Identification of acoustic emission sources for structural health monitoring applications based on convolutional neural networks and deep transfer learning

Hesser, Daniel Frank; Mostafavi, Shimaalsadat; Kocur, Georg Karl; Markert, Bernd

doi:10.1016/j.neucom.2021.04.108

Cited by 45 publications

(15 citation statements)

References 31 publications

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“…The classification task can consider up to seven classes in order to discriminate between the levels using various machine and deep learning methods such as support vector machines or neural networks [28,29]. By considering only the tightened levels, such as 60 cNm and 50 cNm, it can also be used to evaluate anomaly detectors based on autoencoders, self-organizing maps, or one-class classifiers [13,14].…”

Section: Train Supervised Learning Methodsmentioning

confidence: 99%

Monitoring a Bolted Vibrating Structure Using Multiple Acoustic Emission Sensors: A Benchmark

Ramasso

Verdin

Chevallier

2022

Data

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The dataset presented in this work, called ORION-AE, is made of raw AE data streams collected by three different AE sensors and a laser vibrometer during five campaigns of measurements by varying the tightening conditions of two bolted plates submitted to harmonic vibration tests. With seven different operating conditions, this dataset was designed to challenge supervised and unsupervised machine/deep learning as well as signal processing methods which are developed for material characterization or structural health monitoring (SHM). One motivation of this work was to create a common benchmark for comparing data-driven methods dedicated to AE data interpretation. The dataset is made of time series collected during an experiment designed to reproduce the loosening phenomenon observed in aeronautics, automotive, or civil engineering structures where parts are assembled together by means of bolted joints. Monitoring loosening in jointed structures during operation remains challenging because contact and friction in bolted joints induce a nonlinear stochastic behavior.

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Section: Train Supervised Learning Methodsmentioning

confidence: 99%

Monitoring a Bolted Vibrating Structure Using Multiple Acoustic Emission Sensors: A Benchmark

Ramasso

Verdin

Chevallier

2022

Data

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“…This study adopted the second approach because 2-D spectrograms were input into the neural network model. In image feature extraction using convolutional layers, multiple convolutional kernels, or filters [39] slide and convolve over the input image in a defined order. After completing the operation, a feature map is generated, serving as the next layer's input data.…”

Section: Convolutional Layermentioning

confidence: 99%

Application of a Deep Learning Neural Network for Voiding Dysfunction Diagnosis Using a Vibration Sensor

et al. 2022

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In a clinical context, there are increasing numbers of people with voiding dysfunction. To date, the methods of monitoring the voiding status of patients have included voiding diary records at home or urodynamic examinations at hospitals. The former is less objective and often contains missing data, while the latter lacks frequent measurements and is an invasive procedure. In light of these shortcomings, this study developed an innovative and contact-free technique that assists in clinical voiding dysfunction monitoring and diagnosis. Vibration signals during urination were first detected using an accelerometer and then converted into the mel-frequency cepstrum coefficient (MFCC). Lastly, an artificial intelligence model combined with uniform manifold approximation and projection (UMAP) dimensionality reduction was used to analyze and predict six common patterns of uroflowmetry to assist in diagnosing voiding dysfunction. The model was applied to the voiding database, which included data from 76 males aged 30 to 80 who required uroflowmetry for voiding symptoms. The resulting system accuracy (precision, recall, and f1-score) was around 98% for both the weighted average and macro average. This low-cost system is suitable for at-home urinary monitoring and facilitates the long-term uroflow monitoring of patients outside hospital checkups. From a disease treatment and monitoring perspective, this article also reviews other studies and applications of artificial intelligence-based methods for voiding dysfunction monitoring, thus providing helpful diagnostic information for physicians.

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“…Hesser et al found that the influencing factors of the business model include environmental analysis, stakeholders, corporate governance, competitive advantage, entrepreneur leadership, and objective mission. The direction and structure of the business model were mainly analyzed [ 8 ]. Dai et al built the business model from the perspective of business ecology.…”

Section: Review and Analysis Of Related Researchmentioning

confidence: 99%

Construction of Internet Business Innovation Network Model and Data Image Research under the Background of Game Theory

Bai

2022

Computational Intelligence and Neuroscience

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Facing the fierce market competition, enterprises not only need a deeper understanding and concept renewal in marketing theory but also need a set of standardized, practical, and efficient technical means and methods. Therefore, it is urgent to develop advanced marketing analysis tools and marketing decision-making methods. Based on game theory and neural network model, this study simplifies the existing research methods. By introducing different models, an image analysis and decision-making model based on game theory and neural network is constructed. It mainly aims at enterprise decision-making. The method used is to simulate various decision-making processes of enterprises by establishing neural network model and game model. At the same time, the image simulation of the model is carried out. The results show that the highest market share of the selected products is 36.1%, and the highest brand awareness is 9 points. The product with the second market share has better quality, 8 brand awareness points, and the highest dealer fee (2.3 yuan). Market share is less affected by product price and dealer expenses. The accuracy of the designed market share neural network model is 93%. This shows that the increase of market share is not realized simply by reducing the price but by increasing the profits of distributors and improving the brand image. Market leaders have the greatest revenue and profits. There is a positive correlation between the efforts of managers and the results achieved. Internet employees’ work effort is positively correlated with their basic salary. Different decisions have different effects on business models. The research of this paper provides a new idea for the innovation and development of Internet enterprise business model.

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Identification of acoustic emission sources for structural health monitoring applications based on convolutional neural networks and deep transfer learning

Cited by 45 publications

References 31 publications

Monitoring a Bolted Vibrating Structure Using Multiple Acoustic Emission Sensors: A Benchmark

Monitoring a Bolted Vibrating Structure Using Multiple Acoustic Emission Sensors: A Benchmark

Application of a Deep Learning Neural Network for Voiding Dysfunction Diagnosis Using a Vibration Sensor

Construction of Internet Business Innovation Network Model and Data Image Research under the Background of Game Theory

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