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Reliability engineering for industrial processes = an analytics perspective /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Reliability engineering for industrial processes/ edited by P. K. Kapur ... [et al.].
Reminder of title:	an analytics perspective /
other author:	Kapur, P. K.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	vii, 397 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Comparison of OSS Reliability Assessment Methods by Using Wiener Data Preprocessing Based on Deep Learning -- Reliability and sensitivity analysis of a wastewater treatment plant operating with two blowers as a single system -- A Review on Cardiovascular Disease / Heart Disease By Machine Learning Prediction -- A Role of Network Data Envelopment Analysis Approach in Manufacturing Industry: Review of last 5 years -- Exploring Software Systems Engineering through complexity of code changes: A Study based on bugs count, features improvement and new add-ons -- Generating image captions in Hindi based on Encoder-Decoder based Deep Learning techniques -- Fault Removal Efficiency: A Key Driver in Software Reliability Growth Modelling -- Analysis of Progressively Censored Repair Time of Airborne Communication Transceiver with Burr-Hatke Exponential Model -- Bug Prediction Techniques: Analysis and Review -- A Review on Kidney Failure Prediction Using Machine Learning Models -- Machine Learning Based Remaining Useful Life Estimation - Concept and Case Study -- Modelling Software Reliability Growth incorporating Testing Coverage Function and Fault Reduction Factor -- Software Defect Prediction using Abstract Syntax Trees Features and Object - Oriented metrics -- A Review of Alzheimer's Disease Identification by Machine Learning -- Weighted Entropic and Divergence Models in Probability Spaces and their Solicitations for Influencing an Imprecise Distribution -- Considering multiplicative noise in a software reliability growth model using stochastic differential equation -- An Insight into Code Smell Detection Tool -- Efficiency of Divergence Measure in Fuzzy TOPSIS Algorithm for Multi-Attribute Decision Making -- Exploring the Impact of Latent and Obscure Factors on Left-Censored Data: Bayesian Approaches and Case Study -- Reliability Perspective of Software and Hardware Models -- Stress-Strength Modelling for a New Modified Lindley Distribution Under Progressively Censored Data -- Imperfect debugging, testing coverage, and compiler error-based SRGM with two types of faults under the uncertainty of the operating environment -- A statistical approach to estimate severe accident vehicle collision probability inside a multi-lane road tunnel with unidirectional traffic flow.
Contained By:	Springer Nature eBook
Subject:	Reliability (Engineering) -
Online resource:	https://doi.org/10.1007/978-3-031-55048-5
ISBN:	9783031550485

Reliability engineering for industrial processes = an analytics perspective /
Reliability engineering for industrial processesan analytics perspective /[electronic resource] :edited by P. K. Kapur ... [et al.]. - Cham :Springer Nature Switzerland :2024. - vii, 397 p. :ill. (some col.), digital ;24 cm. - Springer series in reliability engineering,2196-999X. - Springer series in reliability engineering..

Comparison of OSS Reliability Assessment Methods by Using Wiener Data Preprocessing Based on Deep Learning -- Reliability and sensitivity analysis of a wastewater treatment plant operating with two blowers as a single system -- A Review on Cardiovascular Disease / Heart Disease By Machine Learning Prediction -- A Role of Network Data Envelopment Analysis Approach in Manufacturing Industry: Review of last 5 years -- Exploring Software Systems Engineering through complexity of code changes: A Study based on bugs count, features improvement and new add-ons -- Generating image captions in Hindi based on Encoder-Decoder based Deep Learning techniques -- Fault Removal Efficiency: A Key Driver in Software Reliability Growth Modelling -- Analysis of Progressively Censored Repair Time of Airborne Communication Transceiver with Burr-Hatke Exponential Model -- Bug Prediction Techniques: Analysis and Review -- A Review on Kidney Failure Prediction Using Machine Learning Models -- Machine Learning Based Remaining Useful Life Estimation - Concept and Case Study -- Modelling Software Reliability Growth incorporating Testing Coverage Function and Fault Reduction Factor -- Software Defect Prediction using Abstract Syntax Trees Features and Object - Oriented metrics -- A Review of Alzheimer's Disease Identification by Machine Learning -- Weighted Entropic and Divergence Models in Probability Spaces and their Solicitations for Influencing an Imprecise Distribution -- Considering multiplicative noise in a software reliability growth model using stochastic differential equation -- An Insight into Code Smell Detection Tool -- Efficiency of Divergence Measure in Fuzzy TOPSIS Algorithm for Multi-Attribute Decision Making -- Exploring the Impact of Latent and Obscure Factors on Left-Censored Data: Bayesian Approaches and Case Study -- Reliability Perspective of Software and Hardware Models -- Stress-Strength Modelling for a New Modified Lindley Distribution Under Progressively Censored Data -- Imperfect debugging, testing coverage, and compiler error-based SRGM with two types of faults under the uncertainty of the operating environment -- A statistical approach to estimate severe accident vehicle collision probability inside a multi-lane road tunnel with unidirectional traffic flow.

