A research approach of crack detection of rotating shafts based on acoustic emission (AE) signals and machine learning is proposed in this paper. The relationship between crack intensity and domain features are investigated, and the features which could well indicate the crack condition are selected for modelling and crack prediction. Multiple Linear Regression (MLR), Artificial Neural Networks (ANN) and Adaptive Neural-Fuzzy Inference System (ANFIS) methods are used to establish the predictive correlation models by using selected features. A case study is carried out to emulate online working conditions of rotating shafts by using 10 normal shafts with 0.8mm – 8mm crack intensities. It is proved that AE signals can be used for earlier crack intensity detection, for example 0.8mm – 2.4 mm cracks can be fully detected according to experimental results in this study. Different modelling methods are also compared and discussed. Results show that ANFIS is a good choice in terms of overall predictive accuracy for earlier crack detection and prediction.

A simple method to compute the minimum distance D/sub min/ in multiuser code division multiple access (CDMA) systems based on the Cholesky decomposition of the positive-definite symmetric correlation matrix is proposed. Although finding D/sub min/ is known to be NP-hard, this decomposition allows the computation of the minimum distance and the asymptotic efficiency of optimum multiuser detection to be performed very efficiently in almost all cases of practical interest. Numerical results for synchronous CDMA with binary random signature waveforms of length 31 are used to illustrate the method.