Journal of Theoretical and Applied Vibration and Acoustics
Volume:10 Issue: 1, Winter & Spring 2024

Paying attention to the sound quality of mosques is one of the architectural fundamentals of Islamic societies because the mosque is the most prominent ritual place of Islam, and speech and hearing are considered its main characteristics. In this research, the analysis of the most effective form in optimal sound quality was defined as the primary goal, and for this purpose, the historical mosques of Tabriz city were subjected to field analysis. The initial analysis of the samples from the perspective of the variables affecting the acoustics led to the selection of 15 samples in three volume categories and five form groups, in which the selected standards for measurement are 3382-1 and 3382-2, and the measurement equipment are the 2260 B&K investigator and the SINUS Acoustic Camera. Among the acoustic variables, Background Noise, Sound Pressure Level, and Reverberation Time were analyzed, and the results of field measurement led to the framework becoming the final goal of the study. The results of this study show that acoustic behavior in samples regardless of their size and form is almost similar, which is due to the components such as materials and their implementation, as well as the overall formal frame within mosques, which were considered the same to reduce the interventional variables. The second achievement, which is the result of a visual data collection of the quality of sound playback, identifies the mentioned components and shows that the reflection and propagation of sound in selected mosques are from architectural elements that are common in all samples and form the overall frame of space. These two achievements framed the final goal of the study in the form of determining acoustic identification for historical mosques in Tabriz.  The final result of this study is the analysis of data in MATLAB software which extracts the Background Noise, Sound Pressure Level, and Reverberation Time equation based on frequency in Tabriz historical mosques and spaces with a similar model.

Nonlinear dynamical system research is essential in science and engineering because it could be potentially employed to represent real-world phenomena.  Traditional methods rely on pre-defined models or computationally expensive simulations, limiting their applicability to only numerical data. In the present research, without any prior knowledge of the system, we suggest a novel way to build a linear representation of the Duffing oscillator by fusing the capabilities of deep neural networks with the Koopman operator. This recently established methodology makes it easier to estimate system parameters effectively and accurately predict the oscillator's future behavior. Our approach incorporates a modified training procedure that restricts the Koopman operator to a single linear layer within the neural network, improving interpretability and potentially reducing training complexity. This methodology not only simplifies nonlinear system analysis but also paves the way for advancements in predictive modeling across various fields. Notably, our method yields distinctive eigenvalues of the Koopman generator matrix, enabling the Koopman operator to exhibit robustness against noise and capture a spectrum of Duffing equation behaviors. This includes the precise prediction of periodic oscillations and the capturing of period-doubling bifurcation, all while maintaining tractability within the neural network framework.

The vortex shedding from a bluff body can provoke structural vibrations known as flow-induced vibrations (FIV), which characterize an intrinsic phenomenon in the design of cylindrical structures. There are numerous passive and active methods to suppress FIV, among which suction and/or blowing on a cylinder surface is one of the most common approaches. In this work, different configurations of simultaneous suction and blowing are considered at two different Reynolds numbers corresponding to the galloping and frequency synchronization ranges. The parameters studied include the number of slots for suction and blowing, their length, and the velocity of the sucked and blown flow. The design of experiments method (DOE) is used to find the required simulation elements. The simulation results show that the dominant parameter in the reduction of galloping and vortex-induced oscillations and mass flow rate is the flow velocity in the areas of blowing and sucking. In addition, regression analysis is used to derive a relationship between various influencing parameters and performance parameters.

We introduce a novel approach to enhance gearbox fault diagnosis by integrating Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) for vibrational data analysis. Our method aims to improve fault detection accuracy, particularly in identifying subtle anomalies like broken teeth. However, real-world data often contains noise, which can hinder the effectiveness of such models. To address this challenge, we incorporate Singular Value Decomposition (SVD) pooling layers within the model. Our methodology starts with continuous wavelet transform (CWT), applied to the vibrational data to reveal crucial frequency-domain features. Concurrently, a CNN, using the Inception architecture, extracts spatial features. Simultaneously, LSTM networks capture temporal patterns. The unique feature representations from the CNN and LSTM branches are fused, creating a holistic feature set incorporating spatial, material, and frequency-domain information. This integrated feature set is then classified using a fully connected neural network. Our method's effectiveness is rigorously validated through comprehensive experiments on a diverse dataset. The results demonstrate exceptional accuracy in identifying gearbox faults, even in the early stages. This research advances predictive maintenance, offering a precise and comprehensive approach to gearbox fault diagnosis. In conclusion, the fusion of LSTM and CNN architectures for vibrational data analysis holds promise for gearbox fault diagnosis, benefiting industries reliant on machinery reliability and operational efficiency.

Investigating the sound radiated from faulty rolling element bearings (REBs) in rotor-bearing systems is as crucial as studying their vibration characteristics. This approach offers experts and researchers a more comprehensive understanding of faulty REB behavior, enhancing fault diagnosis capabilities. This study examines the impact of bearing faults, pedestal looseness, and shaft eccentricity on the vibro-acoustic characteristics of REBs. Additionally, it assesses the influence of fault severity and compound fault scenarios on these behaviors. A 6-degree-of-freedom (DOF) dynamic model is developed for an SKF 6205 bearing, including the shaft, inner ring, outer ring, and pedestal. The Hertzian theory is employed to model contact between the bearing balls and inner/outer rings. The governing equations are solved using the Runge-Kutta method to determine the surface velocity of the REB components, yielding a 4.56% error compared to experimental results, demonstrating good agreement. Based on the surface velocity, the sound pressure level (SPL) is calculated by modeling the inner and outer rings as cylindrical sound sources. The results reveal that auditory and visual observation can identify shaft eccentricity, while sound is a more sensitive indicator of bearing faults. Detecting incipient faults remains challenging, regardless of whether vibration or sound measurement tools, such as accelerometers or sound level meters, are employed. Furthermore, phase portraits indicate that pedestal looseness and bearing faults, unlike shaft eccentricity, result in chaotic and unpredictable motion, which may explain the sudden failures often observed in industrial REBs.