Blind Source Separation Machinery And Equipment Acoustic Signal Feature Extraction Applications

In signal processing of engineering, the signals received by sensors are frequently mixed signals which come from many signal sources due to the signals from other sources together with additive noise. The Process of recovering original signals from mixed signals is called blind source separation(BSS) which has been successfully used in the fields of acoustic signal processing, biomedicine, earthquake date processing and communications.In this paper, the technology of blind source separation is applied to state monitoring and fault diagnosis of equipments. Based on the model of linear instantaneous blind source separation, the feature extraction of acoustic signals of equipments using the own varied property of signals is discussed. Three algorithms including Fast Independent Component Analysis(FastICA), Robust Second Blind Identification and Double Blind Separation are investigated and compared in various conditions, the results of theoretical analysis is applied to the simulation of signal processing, a large number of simulation experiments are carried out to verify the legitimacy of algorithms that discussed above.In the process of blind source separation, the number of signal source usually is unknown and it also may be variable dynamically, so the related estimation algorithms of the number of blind signal source are investigated. Based on traditional method of principal component analysis, a new algorithm on the basis of blind source extraction and correlation analysis is proposed, the validity of the algorithm is proved by lots of computer simulation experiments.Based on theoretical analysis and simulation experiments, the experiments of blind source separation were conducted in a half-anechoic room, the mixed noise was respectively generated by two loudhailers, a small drill and a fanner. Results of experiments show the validity of the methods proposed in the paper.