CAPACITANCE MEMS ACCELEROMETERS FOR MEASURING HIGH-g ACCELERATIONS

2017 ◽  
Keyword(s):  
Mems Accelerometers

A validated robust and automatic procedure for vibration analysis of bridge structures using MEMS accelerometers

2020 ◽  
Vol 14 (3) ◽  
pp. 327-354
Author(s):  
Mohammad Omidalizarandi ◽  
Ralf Herrmann ◽  
Boris Kargoll ◽  
Steffen Marx ◽  
Jens-André Paffenholz ◽  
...  
Keyword(s):  
Vibration Analysis ◽  
Damping Ratio ◽  
Fem Analysis ◽  
Cost Effective ◽  
Analysis Procedure ◽  
Modal Parameters ◽  
Bridge Structure ◽  
Modal Damping ◽  
Mems Accelerometers ◽  
Better Than

AbstractToday, short- and long-term structural health monitoring (SHM) of bridge infrastructures and their safe, reliable and cost-effective maintenance has received considerable attention. From a surveying or civil engineer’s point of view, vibration-based SHM can be conducted by inspecting the changes in the global dynamic behaviour of a structure, such as natural frequencies (i. e. eigenfrequencies), mode shapes (i. e. eigenforms) and modal damping, which are known as modal parameters. This research work aims to propose a robust and automatic vibration analysis procedure that is so-called robust time domain modal parameter identification (RT-MPI) technique. It is novel in the sense of automatic and reliable identification of initial eigenfrequencies even closely spaced ones as well as robustly and accurately estimating the modal parameters of a bridge structure using low numbers of cost-effective micro-electro-mechanical systems (MEMS) accelerometers. To estimate amplitude, frequency, phase shift and damping ratio coefficients, an observation model consisting of: (1) a damped harmonic oscillation model, (2) an autoregressive model of coloured measurement noise and (3) a stochastic model in the form of the heavy-tailed family of scaled t-distributions is employed and jointly adjusted by means of a generalised expectation maximisation algorithm. Multiple MEMS as part of a geo-sensor network were mounted at different positions of a bridge structure which is precalculated by means of a finite element model (FEM) analysis. At the end, the estimated eigenfrequencies and eigenforms are compared and validated by the estimated parameters obtained from acceleration measurements of high-end accelerometers of type PCB ICP quartz, velocity measurements from a geophone and the FEM analysis. Additionally, the estimated eigenfrequencies and modal damping are compared with a well-known covariance driven stochastic subspace identification approach, which reveals the superiority of our proposed approach. We performed an experiment in two case studies with simulated data and real applications of a footbridge structure and a synthetic bridge. The results show that MEMS accelerometers are suitable for detecting all occurring eigenfrequencies depending on a sampling frequency specified. Moreover, the vibration analysis procedure demonstrates that amplitudes can be estimated in submillimetre range accuracy, frequencies with an accuracy better than 0.1 Hz and damping ratio coefficients with an accuracy better than 0.1 and 0.2 % for modal and system damping, respectively.

scholarly journals SOUNDNESS EVALUATION OF BRIDGE BEARING BASED ON TWO PLACES DISPLACEMENT MEASUREMENT USING MEMS ACCELEROMETERS

2017 ◽  
Vol 73 (2) ◽  
pp. I_649-I_660
Author(s):  
Hidehiko SEKIYA ◽  
Takeshi KINOMOTO ◽  
Masayuki Tai ◽  
Yusuke Koto ◽  
Osamu Maruyama ◽  
...  
Keyword(s):  
Displacement Measurement ◽  
Mems Accelerometers ◽  
Bridge Bearing

Failure Modes Model of MEMS Accelerometers Based on Neural Networks

2008 ◽  
Author(s):  
Yanping Bai ◽  
Ping An ◽  
Yilong Hao
Keyword(s):  
Neural Networks ◽  
Failure Modes ◽  
Late Phase ◽  
Primary Sources ◽  
Bp Network ◽  
Bp Networks ◽  
Mems Accelerometers ◽  
Perception Model ◽  
Hidden Layer ◽  
Training Success

Fabrication of a MEMS system involves design, testing, packaging and reliability related issues. However, reliability issues that are discovered at a late phase may cause major delays in the product development going together with high costs. In this paper we study the failure modes and Mechanisms of MEMS accelerometers products and present the classification modeling of failure modes based on neural networks. In ours MEMS accelerometers, there are six failure mechanisms that have been found to be the primary sources of failure nodes. We introduce nonlinear BP network with a hidden layer and linear perception to classify for MEMS accelerometers products. Classification results show that nonlinear BP network seem to be most appropriate to approach the problem of failure modes classification than linear perception. BP neural network is capable of learning the intrinsic relations of the patterns with which they were trained. For all experiments results, the training success of rate is 100% for both methods. BP networks obtained a high forecast success of rate of over 99.5%. The linear perception model obtained a success of rate of over 95.5%. We also analyze the technology stability of MEMS products.

scholarly journals Prototyping and Validation of MEMS Accelerometers for Structural Health Monitoring—The Case Study of the Pietratagliata Cable-Stayed Bridge

2018 ◽  
Vol 7 (3) ◽  
pp. 30 ◽  
Author(s):  
Chiara Bedon ◽  
Enrico Bergamo ◽  
Matteo Izzi ◽  
Salvatore Noè
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Full Scale ◽  
Shaking Table ◽  
Efficient Design ◽  
Dynamic Identification ◽  
Structural Health ◽  
Cable Stayed Bridge ◽  
Mems Accelerometers

In recent years, thanks to the simple and yet efficient design, Micro Electro-Mechanical Systems (MEMS) accelerometers have proven to offer a suitable solution for Structural Health Monitoring (SHM) in civil engineering applications. Such devices are typically characterised by high portability and durability, as well as limited cost, hence resulting in ideal tools for applications in buildings and infrastructure. In this paper, original self-made MEMS sensor prototypes are presented and validated on the basis of preliminary laboratory tests (shaking table experiments and noise level measurements). Based on the well promising preliminary outcomes, their possible application for the dynamic identification of existing, full-scale structural assemblies is then discussed, giving evidence of their potential via comparative calculations towards past literature results, inclusive of both on-site, Experimental Modal Analysis (EMA) and Finite Element Analytical estimations (FEA). The full-scale experimental validation of MEMS accelerometers, in particular, is performed using, as a case study, the cable-stayed bridge in Pietratagliata (Italy). Dynamic results summarised in the paper demonstrate the high capability of MEMS accelerometers, with evidence of rather stable and reliable predictions, and suggest their feasibility and potential for SHM purposes.

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