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An application of acoustic measurements to quality control of low power electrical motors

100%

Gibiec M.

2006

tom Vol. 31, No. 4

521-528

In this article, an example of the application of acoustic measurements to condition assessment of electric machines is presented. Quality control of new components is considered in this case study. The first symptoms of unserviceability are disclose in the acoustic domain and then, in later exploitation, they develop into a form that is detectable during vibration measurements. The assessment is based on acoustical wave spectrum normalized with respect to rotational speed. An adequately prepared neural network of the Kohonen's type was used as assessing tool. Its performance was compared to results of the minimum distance method based on averaged spectrum patterns.

Fault detection in railway point drive supported by data mining methods

100%

Gibiec M.

2007

tom Vol. 14, No. 4

611-619

In this work diagnostics of railway point drive supported by Data Mining methods was considered. Results of FEM calculations of switching forces acting on the considered point are qualitatively correct, so Data Mining methods efficiency was examined on data obtained from FEM multi-body model. Hidden structures in data and patterns describing particular faults were identified. Proposed algorithms of Kohonen's neural networks and k-means clustering are easy to apply to classifying. Their implementation on the Digital Signal Processor is not difficult and memory consumption is low so diagnostic module supported by implemented Data Mining methods was proposed in order to preliminary asses technical state of railway points and to assure current state monitoring and supporting maintenance activities.