Iranian Journal of Mechanical Engineering Transactions of ISME

Iranian Journal of Mechanical Engineering Transactions of ISME

Condition Monitoring of Reciprocating Compressors using Probabilistic Neural Network and Optimization with Genetic Algorithm

Authors
sina salehi 1
Majid Sadedel 2
1 M.Sc., Department of Mechanical Engineering, Tarbiat Modares University, Tehran, Iran
2 Assistant Professor, Department of Mechanical Engineering, Tarbiat Modares University , Tehran, Iran
10.30506/ijmep.2023.555250.1886
Abstract
Demand for cost-effective, reliable operation and safety of machinery, especially reciprocating compressors, which are among the most expensive machines in maintenance, requires accurate troubleshooting and fault classification. Due to their advantages, data-driven methods are often preferred to physical modeling methods for fault detection. This research simulates the mathematical model of a two-stage reciprocating compressor and conventional faults for use as a monitored system. The artificial neural network used is the probabilistic neural network whose main task is classification. Classification classes include one healthy compressor class and seven defective compressor classes for eight classes. Classification with the probabilistic neural network was performed using time domain and envelope spectrum characteristics. Then, the selected features are optimized using a genetic algorithm before feeding into the probabilistic neural network. Classification with the probabilistic neural network using time-domain characteristics shows a poor classification percentage with 44% Correct accuracy. But classification with a probabilistic neural network and envelope spectrum features has a 95% correct classification accuracy. Also, optimizing the selection of statistical features of the time-domain and frequency envelope spectrum with a genetic algorithm brings 48 and 99% correct accuracy in classification, respectively.
Keywords
Reciprocating compressors
Condition monitoring
Probabilistic neural networks
Genetic algorithm

Subjects

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  • Receive Date 06 June 2022
  • Revise Date 28 April 2023
  • Accept Date 09 October 2023