An Intelligent Condition Monitoring Approach for Spent Nuclear Fuel Shearing Machines Based on Noise Signals

Usually, noise–based fault diagnosis methods include acquiring noise signals of running equipments, extracting features from the noise signals, and making decisions through pattern recognition technology. [...]the key points of noise–based fault diagnosis methods are selecting the feature extraction...

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Bibliographic Details
Published in:Applied sciences 2018-05, Vol.8 (5), p.838
Main Authors: Chen, Jia-Hua, Zou, Shu-Liang
Format: Article
Language:English
Subjects:
Advantages
artificial neural network (ANN)
condition monitoring
Critical point
Empirical equations
Energy
Fault diagnosis
Fourier transforms
Frequencies
hidden Markov model (HMM)
International conferences
Methods
Neural networks
Neurons
Noise
Normalizing
Nuclear fuels
Pattern recognition
Sensors
Shearing
shearing machine
Signal processing
Spent nuclear fuels
Time series
Voice recognition
wavelet packet transform (WPT)
Wavelet transforms
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Summary:Usually, noise–based fault diagnosis methods include acquiring noise signals of running equipments, extracting features from the noise signals, and making decisions through pattern recognition technology. [...]the key points of noise–based fault diagnosis methods are selecting the feature extraction method and determining the pattern recognition model [7]. [...]the time domain and frequency domain (e.g., Fourier transform) methods have limitations for complex and non–stationary signal processing [8]. The total energy of the signal is calculated with: ET=∑i=02n−1Ei. [...]for a n–level WPT feature extraction, the energy feature vector is obtained by normalizing the energy of every frequency band in the nth layer: T=E0 ET,E1 ET,⋯,E2n−1ET. The number of neurons in the hidden layer is mainly determined by tests, while the following empirical equation gives a preliminary range: h=(m+n)+a where m is the number of neurons in the input layer, n the number of neurons in the output layer, and a an integer constant between 1 and 10. Besides the network structures, the activation functions are critical points for the performance of ANNs. (b) Setting training parameters, e.g., selections of initial weight, threshold, goal, learning rate, momentum factor, maximum epochs, as well as selections of learning function, training function, and performance function.
ISSN:2076-3417
2076-3417
DOI:10.3390/app8050838