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Machining. --- Machinery --- Machine learning. --- Monitoring. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Condition monitoring of machinery --- Monitoring of machinery --- Nondestructive testing --- Materials --- Machine-shop practice --- Manufacturing processes --- Cutting --- Machine-tools --- Machining

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Manufacturing systems and processes are becoming increasingly complex, making more rational decision-making in process control a necessity. Better information gathering and analysis techniques are needed and condition monitoring is gaining attention from researchers worldwide as a framework that will enable these improvements. Condition Monitoring and Control for Intelligent Manufacturing brings together the world’s authorities on condition monitoring to provide a broad treatment of the subject accessible to researchers and practitioners in manufacturing industry. The book presents a wide and comprehensive review of the key areas of research in machine condition monitoring and control, before focusing on an in-depth treatment of each important technique, from multi-domain signal processing for defect diagnosis to web-based information delivery for real-time control. Condition Monitoring and Control for Intelligent Manufacturing is a valuable resource for researchers in manufacturing and control engineering, as well as practising engineers in industries from automotive to packaging manufacturing. The Springer Series in Advanced Manufacturing publishes the best teaching and reference material to support students, educators and practitioners in manufacturing technology and management. This international series includes advanced textbooks, research monographs, edited works and conference proceedings covering all subjects in advanced manufacturing. The series focuses on new topics of interest, new treatments of more traditional areas and coverage of the applications of information and communication technology (ICT) in manufacturing.

Machinery --- Manufacturing processes --- Monitoring. --- Automation. --- Condition monitoring of machinery --- Monitoring of machinery --- Nondestructive testing --- Industrial engineering. --- Engineering. --- Vibration. --- Computer aided design. --- Industrial and Production Engineering. --- Automotive Engineering. --- Vibration, Dynamical Systems, Control. --- Computer-Aided Engineering (CAD, CAE) and Design. --- CAD (Computer-aided design) --- Computer-assisted design --- Computer-aided engineering --- Design --- Cycles --- Mechanics --- Sound --- Construction --- Industrial arts --- Technology --- Management engineering --- Simplification in industry --- Engineering --- Value analysis (Cost control) --- Production engineering. --- Automotive engineering. --- Dynamical systems. --- Dynamics. --- Computer-aided engineering. --- CAE --- Dynamical systems --- Kinetics --- Mathematics --- Mechanics, Analytic --- Force and energy --- Physics --- Statics --- Manufacturing engineering --- Process engineering --- Industrial engineering --- Mechanical engineering --- Data processing

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This book discusses condition based monitoring of rotating machines using intelligent adaptive systems. The book employs computational intelligence and fuzzy control principles to deliver a module that can adaptively monitor and optimize machine health and performance. This book covers design and performance of such systems and provides case studies and data models for fault detection and diagnosis. The contents cover everything from optimal sensor positioning to fault diagnosis. The principles laid out in this book can be applied across rotating machinery such as turbines, compressors, and aircraft engines. The adaptive fault diagnostics systems presented can be used in multiple time and safety critical applications in domains such as aerospace, automotive, deep earth and deep water exploration, and energy. .

Machinery. --- Computational intelligence. --- Quality control. --- Reliability. --- Industrial safety. --- Machinery and Machine Elements. --- Computational Intelligence. --- Quality Control, Reliability, Safety and Risk. --- Industrial accidents --- Industries --- Job safety --- Occupational hazards, Prevention of --- Occupational health and safety --- Occupational safety and health --- Prevention of industrial accidents --- Prevention of occupational hazards --- Safety, Industrial --- Safety engineering --- Safety measures --- Safety of workers --- Accidents --- System safety --- Dependability --- Trustworthiness --- Conduct of life --- Factory management --- Industrial engineering --- Reliability (Engineering) --- Sampling (Statistics) --- Standardization --- Quality assurance --- Quality of products --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Machinery --- Machines --- Manufactures --- Power (Mechanics) --- Technology --- Mechanical engineering --- Motors --- Power transmission --- Prevention --- Curious devices --- Monitoring. --- Condition monitoring of machinery --- Monitoring of machinery --- Nondestructive testing

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This book broadens readers’ understanding of proactive condition monitoring of low-speed machines in heavy industries. It focuses on why low-speed machines are different than others and how maintenance of these machines should be implemented with particular attention. The authors explain the best available monitoring techniques for various equipment and the principle of how to get proactive information from each technique. They further put forward possible strategies for application of FEM for detection of faults and technical assessment of machinery. Implementation phases are described and industrial case studies of proactive condition monitoring are included. Proactive Condition Monitoring of Low-Speed Machines is an essential resource for engineers and technical managers across a range of industries as well as design engineers working in industrial product development. This book also: • Explains the practice of proactive condition monitoring and illustrates implementation phases • Presents detailed discussion of best techniques for proactive condition monitoring specifically for low-speed machines • Demonstrates application of finite element modeling for successful proactive condition monitoring • Broadens readers’ contextual understanding with case studies related to applications of proactive condition monitoring in heavy industry.

