IEPE accelerometer fault diagnosis for maintenance management system information integration in a heavy industry

Abstract

Abstract With the increasing demand for reliable production facilities, the design of a health condition monitoring system with the implementation of automatic diagnosis as well as software solutions is one of the main issues for a smart factory. Among many industrial applications, accelerometer is one of the most frequently used sensors for facility vibration monitoring. Thus, the health condition of the sensor itself is a critical factor for a correct diagnosis. Failure to monitor the sensor's health condition would potentially cause a false alarm, which may lead to a wrong decision making made by field operators. In this research, a preprocessing method of synthetic data and a Gaussian mixture model (GMM) classifier were developed to classify the health conditions of the online integrated electronic piezoelectric (IEPE) accelerometers. The proposed method was integrated into a product line and the test results achieved >99% of accuracy in determining five different health conditions of the accelerometers. With the aid of the proposed method, the time of human inspection can be significantly reduced and the field safety can also be improved. Moreover, false alarms caused by sensor failure can be prevented. This leads to increase in reliability of the facility monitoring system. 3 features to represent > 99% 3 features to represent > 99%