In this study, a smart automatic health status diagnosis and monitoring scheme for an integrated-circuit-piezoelectric (IEPE)-type accelerometer is presented. In China Steel Corporation (CSC), IEPE-type accelerometers have been widely and frequently used for machine vibration measurement. Since a valuable monitoring report always counts on the precise measurement of IEPE-type accelerometers, the health condition of the sensors must be guaranteed. However, there are now more than two thousand IEPE accelerometers attached to field machines and some of them are not easy to reach. The point-by-point diagnosis of those sensors by field workers will require a large maintenance effort and is not efficient. As a result, in the pursuit of the so-called smart factory and the enhancement of the production process as well as attenuate numerous human maintenance efforts, a graphical histogram algorithm (GHA) health condition diagnosis and monitoring strategy is proposed. By the analysis of the histogram distribution and the use of spline interpolation on the IEPE accelerometer excitation signals, characteristic profiles can be extracted. Therefore, different health conditions should be classified systematically. Finally, the status of IEPE accelerometers can be automatically identified by estimating the correlations between the characteristic profiles. Experiments have been conducted to verify the feasibility of the proposed GHA.
All Science Journal Classification (ASJC) codes
- Materials Science(all)