Signal Processing
Evolutionary computing in manufacturing industry: an overview of recent applications
Applied Soft Computing
New fuzzy wavelet neural networks for system identification and control
Applied Soft Computing
Recursive weighted median filters admitting negative weights andtheir optimization
IEEE Transactions on Signal Processing
Active control of nonlinear noise processes in a linear duct
IEEE Transactions on Signal Processing
Using radial basis functions to approximate a function and its error bounds
IEEE Transactions on Neural Networks
An RBFN-Wiener hybrid filter using higher order signal statistics
Applied Soft Computing
CPBUM neural networks for modeling with outliers and noise
Applied Soft Computing
Engineering Applications of Artificial Intelligence
A direct adaptive neural command controller design for an unstable helicopter
Engineering Applications of Artificial Intelligence
An intelligent neural system for predicting structural response subject to earthquakes
Advances in Engineering Software
Structural damage detection using fuzzy cognitive maps and Hebbian learning
Applied Soft Computing
Advances in Artificial Intelligence
Hi-index | 0.00 |
The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54-71 and 59-73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications.