A Housekeeping Prognostic Health Management Framework for Microfluidic Systems

Abstract

Abstract--Micro-electro-mechanical Systems and microfluidics are becoming popular solutions for sensing, diagnostics and control applications. Reliability and validation of function is of increasing importance in the majority of these applications. Online testing strategies for these devices have the potential to provide real-time condition monitoring information. It is shown that this information can be used to diagnose and prognose the health of the device. This information can also be used to provide an early failure warning system by predicting the remaining useful life. Diagnostic and prognostic outcomes can also be leveraged to improve the reliability, dependability and availability of these devices. This work has delivered a methodology for a "lightweight" prognostics solution for a microfluidic device based on real-time diagnostics. An oscillation based test methodology is used to extract diagnostic information that is processed using a Linear Discriminant Analysis based classifier. This enables the identification of current health based on pre-defined health levels. As the deteriorating device is periodically classified, the rate at which the device degrades is used to predict the devices remaining useful life. I. INTRODUCTIONA novel implementation of a prognostics function for a microfluidic device based on MEMS technology is presented. The term "prognostics" has been defined in various different ways depending on context [1], this research considers the definition given by ISO13381-1 as the most comprehensive. It states that prognostics is not only the prediction of the time to failure but also the risk of one or more existing or future failure modes [2].Studies have revealed some excellent Prognostic and Health Management (PHM) solutions that provide deep and detailed prognostic capabilities for micro-devices with a high level of accuracy. The use of machine learning [3] and physics based prognostics [4] is being actively explored. PHM solutions similar to these are designed from the ground up in a device specific framework with the prognostics modules being tightly coupled with the physics of failure of the device. While these PHM systems perform very well in conjunction with their target devices, flexibility and wide scale adoption in other MEMS devices is quite challenging.The onus of most PHM frameworks is to provide prognosis at the highest possible confidence level thus providing the most reliable Remaining Useful Life (RUL) predictions [5] [6] [7]. However, this comes at a cost, be it a heavy processing requirement or the aforementioned rigidity or complexity, to name two. This work proposes a "Housekeeping" Prognostics and Health Management (HPHM) framework that has a conservative prognostic mandate. Such a framework caters strictly for wear during normal use and evolving faults. This affords the usage of simpler models that are easy to adapt, implement and use.The scope of this HPHM is to predict patterns of low intensity failures and drift due to gradual aging and noncatastrophic faults du...

Self-Monitoring, Self-Healing Biomorphic Sensor Technology

“…"Probe and Predict" technology utilises reliability indicators that are normally electrical parameters such as a voltage, current or frequency, sensitive to material or geometrical degradation together with prognostic algorithms. An example here is the use of self-oscillation [7] to drive prognostics associated with electrode degradation [8]. Here Linear Discriminant Analysis has been deployed through a low-power algorithm to provide a prognostic capability compatible with the limitations of edge based computing.…”

Self-diagnostic approach for cell counting biosensor

“…In this technique, dependability has been adopted in the microsystem and its medical applications where the sources of the malfunction are still unknown while the whole interconnect subsystem of the biochips has been tested and different procedures for characterising the mechanical and fluidic machinery have been proposed [12]- [13]. The potential of using oscillation based test (OBT) has been investigated, where this technique was utilized to generate indication about the status of the electrodes in multi electrode array (MEA) [14]- [16]. This technique produces an oscillation frequency that is dependent on the fault level on the surface of the sensing electrode.…”