This book explores how transformative changes driven by the new-age economy can bring about improvements in a company's engineering and manufacturing capabilities. The new-age economy is driven by advanced engineering and manufacturing practices, processes, and technologies, including the Internet of Things (IoT), Cloud Computing, Blockchain, Artificial Intelligence, Robotics, Cyber-Physical Systems (CPS), and Internet-enabled systems to automate industrial processes. Today's business dynamics are governed by uncertainties, disruptions, complexities, and ambiguities that demand quicker and more intelligent decisions. These changes could relate a renaissance in the company's engineering and manufacturing capabilities. To sustain these volatile and ever-changing business dynamics, Industry 4.0 and 5.0 have revolutionized how organizations operate and make intelligent business decisions. Moreover, the extensive role of business analytics has overcome the limitations of classical computing through new technologies and intelligent computing methodologies. Over the past few years, much emphasis has been given to investing in developing hardware and programming frameworks for achieving computational intelligence using fuzzy logic, evolutionary computation, neural networks, probabilistic methods, and learning theory. Within this frame of reference, the reliability, quality, and maintenance of complex industrial and manufacturing systems are essential for organizations to utilize them successfully for informed decisions. This book focuses on studies that provide new solutions for system reliability, quality, security, and maintainability using quantitative and qualitative research. It emphasizes developments and problems in systems engineering management, systems integration, software and hardware engineering, and the development process.

ISBN: 9783031550485

Standard No.: 10.1007/978-3-031-55048-5doiSubjects--Topical Terms:

647853
Reliability (Engineering)

LC Class. No.: TA169

Dewey Class. No.: 620.00452

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520 $a This book explores how transformative changes driven by the new-age economy can bring about improvements in a company's engineering and manufacturing capabilities. The new-age economy is driven by advanced engineering and manufacturing practices, processes, and technologies, including the Internet of Things (IoT), Cloud Computing, Blockchain, Artificial Intelligence, Robotics, Cyber-Physical Systems (CPS), and Internet-enabled systems to automate industrial processes. Today's business dynamics are governed by uncertainties, disruptions, complexities, and ambiguities that demand quicker and more intelligent decisions. These changes could relate a renaissance in the company's engineering and manufacturing capabilities. To sustain these volatile and ever-changing business dynamics, Industry 4.0 and 5.0 have revolutionized how organizations operate and make intelligent business decisions. Moreover, the extensive role of business analytics has overcome the limitations of classical computing through new technologies and intelligent computing methodologies. Over the past few years, much emphasis has been given to investing in developing hardware and programming frameworks for achieving computational intelligence using fuzzy logic, evolutionary computation, neural networks, probabilistic methods, and learning theory. Within this frame of reference, the reliability, quality, and maintenance of complex industrial and manufacturing systems are essential for organizations to utilize them successfully for informed decisions. This book focuses on studies that provide new solutions for system reliability, quality, security, and maintainability using quantitative and qualitative research. It emphasizes developments and problems in systems engineering management, systems integration, software and hardware engineering, and the development process.
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