Research on mechanical vibration monitoring based on wireless sensor network and sparse Bayes

Lei, Xinjun; Wu, Yiping

doi:10.1186/s13638-020-01836-9

Cited by 13 publications

(4 citation statements)

References 31 publications

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“…Figure 1 illustrates the network node’s components, including the piezoelectric transducer (PET), a vibration energy collection and management system, and the WVS node 14 . The PET operates based on the piezoelectric effect, generating alternating energy.…”

Section: Theoretical Basis and Research Methodsmentioning

confidence: 99%

Applying self-powered sensor and support vector machine in load energy consumption modeling and prediction of relational database

Yang,

Yu,

et al. 2023

Sci Rep

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This study explores the analysis and modeling of energy consumption in the context of database workloads, aiming to develop an eco-friendly database management system (DBMS). It leverages vibration energy harvesting systems with self-sustaining wireless vibration sensors (WVSs) in combination with the least square support vector machine algorithm to establish an energy consumption model (ECM) for relational database workloads. Through experiments, the performance of self-sustaining WVS in providing power is validated, and the accuracy of the proposed ECM during the execution of Structured Query Language (SQL) statements is evaluated. The findings demonstrate that this approach can reliably predict the energy consumption of database workloads, with a maximum prediction error rate of 10% during SQL statement execution. Furthermore, the ECM developed for relational databases closely approximates actual energy consumption for query operations, with errors ranging from 1 to 4%. In most cases, the predictions are conservative, falling below the actual values. This finding underscores the high predictive accuracy of the ECM in anticipating relational database workloads and their associated energy consumption. Additionally, this paper delves into prediction accuracy under different types of operations and reveals that ECM excels in single-block read operations, outperforming multi-block read operations. ECM exhibits substantial accuracy in predicting energy consumption for SQL statements in sequential and random read modes, especially in specialized database management system environments, where the error rate for the sequential read model is lower. In comparison to alternative models, the proposed ECM offers superior precision. Furthermore, a noticeable correlation between model error and the volume of data processed by SQL statements is observed. In summary, the relational database ECM introduced in this paper provides accurate predictions of workload and database energy consumption, offering a theoretical foundation and practical guidance for the development of eco-friendly DBMS.

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Section: Theoretical Basis and Research Methodsmentioning

confidence: 99%

Applying self-powered sensor and support vector machine in load energy consumption modeling and prediction of relational database

Yang,

Yu,

et al. 2023

Sci Rep

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“…In this paper, author describes that the safety and reliability of the equipment is improved by the mechanical vibration monitoring. Different problems like high-frequency signal pickup collection are faced by the existing wired monitoring technology [19]. A high-precision MEMS (Micro Electro Mechanical System) acceleration sensor is used by the monitoring node with a 16-bit resolution ADC (Analog to Digital Converter) acquisition chip for high-precision acquisition of vibration signals achievement.…”

Section: Literature Surveymentioning

confidence: 99%

Building vibration monitoring based on digital optical cameras

Wang¹,

Shao²

2021

J. vibroeng.

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The complex and process-critical conditions of machineries are needed to be continuously monitored in order for incipient breakdown to detect and ensure its high performance and operating safety. The change in the resonant frequency is focused by the spectrum analysis which is caused by the structural degradation and for health monitoring, useful information is produced. A test signal is required by the spectral analysis for health monitoring of structural condition. A test signal is the easiest way for structural spectral characteristics measurement but during every measurement, a structure vibration is impractical. Several techniques are presented for the condition of a machine depending on the application monitoring. The structure health would be able to be monitored if structural vibrations are utilized for spectral characteristics measurement due to wind and ground motion. The vision-based monitoring with the sources of errors are detailed and presented in this paper. Different parameters are observed for the different signals, unify the scale and can also be used to signals errors as compared with different levels of amplitudes. The PSNR and Structural Similarity are analyzed, the PSNR value is obtained in the range of 40 dB to 50 dB and the SSIM values are nearby to the 1 for all the signals. The MAPE and MAE values obtained by the presented technique are less for all the signal samples. The presented method outperforms the existed technique by 3.21 % to 44.02 % in form of MAPE. The presented method is 3.21 % to 44.02 % better than the previously existed technique in form of MAPE. The percentage improvement of the presented method outperforms by 33.93 % and 42.43 % in terms of MAE.

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“…There are currently studies and applications for failure detection using a range of aspects such as Visual, Acoustic, Electrical and Thermal Analysis (Hemmati et al, 2015) (Weijtjens et al, 2017). In modern high speed bearing failure diagnosis, methods based on vibration signals are widely used and continuous online monitoring of rotating machines is necessary to assess real-time health conditions reducing the possibility of downtime (Kwon et al, 2016) (Chen et al, 2016) (Ince et al, 2016) (Lei & Wu, 2020) (Janssens et al, 2016). Research, Society and Development, v. 11, n. 1, e14211125104, 2022 (CC BY 4.0) | ISSN 2525-3409 | DOI: http://dx.doi.org/10.33448/rsd-v11i1.25104 3 A bearing vibration monitoring system must be accurate when detecting the equipment operation state.…”

Section: Introductionmentioning

confidence: 99%

Paraconsistent annotated logic applied to industry assets condition monitoring and failure prevention based on vibration signatures

Correa

Machado

Filho

et al. 2022

RSD

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In this study, we introduced an expert system (ESvbrPAL2v), responsible for monitoring assets based on vibration signature analysis through a set of algorithms based on the Paraconsistent Annotated Logic – PAL. Being a non-classical logic, the main feature of the PAL is to support contradictory inputs in its foundation. It is therefore suitable for building algorithmic models capable of performing out appropriate treatment for complex signals, such as those coming from vibration. The ESvbrPAL2v was built on an ATMega2560 microcontroller, where vibration signals were captured from the mechanical structures of the machines by sensors and, after receiving special treatment through the Discrete Fourier Transform (DFT), then properly modeled to paraconsistent logic signals and vibration patterns. Using the PAL fundamentals, vibration signature patterns were built for possible and known vibration issues stored in ESvbrPAL2v and continuously compared through configurations composed by a network of paraconsistent algorithms that detects anomalies and generate signals that will report on the current risk status of the machine in real time. The tests to confirm the efficiency of ESvbrPAL2v were performed in analyses initially carried out on small prototypes and, after the initial adjustments, tests were carried out on bearings of a group of medium-power motor generators built specifically for this study. The results are shown at the end of this study and have a high index of signature identification and risk of failure detection. These results justifies the method used and future applications considering that ESvbrPAL2v is still in its first version.

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Research on mechanical vibration monitoring based on wireless sensor network and sparse Bayes

Cited by 13 publications

References 31 publications

Applying self-powered sensor and support vector machine in load energy consumption modeling and prediction of relational database

Applying self-powered sensor and support vector machine in load energy consumption modeling and prediction of relational database

Building vibration monitoring based on digital optical cameras

Paraconsistent annotated logic applied to industry assets condition monitoring and failure prevention based on vibration signatures

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