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%

随着对可靠生产设备的需求不断增加，设计具有自动诊断功能和软件解决方案的健康状况监控系统是智能工厂的主要问题之一。在许多工业应用中，加速度计是用于设备振动监测的最常用的传感器之一。因此，传感器本身的健康状况是正确诊断的关键因素。未能监控传感器的健康状况可能会导致错误警报，这可能导致现场操作员做出错误的决定。在这项研究中，开发了一种合成数据的预处理方法和一个高斯混合模型（GMM）分类器，以对在线集成电子压电（IEPE）加速度计的健康状况进行分类。所提出的方法已集成到产品线中，在确定加速度计的五种不同健康状况时，测试结果达到了> 99％的准确度。借助于所提出的方法，可以大大减少人工检查的时间，并且还可以改善现场安全性。而且，可以防止由传感器故障引起的误报。这导致设施监视系统的可靠性增加。

3个特点

代表> 99％

3个特点

代表> 99％