An Overview of Reliability and Failure Mode Analysis of Microelectromechanical Systems (MEMS)

Li, Yuanbo; Jiang, Zhibin

doi:10.1007/978-1-84800-131-2_58

Cited by 25 publications

(11 citation statements)

References 98 publications

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“…A complete loss of signal can also be represented by this type of fault by assuming that the output from the sensor is zero (i.e., δ = 0). There has been considerable research into specific failure mechanisms of individual types of MEMS sensors [10][11][12][13][14][15][16]. However information regarding operational failure modes in MEMS and how the mechanisms described in the literature link to the functional failures (bias, drift, scaling and hard failure) is lacking [16].…”

Section: Scope Of the Management Systemmentioning

confidence: 99%

Framework for Validation of Permanently Installed MEMS-Based Acquisition Devices Using Soft Sensor Models

et al. 2020

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Asset integrity and predictive maintenance models require field data for an accurate assessment of an asset’s condition. Historically these data collected periodically in the field by technicians using portable units. The significant investment in inexpensive microelectromechanical (MEMS) sensors mounted on untethered (energy-harvesting or battery-powered) microprocessors communicating wirelessly to the cloud is expected to change the way we collect asset health data. Permanently installed MEMS-based sensing units will enable near-real time data collection and reduce the safety exposure of technicians by eliminating the need to manually collect field data. With hundreds of MEMS-based sensing units expected to be installed at a single site it is vital to assure the data they produce and maintain them cost effectively. An asset management framework for validation of MEMS-based sensing units for condition monitoring and structural integrity (CM&SI) applications is proposed. An integral part of this framework is the proposed use of soft sensor models to replace technician inspections in the field. Soft sensor models are used in the process industry to stabilize product quality and process operations but there are few examples in asset management applications. The contributions of this paper are twofold. Firstly, we use an interdisciplinary approach drawing on electronics, process control, statistics, machine learning, and asset management fields to describe the emerging field of permanently installed MEMS-based sensing units for CM&SI. Secondly, we development a framework for assuring validation of the data these sensing units generate.

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Section: Scope Of the Management Systemmentioning

confidence: 99%

Framework for Validation of Permanently Installed MEMS-Based Acquisition Devices Using Soft Sensor Models

et al. 2020

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“…These parts are subject to degradations and failure mechanisms due to several factors (temperature, humidity, vibration, moisture, noise, electrical overloads, etc.). Common failure mechanisms identified and known until now concern stiction, wear, fracture, crystallographic defect, creep and degradation of dielectrics [6,7,8,9]. The reliability of these microsystems is then considered as a major obstacle in their development [10].…”

Section: State Of the Artmentioning

confidence: 99%

“…Always in the same category, a review of MEMS reliability issues and failure mechanisms can be found in [13]. The chapter published in [9] reported known and unknown failure modes of…”

Section: Reliability Reviewsmentioning

confidence: 99%

Condition assessment and fault prognostics of microelectromechanical systems

Medjaher

Skima

Zerhouni

2014

Microelectronics Reliability

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Microelectromechanical systems (MEMS) are used in different applications such as automotive, biomedical, aerospace and communication technologies. They create new functionalities and contribute to miniaturize the systems and reduce their costs. However, the reliability of MEMS is one of their major concerns. They suffer from different failure mechanisms which impact their performance, reduce their lifetime and their availability. It is then necessary to monitor their behavior and assess their health state to take appropriate decision such as control reconfiguration and maintenance. These tasks can be done by using Prognostic and Health Management (PHM) approaches. This paper addresses a condition assessment and fault prognostic method for MEMS. The paper starts with a short review about MEMS and presents some challenges identified and which need to be raised to implement PHM methods. The purpose is to highlight the intrinsic constraints of MEMS from PHM point of view. The proposed method is based on a global model combining both nominal behavior model and degradation model to assess the health state of MEMS and predict their remaining useful life. The method is applied on a microgripper, with different degradation models, to show its effectiveness.

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“…The difficulty in ensuring proper MEMS behavior and maintenance of their functions in routine circumstances emanates from the limited information about the microscopic failure mechanisms (low cycle fatigue, stress corrosion, adhesion, etc) [2]. From an engineering point of view, the typical failure modes are treated as system parameters variations and faults causing abnormal systems performance.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Diagnosis enhanced by a reconfiguration control scheme for a micro-electrostatic actuator

Reppa

Vagia

Tzes

2010

18th Mediterranean Conference on Control and Automation, MED'10

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In this article, a Fault Detection and Diagnosis (FDD) strategy based on Set Membership Identification is proposed for the supervision of an electrostatic microactuator subject to failure modes in its structural components. Relying on the a priori knowledge of the bound of the noise corrupting the measurement data, the parametric uncertainty is assumed to be bounded and the SMI computes the parametric sets (ellipsoids, support-orthotopes), within which the nominal parameter vector resides. A fault is detected at the time instant that an empty parametric set is obtained. Prior to the fault isolation and identification procedure, a control reconfiguration scheme is used for prevention of catastrophic damages in the system, due to large parameter variations. Simulations studies are used to verify the efficiency of the suggested strategy.

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An Overview of Reliability and Failure Mode Analysis of Microelectromechanical Systems (MEMS)

Cited by 25 publications

References 98 publications

Framework for Validation of Permanently Installed MEMS-Based Acquisition Devices Using Soft Sensor Models

Framework for Validation of Permanently Installed MEMS-Based Acquisition Devices Using Soft Sensor Models

Condition assessment and fault prognostics of microelectromechanical systems

Fault Detection and Diagnosis enhanced by a reconfiguration control scheme for a micro-electrostatic actuator

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