Identification of Submarine Mechanical Noise Sources on Sparse Data

Identification of major mechanical noise sources is a key step of the noise control in submarine.But it is usually difficult to obtain enough training samples due to the high expense of testing.So it can be regarded as a pattern recognition problem on sparse data.In order to improve the generalization ability of the classifier,six different algorithms are proposed.The BAGGING method is chosen as the ensemble algorithm.And without the loss of generality,the classification and regression tree(CART)and back-propagation(BP)algorithm are chosen as basic classifiers.Simply combing BAGGING with CART and BP algorithm respectively,two algorithm called B-CART and B-BP algorithm are proposed.Considering the priori:①more than one data channel(accelerometer,hydrophone,etc.)is frequently used in most cases;②fusing the information from multiple sensors can enhance the reliability of the classification rate,two algorithms called B-CART-M and B-BP-M are proposed.Furthermore,after calculating the classification result of signal in one channel by BAGGING and plurality voting first,the final classification result from all sensory channels is obtained by the second voting,which is the main idea of algorithms called B-CART-M' and B-BP-M'. Measurements on a submerged full scale model section of a submarine hull showed:①all the six algorithms proposed above can improve the classification rate and the performance of B-CART-M' and B-BP-M' is the best;②for the given algorithm,the classification rate of outer shell data is the highest and far field lowest.Several suggestions of application of the six proposed algorithms are given.