Fundamentals Of Neural Networks Architectures Algorithms And Applications

Rotating machinery is widely applied in various types of industrial applications. As a promising field for reliability of modern industrial systems, early fault diagnosis (EFD) techniques have attracted increasing attention from both academia and industry. EFD is critical to provide appropriate information for taking necessary maintenance actions and thereby prevent severe failures and reduce financial losses. A massive amounts of research work has been conducted in last two decades to develop EFD techniques. This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings. The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis. After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based methods. Finally, a summary and some new research prospects are discussed.

https://www.mdpi.com/1099-4300/21/4/409/pdf

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery.

An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process

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.

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

Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

/pdf/basic-research-on-machinery-fault-diagnostics-past-present-2pjq0tijdu.pdf

Basic research on machinery fault diagnostics: Past, present, and future trends

Bearing Fault Diagnosis using Wavelet Packet Transform, Hybrid PSO and Support Vector Machine☆

A Gear Fault Identification using Wavelet Transform, Rough set Based GA, ANN and C4.5 Algorithm

Early fault detection is a challenge in gear fault diagnosis. In particular, efficient feature extraction and feature selection is a key issue to automatic condition monitoring and fault diagnosis processes. In order to focus on those issues, this paper presents a study that uses ensemble empirical mode decomposition (EEMD) to extract features and hybrid binary bat algorithm (HBBA) hybridized with machine learning algorithm to reduce the dimensionality as well to select the predominant features which contains the necessary discriminative information. Efficiency of the approaches are evaluated using standard classification metrics such as Nearest neighbours, C4.5, DTNB, K star and JRip. The gear fault experiments were conducted, acquired the vibration signals for different gear states such as normal, frosting, pitting and crack, under constant motor speed and constant load. The proposed method is applied to identify the different gear faults at early stage and the results demonstrate its effectiveness.

https://www.jvejournals.com/article/15774/pdf

Diagnostics of gear faults using ensemble empirical mode decomposition, hybrid binary bat algorithm and machine learning algorithms

Internet of Underwater Things (IoUT) have gained rapid momentum over the past decade with applications spanning from environmental monitoring and exploration, defence applications, etc. The traditional IoUT systems use machine learning (ML) approaches which cater the needs of reliability, efficiency and timeliness. However, an extensive review of the various studies conducted highlight the significance of data privacy and security in IoUT frameworks as a predominant factor in achieving desired outcomes in mission critical applications. Federated learning (FL) is a secured, decentralized framework which is a recent development in machine learning, that will help in fulfilling the challenges faced by conventional ML approaches in IoUT. This paper presents an overview of the various applications of FL in IoUT, its challenges, open issues and indicates direction of future research prospects.

Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities

Big data is a huge collection of data from various sources. It can be of any type and tough to be interpreted and analyses hence we need some tool or mechanic that can easily analyses the data and give us some information out of it. Among various interesting tools R and tableau are the tools which deals with the big data analytics also it generates the output in visualization technique i.e., more understandable and presentable. In this paper we are comparing and contrasting the working of both the tools with some big dataset along with the importance and need of the tool in the field of big data analytics. This study gives the clear picture of growing data and the tools which can help more effectively, accurately and efficiently.

https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042052/pdf

C. Rajeswari

Papers

Bearing Fault Diagnosis using Wavelet Packet Transform, Hybrid PSO and Support Vector Machine☆

A Gear Fault Identification using Wavelet Transform, Rough set Based GA, ANN and C4.5 Algorithm

Diagnostics of gear faults using ensemble empirical mode decomposition, hybrid binary bat algorithm and machine learning algorithms

Federated Learning for IoUT: Concepts, Applications, Challenges and Opportunities

Comparative Study of Big data Analytics Tools: R and Tableau