Experimental force reconstruction on plates of arbitrary shape using neural networks
Time: 7:40 pm
Author: Tyler Dare
Abstract ID: 2397
Measuring the forces that excite a structure into vibration is an important tool in modeling the system and investigating ways to reduce the vibration. However, determining the forces that have been applied to a vibrating structure can be a challenging inverse problem, even when the structure is instrumented with a large number of sensors. Previously, an artificial neural network was developed to identify the location of an impulsive force on a rectangular plate. In this research, the techniques were extended to plates of arbitrary shape. The principal challenge of arbitrary shapes is that some combinations of network outputs (x- and y-coordinates) are invalid. For example, for a plate with a hole in the middle, the network should not output that the force was applied in the center of the hole. Different methods of accommodating arbitrary shapes were investigated, including output space quantization and selecting the closest valid region.
Investigating the relationship between train speed and ground vibrations using random forest machine learning models
Time: 11:40 am
Author: Frank Klein Schaarsberg
Abstract ID: 2463
In the Netherlands, concerned citizens have proposed reducing train speed as an effective measure to mitigate annoyance caused by railway-induced vibrations. In the present study the relationship between train speed and other influencing parameters (e.g. axle load, wheel roughness), and ground vibrations was investigated using measurements, at different locations, of ground vibrations caused by the passage of regular freight trains and a test train at different speeds. Measurements have been analysed using multivariate regression models and a random decision forest model. The prevailing uncertainties have also been measured using normalized mean deviation between the model predicted value and the actual value. A comparison of results demonstrates that a trained and tested random forest model has certain predictive advantages: i) mean deviation between predicted and actual value is found to be the lowest with random forest model; ii) the random forest model considers all available parameters in the dataset, thus simulating the real situation more closely. However, the model is very location-specific and must therefore be used with caution. In general it is observed that a decrease in train speed results in the reduction of measured vibration levels.
A Sound Insulation Prediction Model for Floor Structures in Wooden Buildings Using Neural Networks approach
Time: 5:20 pm
Author: Mohamad BADER EDDIN
Abstract ID: 2619
Recently, machine learning and its applications have gained a large attraction in different fields. Accurate predictions in building acoustics is vital especially in the design stage. This paper presents a sound insulation prediction model based on Artificial Neural Networks (ANNs) to estimate acoustic performance for airborne and impact sound insulation of floor structures. At an initial stage, the prediction model was developed and tested for a small amount of data, specifically 67 measurement curves in one third octave bands. The results indicate that the model can predict the weighted airborne sound insulation for various floors with an error around 1 dB, while the accuracy decreases for the impact sound especially for complex floor configurations due to large error deviations in high frequency bands between the real and estimated values. The model also shows a very good accuracy in predicting the airborne and impact sound insulation curves in the low frequencies, which are of higher interest usually in building acoustics. Keywords: building acoustics, airborne sound, impact sound, prediction model, neural networks
Scalable Machine Learning Approach to Classifying Transportation Noise at Two Urban Sites in Greater Boston, Massachusetts
Time: 12:00 pm
Author: Tiange Wang
Abstract ID: 2907
The goal of this study was to characterize transportation noise by vehicle class in two urban communities, to inform studies of transport noise and ultra-fine particulates. Data were collected from April to September 2016 (150 days) of continuous recording in each urban community using high-resolution microphones. Training data was created for airplanes, trucks/buses, and train events by manual listening and extraction of audio files. Digital signal processing using STFT and Hanning windowing was performed in MATLAB, creating audio spectrograms with varying frequency: log vs linear frequency scales, and 4K vs 20K max frequency. For each of the four spectrogram sets, a neural net model using PyTorch was trained via a compute cluster. Initial results for a multi-class model provide an accuracy of 85%. Comparison between a selection of frequency scales and expanding to longer time periods is ongoing. Validation with airport transport logs and local bus and train schedules will be presented.
Combination of gated recurrent unit and Network in Network for underwater acoustic target recognition
Time: 8:40 pm
Author: shuang yang
Abstract ID: 1490
Underwater acoustic target recognition is an important part of underwater acoustic signal processing and an important technical support for underwater acoustic information acquisition and underwater acoustic information confrontation. Taking into account that the gated recurrent unit (GRU) has an internal feedback mechanism that can reflect the temporal correlation of underwater acoustic target features, a model with gated recurrent unit and Network in Network (NIN) is proposed to recognize underwater acoustic targets in this paper. The proposed model introduces NIN to compress the hidden states of GRU while retaining the original timing characteristics of underwater acoustic target features. The higher recognition rate and faster calculation speed of the proposed model are demonstrated with experiments for raw underwater acoustic signals comparing with the multi-layer stacked GRU model.
A machine learning-based methodology for computational aeroacoustics predictions of multi-propeller drones
Time: 8:20 pm
Author: Cesar Legendre
Abstract ID: 2415
The rapid progress in technological developments of small Unmanned Aircraft Systems (sUAS) or simply "drones" has produced a significant proliferation of this technology. From multinational businesses to drone enthusiasts, such a technology can offer a wide range of possibilities, i.e., commercial services, security, and environmental applications, while placing new demands in the already-congested civil airspace. Noise emission is a key factor that is being addressed with high-fidelity computational fluid dynamics (CFD) and aeroacoustics (CAA) techniques. However, due to uncertainties of flow conditions, wide ranges of propellers' speed variations, and different payload requirements, a complete numerical prediction varying such parameters is unfeasible. In this study, a machine learning-based approach is proposed in combination with high-fidelity CFD and CAA techniques to predict drone noise emission given a wide variation of payloads or propellers speeds. The transient CFD computations are calculated using a time-marching LES simulation with a WALE sub-grid scale. In contrast, the acoustic propagation is predicted using a finite element method in the frequency domain. Finally, the machine learning strategy is presented in the context of fulfilling two goals: (i) real-time noise prediction of drone systems; and (ii) determination of propellers rotation speeds leading to a noise prediction matching experimental data.
Generative Adversarial Neural Network for Semi-supervised Planetary Gearbox Fault Diagnosis
Time: 8:00 pm
Author: Gyuwon Kim
Abstract ID: 2479
Detecting bearing faults in advance is critical for mechanical and electrical systems to prevent economic loss and safety hazards. As part of the recent interest in artificial intelligence, deep learning (DL)-based principles have gained much attention in intelligent fault diagnostics and have mainly been developed in a supervised manner. While these works have shown promising results, several technical setbacks are inherent in a supervised learning setting. Data imbalance is a critical problem as faulty data is scarce in many cases, data labeling is tedious, and unseen cases of faults cannot be detected in a supervised framework. Herein, a generative adversarial network (GAN) is proposed to achieve unsupervised bearing fault diagnostics by utilizing only the normal data. The proposed method first adopts the short-time Fourier transform (STFT) to convert the 1-D vibration signals into 2-D time-frequency representations to use as the input to our (DL) framework. Subsequently, a GAN-based latent mapping is constructed using only the normal data, and faulty signals are detected using an anomaly metric comprised of a discriminator error and an image reconstruction error. The performance of our method is verified using a classic rotating machinery dataset (Case Western Reserve bearing dataset), and the experimental results demonstrate that our method can not only detect the faults but can also cluster the faults in the latent space with high accuracy.