This chapter introduces the finite element method (FEM) as a tool for solution of classical electromagnetic problems. Although we discuss the main points in the application of the finite element method to electromagnetic design, including formulation and implementation, those who seek deeper understanding of the finite element method should consult some of the works listed in the bibliography section.

Introduction to the Finite Element Method

Applications of machine learning to machine fault diagnosis: A review and roadmap

The paper provides a state of the art review of guided wave based structural health monitoring (SHM). First, the fundamental concepts of guided wave propagation and its implementation for SHM is explained. Following sections present the different modeling schemes adopted, developments in the area of transducers for generation, and sensing of wave, signal processing and imaging technique, statistical and machine learning schemes for feature extraction. Next, a section is presented on the recent advancements in nonlinear guided wave for SHM. This is followed by section on Rayleigh and SH waves. Next is a section on real-life implementation of guided wave for industrial problems. The paper, though briefly talks about the early development for completeness,. is primarily focussed on the recent progress made in the last decade. The paper ends by discussing and highlighting the future directions and open areas of research in guided wave based SHM.

https://iopscience.iop.org/article/10.1088/0964-1726/25/5/053001/pdf

Guided wave based structural health monitoring: A review

random data analysis and measurement procedures is available in our digital library an online access to it is set as public so you can get it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the random data analysis and measurement procedures is universally compatible with any devices to read.

Random Data Analysis And Measurement Procedures

Acting Under Secretary of Health requested that a working group be established to develop the Veterans Health Initiative (VHI). He envisioned this as a comprehensive program to recognize the connection between certain health effects and military service, to allow military history to be better documented, to prepare health care providers to better serve their veteran patients, and to establish a data base for further study. This was first discussed by the Acting Under Secretary in relation to the health of former prisoners of war. Development was really begun by the former Chief Academic Affairs Officer, Dr. David Stevens, with the Military Service History project. This involves a pocket card for medical residents detailing the important components of a military service history targeting the health risks associated with various periods of service and more generic issues of concern and a website containing references relevant to the issues. Educational modules in the Veterans Health Initiative VHA will assist health care providers in recognizing the connection between certain health effects and military service, prepare health care providers to better serve veteran patients, and will provide a data base for further study. This independent study is designed to provide primary care practitioners with an introduction to the pathologies that lead to sight loss, their functional implications, appropriate method of referrals, training programs, and special considerations for interactions with visually impaired individuals. After completing this independent study, participants would be able to: • Define legal blindness; • Describe the causes of sight loss; • Delineate the functional implications of vision loss • Delineate the psycho/social impact of vision loss on the veteran; • Outline the role of the Visual Impairment Services Team (VIST) in the treatment of legally blind veterans and the referral process; • Describe the special personal and environmental considerations needed for visually impaired patients; • Describe the special medical considerations needed for visually impaired patients; • Describe the primary care practitioner's role in assisting veterans in establishing well-grounded claims for disability related to the loss of vision; and • Describe compensation and pension benefits provided for veterans with eye disabilities. After completing this independent study, you should 1. be able to: state the definition of legal blindness; 2. be able to: associate eye diseases with their visual implications; 3. be able to: demonstrate insight into the functional and Psycho/Social implications of sight-loss; 4. know when referrals to VIST are indicated; 5. understand …

시력 손상과 시각 장애(Visual Impairment and Blindness)

In the age of industry 4.0, deep learning has attracted increasing interest for various research applications. In recent years, deep learning models have been extensively implemented in machinery fault detection and diagnosis (FDD) systems. The deep architecture’s automated feature learning process offers great potential to solve problems with traditional fault detection and diagnosis (TFDD) systems. TFDD relies on manual feature selection, which requires prior knowledge of the data and is time intensive. However, the high performance of deep learning comes with challenges and costs. This paper presents a review of deep learning challenges related to machinery fault detection and diagnosis systems. The potential for future work on deep learning implementation in FDD systems is briefly discussed.

/pdf/challenges-and-opportunities-of-deep-learning-models-for-3fwh0bkrtc.pdf

Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review

This paper reviews the state-of-the-art in numerical wave propagation analysis. The main focus in that regard is on guided wave-based structural health monitoring (SHM) applications. A brief introduction to SHM and SHM-related problems is given, and various numerical methods are then discussed and assessed with respect to their capability of simulating guided wave propagation phenomena. A detailed evaluation of the following
methods is compiled: (i) analytical methods, (ii) semi-analytical methods, (iii) the local interaction simulation approach (LISA), (iv) finite element methods (FEMs), and (v) miscellaneous methods such as mass–spring lattice models (MSLMs), boundary element methods (BEMs), and fictitious domain methods. In the framework of the FEM, both time and frequency domain approaches are covered, and the advantages of using high order shape functions are also examined.

Simulation Methods for Guided Wave-Based Structural Health Monitoring: A Review

Massive volumes of data are needed for deep learning (DL) models to provide accurate diagnosis results. Numerous studies of fault diagnosis systems have demonstrated the effectiveness of DL models over shallow machine learning (SL) in terms of feature extraction, feature dimensional reduction and diagnosis performance. Occasionally, during data acquisition, a problem with a sensor renders some of the data potentially unsuitable for further analysis, leaving only a small data sample. To compensate for this deficiency, a DL model based on a stacked sparse autoencoder (SSAE) model is designed to deal with limited sample data. In this article, the fault diagnosis system is developed based on time-frequency image pattern recognition. Therefore, two gearbox datasets are used to evaluate the proposed diagnosis system. The results from the experiments prove that the proposed system is capable of achieving high diagnostic accuracy even with limited sample data. The proposed fault diagnosis system achieved 100% and 99% diagnosis performance on experimental gearbox and wind turbine gearbox datasets, respectively. The proposed diagnosis system increased diagnosis performance between 10% and 20% over the standard SSAE model. In addition, the proposed model achieved higher diagnosis performance compared to deep neural network and convolutional neural networks models.

Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample

Lamb wave propagation in plates is multi-modal, dispersive and highly dependent on the material properties. In homogeneous isotropic plates, Lamb modes can be grouped into symmetric and anti-symmetric modes, and they are decoupled from the shear modes. Due to the material isotropy, Lamb wave propagation behavior is independent from the propagation direction. This property can be used to find analytical solutions for the field of displacements in the frequency domain. However, in composite plates having anisotropic material properties, Lamb wave propagation behavior depends on the propagation direction. For this reason, the complexity of Lamb wave propagation modes increases, and no direct analytical solutions are available. Thus, numerical methods need to be used. In this paper, the semi-analytical finite element (SAFE) method is applied as it is suitable for both isotropic homogeneous plates and anisotropic composite laminated plates. Dispersion curves for these complex materials are calculated using SAFE. The effect of obstacles on the reflection and transmission of Lamb waves is considered. SAFE is also used for 2D point force response analysis. By applying the 2D point response analysis for perfectly bonded strip actuators, mode tuning behavior is calculated for general composite plates. A method to consider plate edge reflections in the 2D force response analysis is also presented.

Semi-analytical finite element method for modeling of lamb wave propagation

https://www.ovgu.de/ifme/l-numerik/quellen/Ahmad_2012_Ultrasonic.pdf

Zair Asrar Ahmad

Papers

Challenges and Opportunities of Deep Learning Models for Machinery Fault Detection and Diagnosis: A Review

Simulation Methods for Guided Wave-Based Structural Health Monitoring: A Review

Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample

Semi-analytical finite element method for modeling of lamb wave propagation

Simulation of lamb wave reflections at plate edges using the semi-analytical finite element method