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Dr. R Kumar is an Assistant Professor in the Department of Mechanical Engineering at M. S. Ramaiah Institute of Technology, Bangalore. His contribution focuses on machine learning applications for predictive maintenance in industrial systems.
Predictive maintenance has become an essential use of machine learning in contemporary industrial systems. Predictive maintenance, in contrast to conventional reactive and preventative measures, employs both real-time and historical operational data to discern equipment deterioration, identify abnormalities, forecast possible failures, and assess the remaining usable life of industrial assets. This section elucidates the essential principles, techniques, and uses of machine learning for anticipatory maintenance in industrial settings. It examines the collection of industrial data via sensors and Industrial Internet of Things (IIoT) frameworks, data preprocessing, feature engineering, machine learning methodologies, deep learning strategies, anomaly identification, defect diagnosis, and predictions of remaining useful life. The section moreover examines the amalgamation of edge computing, digital twins, explicable artificial intelligence, and federated learning to create scalable and intelligent upkeep frameworks. A cohesive machine learning framework for predictive maintenance is introduced, linking data collection, condition assessment, predictive modelling, failure evaluation, and maintenance planning. Ultimately, significant obstacles and prospective research avenues concerning data integrity, model elucidation, computational intricacy, cybersecurity, and instantaneous implementation are examined.
The swift advancement of self-driving and interconnected vehicles has generated an increasing need for sophisticated technologies that can comprehend intricate driving contexts, analyse substantial amounts of sensor information, and execute instantaneous decisions. Deep learning has become a crucial technique for facilitating observation, forecasting, decision-making, and regulation in intelligent transportation systems. This section elucidates the foundational concepts and primary utilisations of deep learning in self-driving and interconnected automobiles.The section additionally explores the amalgamation of deep learning with Vehicle-to-Vehicle, Vehicle-to-Infrastructure, and Vehicle-to-Cloud communication to advance collaborative and interconnected transportation frameworks. Innovative technologies include edge AI, federated learning, digital twins, explainable AI, and generative AI are examined in further detail. A comprehensive deep learning framework for self-driving and connected automobiles is introduced, along by an examination of issues pertaining to real-time processing, safety, cybersecurity, data privacy, robustness, and interpretability. The chapter offers an extensive examination of how deep learning might facilitate the advancement of secure, intelligent, adaptable, and interconnected mobility systems.
Predictive analytics has become a crucial element of contemporary engineering systems as it allows engineers to convert historical and real-time data into practical forecasts. The swift advancement of sensors, Internet of Things (IoT) devices, cloud computing, digital twins, and artificial intelligence has led to the accessibility of substantial quantities of engineering data. Machine learning (ML) offers computational methodologies for identifying patterns within datasets and constructing prediction models without the need to manually code every relationship between input variables and results. This chapter elucidates the essential principles of predictive analytics grounded on machine learning and explores frequently utilised supervised, unsupervised, and ensemble learning methods for engineering purposes The section elaborates on the predictive analytics process, encompassing data preparation, feature engineering, model training, validation, performance assessment, interpretability, and deployment. The study investigates applications in predictive maintenance, manufacturing, structural health assessment, energy systems, renewable energy forecasting, transportation, civil infrastructure, environmental surveillance, and industrial IoT. Ultimately, the chapter addresses obstacles like data integrity, model transparency, concept evolution, computing demands, cybersecurity threats, and ethical implications. The section illustrates how machine learning-based predictive analytics can facilitate dependable, effective, sustainable, and astute engineering decision-making.
Deep learning has revolutionised image processing and computer vision by allowing machines to autonomously acquire hierarchical representations from extensive visual datasets. In contrast to traditional image-processing methods that rely significantly on handcrafted characteristics, deep learning frameworks are capable of autonomously extracting pertinent spatial, semantic, and contextual features from unprocessed images. This chapter elucidates the essential principles of deep learning pertinent to image analysis and examines prominent architectures such as Convolutional Neural Networks (CNNs), AlexNet, VGG, GoogLeNet/Inception, ResNet, DenseNet, MobileNet, EfficientNet, U-Net, Generative Adversarial Networks (GANs), Vision Transformers (ViTs), and hybrid CNN-transformer models. Applications in healthcare, manufacturing, agriculture, autonomous transport, remote sensing, security, robotics, and infrastructure oversight are likewise discussed. The chapter ultimately explores difficulties with computing demands, data reliance, model interpretability, resilience, domain adaption, privacy, and implementation, concluding with prospective research avenues in efficient, explainable, multimodal, and reliable computer vision systems.