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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)