AI and Machine Learning for Mechanical and Electrical Engineering ...

Name: AI and Machine Learning for Mechanical and Electrical Engineering ...
Author: Unknown
ISBN: 9781032759487

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First edition

PublisherCRC PressPublished2026pages331LanguageEnglishISBN-139781032759487ISBN-101032759488FormatPDF

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Intelligent Scheduling for Load Distribution in Power GridsOptimization of Tribological Parameters for Bio-CompositesIoT and Sensor-Based Smart Farming and BioinformaticsCrude Oil Storage Health MonitoringSurface Water Analysis with ANFISSmart Grid Protection and ClassificationAnomaly Detection in Electric MotorsAI and Cybersecurity in 6G Wireless NetworksAnomaly Detection and Root Cause Analysis

About this book

Practical and informative, AI and Machine Learning for Mechanical and Electrical Engineering examines how artificial intelligence (AI) is changing the status quo in mechanical engineering, electrical systems, and management. Real-world examples and case studies demonstrate the application of AI in such diverse settings as industry and policymaking. This book illustrates how AI is playing a crucial role in enhancing productivity and innovation in various industries. It discusses transition methods and the ethical implications of using AI in mechanical engineering. Chapter highlights include the following:

Developing a smart algorithm to integrate fault detection and classification
Algorithms to investigate different testing scenarios for various anomalies in electric motors
Data fusion to detect and assess electromechanical damage
Neural networks for rolling bearing fault diagnosis
Evolutionary algorithms to optimize deep learning models for water industry forecasts
AI-based anomaly detection and root-cause analysis

An overarching theme is the transition from traditional mechanical, electrical, and management systems to AI-enabled smart systems. The book helps readers make sense of the challenges of integrating smart systems. It equips engineers with theoretical understanding as well as insight based on hands-on expertise. It shows how to better link and automate systems and improve productivity. This book not only shows how to implement smart solutions now but also shows the way to a more intelligent, productive, and interconnected future.

Questions & Answers from this book

8 questions7 chapters covered18 topics

Questions and answers are connected to the referenced book and its available source material.

Chapter 3: A Data Fusion Technique to Detect and Assess Electromechanical Damage

What is the main advantage of the proposed LSTM RNN-based model over current sensors in detecting FDIA?

The main advantage is that the proposed LSTM RNN-based model maintains high accuracy in detecting FDIA across a variety of contexts, whereas current sensors experience a considerable decline in accuracy. It also withstands the stresses of modern electricity grids with ease.

Intermediatep. 72

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Chapter 7: The Implementation of Artificial Intelligence for Auto Gearbox Failure Detection

According to the chapter, which machine learning algorithm achieved the highest classification accuracy for gearbox vibration data, and what accuracy was reported for gear 4 at 750 rpm in Table 7.6?

The chapter identifies support vector machine (SVM) as the top-performing algorithm for gearbox vibration data. In Table 7.6, the SVM accuracy reported for gear 4 at 750 rpm is 86.8%.

Intermediatep. 112-124

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Chapter 8: Evolutionary Algorithms to Optimise Deep Learning Model for Water Industry Forecasts

What are the limitations of mechanistic models in wastewater treatment, and how do data-driven models like deep learning address these limitations?

Mechanistic models in wastewater treatment are based on mass conservation and biochemical rate equations, which approximate aggregated biological groups. They are useful for design and interpretation but cannot capture all behaviors, especially in complex, data-rich systems or when governing mechanisms are unknown. Data-driven models like deep learning address these limits by learning directly from large sensor datasets, and architectures such as Bi-LSTM can capture both short- and long-term process dynamics, improving effluent quality prediction and nutrient removal forecasting.

Intermediatep. 126-143

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Chapter 10: Artificial Intelligence and Internet of Things-Based Intelligent Scheduling for Load Distribution in Power Grids

According to the experimental results, by what percentage does the proposed source–grid–load–storage collaborative scheduling reduce the average load adjustment compared to no coordinated scheduling and compared to the existing mechanism that only considers energy storage?

The proposed source–grid–load–storage collaborative scheduling reduces the average load adjustment by 50% compared to no coordinated scheduling and by 40% compared to the existing mechanism that only considers energy storage.

Intermediatep. 177-183

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What are the two main methods for peak shaving and valley filling in power grids, and how does UPIoT enable them to work together?

The two main methods are user-side policy measures such as time-of-use pricing that encourage voluntary load shifting, and coordinated dispatch of flexible loads and energy storage devices that discharge during peaks and charge during valleys. Under UPIoT, data barriers between the grid and users are broken, so real-time user consumption intentions can be fed into the distribution node and combined with controllable loads and storage in a single coordinated dispatch, allowing both methods to work together more effectively.

Intermediatep. 160-167

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Chapter 12: Smart Farming and Human Bioinformatics Systems Based on IoT and Sensor Devices

What are the four layers of the basic IoT architecture as described in the chapter?

The chapter describes the basic IoT architecture as consisting of four layers: the sensing layer, network layer, processing layer, and application layer. The sensing layer gathers data from the environment using sensors and actuators. The network layer provides communication and connectivity among IoT components. The processing layer analyzes and interprets data, and the application layer interacts with end users.

Introductoryp. 207-212

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Chapter 13: Machine Learning Techniques Applied in Predictive Maintenance: A Review

What are the five consecutive stages of the intelligent PdM planning model introduced by Mustufa Haider Abidi et al., and what was the purpose of using SVM and RNN in that model?

The five consecutive stages are data cleansing, data normalization, optimal feature selection, precision network decision-making, and prediction. In the model, SVM was used to identify a network that could handle prediction values with different ranges, while RNN was employed to provide predictions throughout a variety of ranges, with the J-SLnO algorithm optimizing the RNN's weights.

Intermediatep. 235-244

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Chapter 17: Cybersecurity Education Gamification: A Current Review and Research Agenda

What are the potential benefits and drawbacks of using gamification in cybersecurity education as discussed in the chapter?

The chapter reports that gamification in cybersecurity education can improve learner motivation, engagement, information retention, and practical problem-solving skills, partly by letting students apply theory through real-world simulations. However, it also notes drawbacks such as high resource demands for developing and maintaining gamified platforms, the need to train instructors in both cybersecurity and game design, and the risk that poorly integrated gamification will distract students from learning objectives rather than support them.

Intermediatep. 305-312

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