Abstract
Fault detection and state estimation using acoustic signals is a procedure highly affected by ambient noise. This is particularly pronounced in an industrial environment where noise pollution is especially strong. In this paper a noise detection algorithm is proposed and implemented. This algorithm can identify the times in which the recorded acoustic signal is influenced by different types of noise in the form of unwanted impulse disturbance or speech contamination. The algorithm compares statistical parameters of the recordings by generating a series of QQ plots and then using an appropriate stochastic signal analysis tools like hypothesis testing. The main purpose of this algorithm is to eliminate noisy signals and to collect a set of noise free recordings which can then be used for state estimation. The application of these techniques in a real industrial environment is extremely complex because sound contamination usually tends to be intense and nonstationary. The solution described in this paper has been tested on a specific problem of acoustic signal isolation and noise detection of a coal grinding fan mill in thermal power plant in the presence of intense contaminating sound disturbances, mainly impulse disturbance and speech contamination.
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