Engineering. --- Machinery and Machine Elements. --- Engine Technology. --- Quality Control, Reliability, Safety and Risk. --- Engineering Design. --- Engineering design. --- System safety. --- Ingénierie --- Conception technique --- Sécurité des systèmes --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Civil Engineering --- Machinery --- Engineering management. --- Monitoring. --- Condition monitoring of machinery --- Monitoring of machinery --- Machinery. --- Engines. --- Quality control. --- Reliability. --- Industrial safety. --- Nondestructive testing --- Design, Engineering --- Engineering --- Industrial design --- Strains and stresses --- Safety, System --- Safety of systems --- Systems safety --- Accidents --- Industrial safety --- Systems engineering --- Construction --- Industrial arts --- Technology --- Design --- Prevention --- Industrial accidents --- Industries --- Job safety --- Occupational hazards, Prevention of --- Occupational health and safety --- Occupational safety and health --- Prevention of industrial accidents --- Prevention of occupational hazards --- Safety, Industrial --- Safety engineering --- Safety measures --- Safety of workers --- System safety --- Dependability --- Trustworthiness --- Conduct of life --- Factory management --- Industrial engineering --- Reliability (Engineering) --- Sampling (Statistics) --- Standardization --- Quality assurance --- Quality of products --- Machines --- Manufactures --- Power (Mechanics) --- Mechanical engineering --- Motors --- Power transmission --- Curious devices

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Condition monitoring uses the observed operating characteristics of a machine or structure to diagnose trends in the signal being monitored and to predict the need for maintenance before a breakdown occurs. This reduces the risk, inherent in a fixed maintenance schedule, of performing maintenance needlessly early or of having a machine fail before maintenance is due either of which can be expensive with the latter also posing a risk of serious accident especially in systems like aeroengines in which a catastrophic failure would put lives at risk. The technique also measures responses from the whole of the system under observation so it can detect the effects of faults which might be hidden deep within a system, hidden from traditional methods of inspection. Condition Monitoring Using Computational Intelligence Methods promotes the various approaches gathered under the umbrella of computational intelligence to show how condition monitoring can be used to avoid equipment failures and lengthen its useful life, minimize downtime and reduce maintenance costs. The text introduces various signal-processing and pre-processing techniques, wavelets and principal component analysis, for example, together with their uses in condition monitoring and details the development of effective feature extraction techniques classified into frequency-, time-frequency- and time-domain analysis. Data generated by these techniques can then be used for condition classification employing tools such as: ·        fuzzy systems; ·        rough and neuro-rough sets; ·        neural and Bayesian networks; ·        hidden Markov and Gaussian mixture models; and ·        support vector machines. On-line learning methods such as Learn++ and ILUGA (incremental learning using genetic algorithms) are used to enable the classifiers to take on additional information and adjust to new condition classes by evolution rather than by complete retraining. Both the chosen methods have good incremental learning abilities with ILUGA, in particular, not suffering from catastrophic forgetting. Researchers studying computational intelligence and its applications will find Condition Monitoring Using Computational Intelligence Methods to be an excellent source of examples. Graduate students studying condition monitoring and diagnosis will find this alternative approach to the problem of interest and practitioners involved in fault diagnosis will be able to use these methods for the benefit of their machines and of their companies.

Artificial intelligence. --- Computational intelligence. --- Engineering. --- Machinery -- Monitoring -- Data processing. --- Nondestructive testing -- Data processing. --- Sensor networks. --- Structural control (Engineering). --- System safety. --- Mechanical Engineering --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Civil Engineering --- Mechanical Engineering - General --- Machinery --- Materials --- Monitoring. --- Monitoring --- Data processing. --- Testing. --- Intelligence, Computational --- Machines --- Condition monitoring of machinery --- Monitoring of machinery --- Curious devices --- Machinery. --- Quality control. --- Reliability. --- Industrial safety. --- Industrial engineering. --- Machinery and Machine Elements. --- Computational Intelligence. --- Artificial Intelligence (incl. Robotics). --- Signal, Image and Speech Processing. --- Quality Control, Reliability, Safety and Risk. --- Operating Procedures, Materials Treatment. --- Artificial intelligence --- Soft computing --- Management engineering --- Simplification in industry --- Engineering --- Value analysis (Cost control) --- Industrial accidents --- Industries --- Job safety --- Occupational hazards, Prevention of --- Occupational health and safety --- Occupational safety and health --- Prevention of industrial accidents --- Prevention of occupational hazards --- Safety, Industrial --- Safety engineering --- Safety measures --- Safety of workers --- Accidents --- System safety --- Dependability --- Trustworthiness --- Conduct of life --- Factory management --- Industrial engineering --- Reliability (Engineering) --- Sampling (Statistics) --- Standardization --- Quality assurance --- Quality of products --- Manufactures --- Power (Mechanics) --- Technology --- Mechanical engineering --- Motors --- Power transmission --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Construction --- Industrial arts --- Prevention --- Nondestructive testing --- Manufactures. --- Artificial Intelligence. --- Manufacturing, Machines, Tools, Processes. --- Safety, System --- Safety of systems --- Systems safety --- Industrial safety --- Systems engineering --- Manufactured goods --- Manufactured products --- Products --- Products, Manufactured --- Commercial products --- Manufacturing industries --- Signal processing. --- Image processing. --- Speech processing systems. --- Computational linguistics --- Electronic systems --- Information theory --- Modulation theory --- Oral communication --- Speech --- Telecommunication --- Singing voice synthesizers --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Optical data processing --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication)