Development of a Cyber-Physical System to Monitor Early Failures Detection in Vibrating Screens

Aqueveque, Pablo; Radrigan, Luciano; Morales, Anibal S.; Willenbrinck, Eduardo

doi:10.1109/access.2021.3118283

Cited by 10 publications

(5 citation statements)

References 37 publications

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“…The proposed approach, unlike other known studies, e.g., [11], considers the vibrating screen as a system with a changeable structure, which depends on bolted joints loosening. In other studies, the higher-order dynamical models including DEM and FEM simulations [19] were focused on the screen performance optimization along with a diagnostics of supporting springs [1] and bearings [34], while diagnostics of bolted joints was not covered.…”

Section: Discussionmentioning

confidence: 99%

“…The selection of the informative frequency band in a bearing fault diagnosis in the presence of non-Gaussian noise with a comparison of recently developed methods is fulfilled in [10]. Battery-powered wireless sensors are designed in [11] which are able to withstand vibratory loading and are embedded in the rubber screens for condition monitoring. Nevertheless, diagnostics of vibrating machines, in particular sieving screens, still is a challenge in theory and practice.…”

Section: Problems In Vibrating Sieving Screen Operation and Diagnosticsmentioning

confidence: 99%

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Diagnostics of Bolted Joints in Vibrating Screens Based on a Multi-Body Dynamical Model

Krot,

Shiri,

Dąbek

et al. 2023

Materials

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The condition-based maintenance of vibrating screens requires new methods of their elements’ diagnostics due to severe disturbances in measured signals from vibrators and falling pieces of material. The bolted joints of the sieving deck, when failed, require a lot of time and workforce for repair. In this research, the authors proposed the model-based diagnostic method based on modal analysis of the 2-DOF system, which accounts for the interaction of the screen body and the upper deck under conditions of bolted joint degradation. It is shown that the second natural mode with an out-of-phase motion of the upper deck against the main screen housing may coincide with the excitation frequency or its higher harmonics, which appear when vibrators’ bearings are in bad condition. This interaction speeds up bolt loosening and joint opening by the dynamical loading of higher amplitude. The proposed approach can be used to detune the system from resonance and anti-resonance to reduce maintenance costs and energy consumption. To prevent abrupt failures, such parameters as second natural mode frequency, damping factor, and phase space plot (PSP) distortion measures are proposed as bolt health indicators, and these are verified on the laboratory vibrating screen. Also, the robustness is tested by the impulsive non-Gaussian noise addition to the measurement data. A special diagram was proposed for the bolted joints’ strength capacity assessment and maintenance actions planning (tightening, replacement), depending on clearance in the joints.

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Section: Discussionmentioning

confidence: 99%

Section: Problems In Vibrating Sieving Screen Operation and Diagnosticsmentioning

confidence: 99%

Diagnostics of Bolted Joints in Vibrating Screens Based on a Multi-Body Dynamical Model

Krot,

Shiri,

Dąbek

et al. 2023

Materials

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“…Pengayakan merupakan satuan operasi pemisahan dan penyaringan dari berbagai ukuran bahan untuk dipisahkan ke dalam dua atau tiga fraksi dengan menggunakan ayakan. Setiap fraksi yang keluar dari ayakan mempunyai ukuran yang seragam [3].…”

Section: Pendahuluanunclassified

Perancangan mesin classifier dengan ukuran mikron untuk pemisahan dan penyaringan bahan baku semen

Wibawa,

Saleh,

Taufik

2024

JTMI

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Semen yang berkualitas merupakan faktor utama infrastuktur yang baik. Meningkatnya sektor infrastuktur akan kebutuhan semen meningkat secara pesat pada pada tahun 2012 yang mencapai 54 juta ton hingga tahun 2017 sebesar 84 juta ton. Adapun pada tahun 2022, konsumsi semen dalam negeri meningkat sebesar 7,6%. Berdasarkan data tersebut kebutuhan pokok terhadap semen semakin meningkat. Salah satu upaya peningkatan produksi semen yang baik dengan mengolah bahan baku sebaik mungkin, sehingga produk yang dihasilkan berupa semen yang memiliki kualitas yang tinggi. Tujuan dari kajian ini yaitu untuk merancang mesin classifier berjenis vibrating screen yang akan digunakan untuk mengolah bahan baku semen. Penggunaan mesin ini bertujuan untuk memisahkan dan menyaring ukuran partikel bahan baku yang telah diproses oleh mesin crusher, sehingga dapat memilah ukuran bahan baku sesuai dengan yang dinginkan. Mesin ini menggunakan material baja karbon 1020 dan Stainless steel 316, kemudian menganalisis kekuatan struktur pada rangka mesin. Dalam kajian ini, metode pemodelan menggunakan software SolidWorks 2019 dan metode Finite Element Analysis (FEA) mengunakan software ANSYS 2022. Persamaan matematis meliputi perhitungan elemen mesin, kapasitas aliran, beban yang diberikan. Analisis statik yang dilakukan yaitu Von mises stress, displacement, dan safety factor. Hasil analisis menunjukan bahwa nilai Von mises stress maksimum sebesar 53,865 MPa, total deformation maksimum sebesar 2,1649 mm, dan safety factor minimum sebesar 4,6804 di atas 3, sehingga desain mesin classifier yang dirancang dapat dikatakan aman.

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“…The fault diagnosis technology of vibrating screen has only experienced more than 10 years of development, the method used is relatively lagging behind, mostly for the construction of simulation models, analysis and diagnosis of a single component in the vibrating screen such as springs, bearings, and has not yet used intelligent and novel fault diagnosis methods. [3][4][5][6] In the complex working environment, the linear vibrating screen itself has a large impact amplitude and the interaction between the transmission mechanism, screen box and spring, so that the vibration signal background noise is strong, coupling is serious, the fault signal is submerged, and early failure is difficult to identify.…”

Section: Introductionmentioning

confidence: 99%

Research on fault diagnosis method of linear vibration screen based on fused RP-improved CNN

Fan,

Feng,

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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A fault diagnosis method based on fused RP (recurrence plot) and improved CNN (convolutional neural networks) is proposed for the traditional fault diagnosis method of linear vibrating screen manually designed and optimized features with feature quality uncertainty. A deep convolutional neural network combination model (MDCNN) with high-level feature fusion is designed. The collected multi-source vibration signals were converted into black and white recurrence plot, and fusion into three-channel RGB recurrence plot. Fault diagnosis was classified by the combination model of deep convolution neural network with high level feature fusion. The results show that the recurrence plot of homologous multi-sensor signals contains more information about the characteristics of the vibrating screen than the original colorful signal map or the Gram Point Field, which contributes to the feature learning and classification of MDCNN, with an average recognition accuracy of 97.59% under strong vibrating sift-5db noise. Compared with manifold classical neural network models, the MDCNN model is proved to be effective, stable and small space occupying.

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Development of a Cyber-Physical System to Monitor Early Failures Detection in Vibrating Screens

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 10 publications

References 37 publications

Diagnostics of Bolted Joints in Vibrating Screens Based on a Multi-Body Dynamical Model

Diagnostics of Bolted Joints in Vibrating Screens Based on a Multi-Body Dynamical Model

Perancangan mesin classifier dengan ukuran mikron untuk pemisahan dan penyaringan bahan baku semen

Research on fault diagnosis method of linear vibration screen based on fused RP-improved CNN

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