Quality Control

Condition Monitoring Using Computational Intelligence by Tshilidzi Marwala

By Tshilidzi Marwala

Condition tracking utilizing Computational Intelligence equipment promotes a few of the techniques accrued less than the umbrella of computational intelligence to teach how tracking can be utilized to prevent gear mess ups and delay its important existence, reduce downtime and decrease upkeep expenses. The textual content introduces a number of signal-processing and pre-processing suggestions, wavelets and relevant part research, for instance, including their makes use of in tracking and info the improvement of potent characteristic extraction thoughts labeled into frequency-, time-frequency- and time-domain research. information generated through those innovations can then be used for class applying instruments such as:

fuzzy structures; tough and neuro-rough units; neural and Bayesian networks;hidden Markov and Gaussian blend versions; and help vector machines.

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Additional resources for Condition Monitoring Using Computational Intelligence Methods: Applications in Mechanical and Electrical Systems

Example text

4 Data-Logging System The National Instruments DAQCard 1,200 with 12-bits over a ˙5 V analoguedigital conversion was applied in the cylindrical shell example, to record the impulse force and the acceleration response. , 0–5 kHz). A Visual Basic program running on a Daytek desktop computer was used to control the DAQCard. This program was employed to start the data logging, set the sampling frequencies, to check the sample saturation and to save the data. After the raw data were measured and saved, they were then opened using MATLAB and checked as to whether they were acceptable or not by estimating the frequency response functions.

2 ! 2 ki li d! i ! 12) Assuming that the damping is low, Eq. 13 demonstrates that the inertance pseudo-modal energy may be expressed as a function of the modal properties. The inertance pseudo-modal energies may be estimated directly from the FRFs using any numerical integration scheme. This avoids going through the process of modal extraction. 5 Pseudo-Modal Energies 35 The advantages of using the pseudo-modal energies over the use of the modal properties are: • all the modes in the structure are taken into account, as opposed to using the modal properties, which are limited by the number of modes identified; and • integrating the FRFs to obtain the pseudo-modal energies smooths out the zeromean noise present in the FRFs.

Supervised neural networks are used to represent a mapping from an input vector onto an output vector, while unsupervised networks are used to classify the data without prior knowledge of the classes involved. The most common neural network architecture is the Multi-Layer Perceptron (MLP), trained using the back-propagation technique (Bishop 1995). An alternative network is the radial basis function (RBF) (Bishop 1995). 5 Strategies Used for Condition Monitoring 17 Kudva et al. (1991) used MLP neural networks to identify faults on a plate.

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