By Tshilidzi Marwala
Condition tracking utilizing Computational Intelligence tools promotes a number of the methods accumulated less than the umbrella of computational intelligence to teach how situation tracking can be utilized to prevent gear disasters and prolong its precious existence, reduce downtime and decrease upkeep expenditures. The textual content introduces a number of signal-processing and pre-processing thoughts, wavelets and critical part research, for instance, including their makes use of in tracking and info the improvement of potent function extraction suggestions categorized into frequency-, time-frequency- and time-domain research. info generated via those recommendations can then be used for situation type using instruments such as:
fuzzy structures; tough and neuro-rough units; neural and Bayesian networks;hidden Markov and Gaussian combination versions; and aid vector machines.
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Extra resources for Condition Monitoring Using Computational Intelligence Methods: Applications in Mechanical and Electrical Systems
2 Measured frequency response function of a population of cylinders Sufficient data to define the relationship between the changes in physical parameters and the changes in FRFs must be generated. From this set of data, a functional mapping between the identity of fault and the FRFs was identified using various computational intelligence tools that are described in this book. An example of a set of the FRFs which were obtained from a population of cylindrical shells is shown in Fig. 2 (Marwala 2001).
In: Proceedings of the international conference on computer intelligence and software engineering, Wuhan, China, pp 1–4 Zhang L, Huang AQ (2011) Model-based fault detection of hybrid fuel cell and photovoltaic direct current power sources. J Power Sour 196:5197–5204 Zhang Y, Suonan J (2010) Time domain fault location method based on UHV transmission line parameter identification using two terminal data. In: Proceedings of the Asia-Pacific power and energy engineering conference, Wuhan, China, pp 1–5 Zhou JH, Pang CK, Lewis FL, Zhong ZW (2011a) Dominant feature identification for industrial fault detection and isolation applications.
4) Here the ˛ is the frequency response function, ! is the frequency, [M] p is the 1. 4 Modal Properties 31 105 104 Inertance (m/s2/N) 103 102 101 100 10-1 10-2 10-3 10-4 0 500 1000 1500 2000 2500 3000 3500 Frequency (Hz) 4000 4500 5000 Fig. 2 Measured frequency response function of a population of cylinders Sufficient data to define the relationship between the changes in physical parameters and the changes in FRFs must be generated. From this set of data, a functional mapping between the identity of fault and the FRFs was identified using various computational intelligence tools that are described in this book.
Condition Monitoring Using Computational Intelligence Methods: Applications in Mechanical and Electrical Systems by Tshilidzi Marwala