Highly Reliable Multicomponent MEMS Sensor for Predictive Maintenance Management of Rolling Bearings

Author:

Landi Elia1ORCID,Prato Andrea2ORCID,Fort Ada1ORCID,Mugnaini Marco1,Vignoli Valerio1ORCID,Facello Alessio2ORCID,Mazzoleni Fabrizio2,Murgia Michele3,Schiavi Alessandro2ORCID

Affiliation:

1. Department of Information Engineering and Mathematical Sciences, University of Siena, 53100 Siena, Italy

2. Division of Applied Metrology and Engineering INRiM, National Institute of Metrological Research, 10135 Turin, Italy

3. Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy

Abstract

In the field of vibration monitoring and control, the use of low-cost multicomponent MEMS-based accelerometer sensors is nowadays increasingly widespread. Such sensors allow implementing lightweight monitoring systems with low management costs, low power consumption and a small size. However, for the monitoring systems to provide trustworthy and meaningful data, the high accuracy and reliability of sensors are essential requirements. Consequently, a metrological approach to the calibration of multi-component accelerometer sensors, including appropriate uncertainty evaluations, are necessary to guarantee traceability and reliability in the frequency domain of data provided, which nowadays is not fully available. In addition, recently developed metrological characterizations at the microscale level allow to provide detailed and accurate quantification of the enhanced technical performance and the responsiveness of these sensors. In this paper, a dynamic calibration procedure is applied to provide the sensitivity parameters of a low-cost, multicomponent MEMS sensor accelerometer prototype (MDUT), designed, developed and realized at the University of Siena, conceived for rolling bearings vibration monitoring in a broad frequency domain (from 10 Hz up to 25 kHz). The calibration and the metrological characterization of the MDUT are carried out by comparison to a reference standard transducer, at the Primary Vibration Laboratory of the National Institute of Metrological Research (INRiM).

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Control and Systems Engineering

Reference56 articles.

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1. Structural health monitoring for 3D-printed civil infrastructures: a review of challenges, applications and future directions;Smart Materials and Structures;2024-09-12

2. Evaluation of the Diagnostic Sensitivity of Digital Vibration Sensors Based on Capacitive MEMS Accelerometers;Sensors;2024-07-10

3. Prediction of Remaining Useful Life of Mechanical Equipment : A Review;2023 International Conference on New Trends in Computational Intelligence (NTCI);2023-11-03

4. Bearing Failure Classification with Low Complexity Neural Network;2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE);2023-10-25

5. A Low Complexity Rolling Bearing Diagnosis Technique Based on Machine Learning and Smart Preprocessing;Sensors;2023-08-30

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