Experimental monitoring data for prognostics and health management of MEMS

Abstract

This paper presents the data acquisition step of a Prognostics and Health Management (PHM) of Micro-Electro-Mechanical Systems (MEMS) application. The targeted MEMS device is an electro-thermally actuated MEMS valve. The data acquisition is performed during the accelerated lifetime tests. To perform tests, an experimental test bed is designed and built. Several test campaigns are performed where MEMS valves operated continuously and data acquired regularly. The obtained experimental results show that MEMS fabricated with the same micro-fabrication process and tested in the same conditions do not have the same behavior and the same evolution of degradation in time. Therefore, this supports the importance of applying PHM of MEMS rather than the predictive reliability.

A method to estimate the remaining useful lifetime of a two-jaw parallel gripper based on experimental failure threshold data

“…The hybrid method is based on the experimental-based and data-driven approach. Skima et al [8] have developed a hybrid method in which grippers at microelectromechanical (MEMS) system-level have been investigated for degradation. This hybrid method mathematically calculates the nominal behavior of the gripper and derives the degradation from it.…”

“…According to Skima et al [8], the physical-model-based approach deals with the estimation of the RUL by using mathematical or physical models to describe the physics of the component and the degradation phenomena. The physical-model based approach builds upon a detailed understanding of the gripper's physics [2].…”

On Sensor Data Clustering for Machine Status Monitoring and Its Application to Predictive Maintenance

“…Predictive maintenance is a methodology that aims at predicting the deterioration of the health conditions of an industrial machine, typically associated with anomalies of its components. Predicting accurately impending failures can be very difficult: it is essential to have a deep knowledge of the specific system to derive a precise prediction model [8]. Nevertheless, it may happen that the collected data provide inadequate information to accurately determine the degradation status of a particular component.…”