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Diagnostic and severity analysis of combined failures composed by imbalance and misalignment in rotating machines

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Abstract

Failure detection from mechanical vibration analysis is crucial in industry machinery, with early discovery allowing for preventive action to be performed. This paper introduces a prototype of an IoT system capable of (i) identifying combined failures of a rotating machine and (ii) predicting failures, in a non-invasive manner. An embedded solution is devised, which is able to classify four types of operating conditions, namely (i) normal, (ii) imbalanced, (iii) imbalanced associated with horizontal misalignment, and (iv) imbalanced associated with vertical misalignment. The goal of the paper is to propose an automatic method of diagnosis and measurement of combined failures in rotating machines. The employed methodology combines a simulation bench and measuring the severity in a controlled environment. Three distinct machine learning techniques were compared for classification purposes: support vector machines, k-nearest neighbors, and random forests. The results obtained reveal the possibility of differentiating between the types of combined faults; an accuracy of 81.41% using a random forest classifier was achieved. A supervisory system was developed which is responsible for monitoring machines and sending wireless alert messages. The latter are sent to a control application, allowing for user interaction through mobile devices. Results reveal the possibility of differentiating between the types of combined faults, and also motor failure severity profile for different scenarios. Through the construction of severity profiles, when faults occurred, high vibration values were registered at elevated speeds. The proposed methodology can be used in any rotating machine that complies with the conditions imposed by ISO 10816.

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Funding

This research was supported in part by Brazilian Federal Agencies: CEFET-RJ, CAPES, CNPq, and FAPERJ.

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Authors and Affiliations

  1. Federal Center for Technological Education of Rio de Janeiro, Rio de Janeiro, Brazil

    Dionísio Henrique Carvalho de Sá Só Martins, Denys Pestana Viana, Amaro Azevedo de Lima, Milena Faria Pinto, Luís Tarrataca, Fabrício Lopes e Silva, Thiago de Moura Prego & Diego Barreto Haddad

  2. Federal University of Rio de Janeiro, Brazil, Rio de Janeiro, Brazil

    Ricardo Homero Ramírez Gutiérrez & Ulisses Admar Barbosa Vicente Monteiro

Authors
  1. Dionísio Henrique Carvalho de Sá Só Martins
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  2. Denys Pestana Viana
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  3. Amaro Azevedo de Lima
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  4. Milena Faria Pinto
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  5. Luís Tarrataca
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  6. Fabrício Lopes e Silva
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  8. Thiago de Moura Prego
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  9. Ulisses Admar Barbosa Vicente Monteiro
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Correspondence to Dionísio Henrique Carvalho de Sá Só Martins.

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Author contribution

Experimentation: Dionísio Martins, Denys Viana, Ricardo Gutiérrez, and Ulisses Monteiro. Original draft writing: Dionísio Martins, Milena Pinto, and Luís Tarrataca. Review and editing: Amaro Lima, Thiago Prego, Fabrício Lopes e Silva, and Diego Haddad.

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The dataset generated in this paper is available from the corresponding author on reasonable request.

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The custom software code generated during the current study is not publicly available due to confidentiality policy.

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de Sá Só Martins, D.H.C., Viana, D.P., de Lima, A.A. et al. Diagnostic and severity analysis of combined failures composed by imbalance and misalignment in rotating machines. Int J Adv Manuf Technol 114, 3077–3092 (2021). https://doi.org/10.1007/s00170-021-06873-2

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