randomly by considering their agglomeration effect. Kinematic relations of the mentioned structure regarding the shear deformation effects and based on the first-order theory are described, and then, variations of strain and kinetic energies by considering rotation via the calculus variation method are calculated. To extract the governing motion equations and associated boundary conditions, Hamilton's principle is employed, and then they are solved with the aid of the generalized differential quadrature method. After ensuring the correctness of the results obtained from the scripted code by comparing them in the simpler state with the previous research, the effect of different parameters such as pores’ distribution patterns, carbon nanotubes dispersion patterns and their agglomeration, core and face sheets thickness, and other parameters on the natural frequencies of the structure is investigated. Considering the obtained results, it can be found that increasing the porosity leads to a slight increment in the natural frequencies, generally, and increasing the carbon nanotubes’ mass fraction leads to significant enhancement in them. The outcomes of this study can be used in different industries, such as aerospace, military, and marine industries.

18

Micro‑mass sensor‑based vibration response of smart bidirectional functionally graded auxetic microbeams

Wei Y. Y., Al-Furjan M. S. H., Shan L., Shen X., Kolahchi R., Rabani bidgoli M., Farrokhian A.

Archives of Civil and Mechanical Engineering

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2024

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Vol. 24, no. 1

art. no. e38, 2024

EN

Microsensor-based vibration of 2D smart functionally graded sandwich microbeam with attached microparticles is investigated using the strain gradient hypothesis. The application of micro-materials as active sensing particles in micro-sensors has increased the sensitivity performance of micro-sensors that can be able to detect particles, for example, bacteria with very nano-dimensions and low concentrations. The sandwich beam contains a negative Poisson’s ratio auxetic honeycombs covered by a piezoelectric smart layer at the top and a bidirectional functionally graded material (FGM) layer at the bottom layers. Partial differential equations of the simply supported sandwich beams are first attained using the energy method utilizing refined zigzag theory. The coupled final equations are solved analytically utilizing Galerkin’s technique to present the frequency. The impact of the position and mass of the microparticles, applied voltage, material distribution in the bottom layer, size scale parameter, the honeycomb auxetic core geometrical properties, and the layer thickness on the frequency are discussed. The obtained findings showed that by enhancing the mass of the nanoparticle, the frequency is reduced. In addition, the location of the nanoparticle on the beam is important so that when it is close to the beam center, the frequency decreases. Further, by enhancing the thickness of the face sheet, the microbeam frequency decreases but increasing the core layer thickness plays an inverse role. Besides, it is found that when the material in-homogeneity index P x or P z in the 2D-FGM layer is enhanced, the frequency decreases.

19

Application of isogeometric approach to dynamics of curved beam

Łasecka-Plura Magdalena, Białasik Jan, Kuczma Mieczysław, Tabrizikahou Alireza

Vibrations in Physical Systems

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2023

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Vol. 34, nr 1

art. no. 2023117

EN

In the paper dynamics of a free-form Timoshenko curved beam is investigated. The considered problem is solved using isogeometric analysis. Non-uniform rational B-spline (NURBS) basis functions are applied to describe both geometry and displacement field of the considered beam. The Timoshenko beam theory is used to derive the element stiffness and mass matrices. The application of the presented method is shown in numerical examples. The correctness of the presented approach is proved by comparing the obtained results to those available in the literature and calculated by the finite element method. Analysis of convergence is presented for different orders of NURBS basis functions.

20

Research on vibration-based early diagnostic system for excavator motor bearing using 1-D CNN

Yandagsuren Dorjsuren, Kurauchi Tatsuki, Toriya Hisatoshi, Ikeda Hajime, Adachi Tsuyoshi, Kawamura Youhei

Journal of Sustainable Mining

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2023

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Vol. 22, iss. 1

65--80

EN

In mining, super-large machines such as rope excavators are used to perform the main mining operations. A rope excavator is equipped with motors that drive mechanisms. Motors are easily damaged as a result of harsh mining conditions. Bearings are important parts in a motor; bearing failure accounts for approximately half of all motor failures. Failure reduces work efficiency and increases maintenance costs. In practice, reactive, preventive, and predictive maintenance are used to minimize failures. Predictive maintenance can prevent failures and is more effective than other maintenance. For effective predictive maintenance, a good diagnosis is required to accurately determine motor-bearing health. In this study, vibration-based diagnosis and a one-dimensional convolutional neural network (1-D CNN) were used to evaluate bearing deterioration levels. The system allows for early diagnosis of bearing failures. Normal and failure-bearing vibrations were measured. Spectral and wavelet analyses were performed to determine the normal and failure vibration features. The measured signals were used to generate new data to represent bearing deterioration in increments of 10%. A reliable diagnosis system was proposed. The proposed system could determine bearing health deterioration at eleven levels with considerable accuracy. Moreover, a new data mixing method was applied.