Evidence map›Paper›PMID 36838075›Full record

ArticleMicromachines2023

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

Elia Landi, Andrea Prato, Ada Fort, Marco Mugnaini, Valerio Vignoli, Alessio Facello, Fabrizio Mazzoleni, Michele Murgia, Alessandro Schiavi

Abstract read
In one paragraph

Article in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Elia LandiDepartment of Information Engineering and Mathematical Sciences, University of Siena, 53100 Siena, Italy.ORCID 0000-0003-2797-481X
Andrea PratoDivision of Applied Metrology and Engineering INRiM, National Institute of Metrological Research, 10135 Turin, Italy.ORCID 0000-0003-3733-4189
Ada FortDepartment of Information Engineering and Mathematical Sciences, University of Siena, 53100 Siena, Italy.ORCID 0000-0003-0916-1516
Marco MugnainiDepartment of Information Engineering and Mathematical Sciences, University of Siena, 53100 Siena, Italy.
Valerio VignoliDepartment of Information Engineering and Mathematical Sciences, University of Siena, 53100 Siena, Italy.ORCID 0000-0003-2509-6566
Alessio FacelloDivision of Applied Metrology and Engineering INRiM, National Institute of Metrological Research, 10135 Turin, Italy.ORCID 0000-0001-5840-9296
Fabrizio MazzoleniDivision of Applied Metrology and Engineering INRiM, National Institute of Metrological Research, 10135 Turin, Italy.
Michele MurgiaDepartment of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy.
Alessandro SchiaviDivision of Applied Metrology and Engineering INRiM, National Institute of Metrological Research, 10135 Turin, Italy.ORCID 0000-0003-4168-3605

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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).

Indexed as

broad-frequency bandIoT condition monitoringlow-costmulticomponent MEMS sensortraceability

Identifiers

PMID36838075
PMCPMC9962216

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.