Fault Diagnosis of an Automobile Cylinder Block with Neural Process of Modal Information

Morteza Mohammadzaheri, Amirhosein Amouzadeh, Mojtaba Doustmohammadi, Mohammadreza Emadi, Ehsan Jamshidi, Mojataba Ghodsi, Payam Soltani, Navid Nasiri

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The focus of this article is fault-diagnosis of
complex mechanical parts through the process of their modal
information using a multi-layer perceptron (MLP), a type of
artificial neural networks (ANNs). The major contribution of this work is to formulate the problem of fault diagnosis of complex mechanical parts based on their modal information so as to be solved with use of ANNs. This method consists of three major steps: (1) Extracting natural frequencies of the part with or without faults. (2) Creating the “fault signatures” by deducting the natural frequencies of some faulty specimens from the ones of the faultless part. (3) Constructing and training a mathematical model in the form of an ANN, with information obtained in previous steps, to locate (and even further characterize) the fault. The presented method was successfully adopted to estimate the location of an undersurface mechanical fault on an automobile cylinder block and is shown to have the potential to solve more sophisticated fault
diagnosis problems.
Original languageEnglish
Article number 210102-4848-IJMME-IJENS
Pages (from-to)1-8
Number of pages8
JournalInternational Journal of Mechanical and Mechatronics Engineering
Issue number2
Publication statusPublished - 2021


  • Vibrations
  • Fault Diagnosis
  • Artificial Neural Networks
  • Natural Frequencies
  • Cylinder Block

ASJC Scopus subject areas

  • Engineering(all)


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