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condition monitoring, fault diagnosis, fault classification or fiding fault for machenary
Engineering and Construction
Pages 25 (6275 words)
CHAPTER SIX Condition Monitoring and Fault Classification Using Artificial Intelligent Techniques from Vibration reciprocating air compressor 1.1 Introduction Condition monitoring has been favoured in maintenance circles for long in order to detect and deal with errors as they develop.
In recent years, there has been a growing trend to introduce more intelligent methods in order to deal with condition monitoring and fault classification for machines (Mills, 2010). The realm of artificial intelligence and its application may be infant as yet but still involves the application of various methods and techniques for achieving desired ends. The current research will look into various artificial intelligence methods that have been applied to the condition monitoring and fault diagnosis for a reciprocating air compressor based on emerging and already developed methods and techniques. 1.2 Artificial IntelligenCe Based Methods It is possible to solicit problems in plant machinery using vibration signals that can be processed to reveal a multitude of information relating to the machine and its components as well as their operation (Wang & Chen, 2011). Given that condition monitoring and diagnosis relies largely on vibration feature analysis, it is important to extract the vibration signals at every state change that the machine experiences (Lin & Qu, 2000) (Wang & Chen, 2007). Extracting vibration features can often be difficult since the measured vibration patterns tend to contain a large amount of noise that must be filtered out (Wang & Chen, 2011). ...
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