Failure Rate Analysis of Jaw Crusher Using Artificial Neural Network

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Authors

  • R. S. Sinha
     Department of Mining Machinery Engineering, Indian School of Mines, Dhanbad ,IN
  • A. K. Mukhopadhyay
     Department of Mining Machinery Engineering, Indian School of Mines, Dhanbad ,IN

Keywords:

Jaw Crusher, Weibull Distribution, ANNs and Failure Rate.

Abstract

Crusher is the primary equipment which is employed for comminuting the mineral in processing plants. Hence, any kind of failure of equipment will accordingly hinder the performance of the plant. Therefore, to minimize sudden failures, proper brainstorming needs to be done to improve performance and operational reliability of jaw crushers. This paper considers the methods for analysing failure rates of jaw crusher through 2-parameter Weibull distribution using ANN (Artificial Neural Network). 40 numbers of Weibull distribution parameters are evaluated to examine R2 (Regression coefficient) using ANN. ANN multilayer perceptron model constructed with back-propagation algorithm using shape, scale and time parameters as input and failure rate as an output from Weibull distribution.

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Published

2022-10-17

How to Cite

Sinha, R. S., & Mukhopadhyay, A. K. (2022). Failure Rate Analysis of Jaw Crusher Using Artificial Neural Network. Journal of Mines, Metals and Fuels, 64(5), 143–145. Retrieved from https://informaticsjournals.com/index.php/jmmf/article/view/31490

Issue

Volume 64, Issue 5, May 2016

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

 

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