{"product_id":"unsupervised-process-monitoring-and-fault-diagnosis-with-machine-learning-methods-9781447171607","title":"Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods","description":"\u003cp\u003e • Author(s): Chris Aldrich\u003cbr\u003e • Publisher: Springer\u003cbr\u003e • Publisher Imprint: Springer\u003cbr\u003e • BISAC: Artificial Intelligence - General\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFrom the Back Cover\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eAlgorithms for intelligent fault diagnosis of automated operations offer significant benefits to the manufacturing and process industries. Furthermore, machine learning methods enable such monitoring systems to handle nonlinearities and large volumes of data.\u003c\/p\u003e\u003cp\u003eThis unique text\/reference describes in detail the latest advances in \u003ci\u003eUnsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods\u003c\/i\u003e. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eTopics and features: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eReviews the application of machine learning to process monitoring and fault diagnosis\u003c\/li\u003e\n\u003cli\u003eDiscusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods\u003c\/li\u003e\n\u003cli\u003eExamines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning\u003c\/li\u003e\n\u003cli\u003eDescribes the use of spectral methods in process fault diagnosis\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eThis highly practical and clearly-structured work is an invaluable resource for all researchers and practitioners involved in process control, multivariate statistics and machine learning.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eDr. Chris Aldrich\u003c\/b\u003e is a Professor in the Department of Metallurgical and Minerals Engineering at Curtin University, Perth, Australia. \u003cb\u003eDr. Lidia Auret\u003c\/b\u003e is a Lecturer in the Department of Process Engineering at Stellenbosch University, South Africa.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Paperback","offer_id":45274210009239,"sku":"9781447171607","price":8447.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9781447171607.webp?v=1769279229","url":"https:\/\/atlanticbooks.com\/products\/unsupervised-process-monitoring-and-fault-diagnosis-with-machine-learning-methods-9781447171607","provider":"Atlantic Books","version":"1.0","type":"link"}