Neural network applications in fault diagnosis and detection: an overview of implementations in engineering-related systems


The use of artificial neural networks (ANN) in fault detection analysis is widespread. This paper aims to provide an overview on its application in the field of fault identification and diagnosis (FID), as well as the guiding elements behind their successful implementations in engineering-related applications. In most of the reviewed studies, the ANN architecture of choice for FID problem-solving is the multilayer perceptron (MLP). This is likely due to its simplicity, flexibility, and established usage. Its use managed to find footing in a variety of fields in engineering very early on, even before the technology was as polished as it is today. Recurrent neural networks, while having overall stronger potential for solving dynamic problems, are only suggested for use after a simpler implementation in MLP was attempted. Across various ANN applications in FID, it is observed that preprocessing of the inputs is extremely important in obtaining the proper features for use in training the network, particularly when signal analysis is involved. Normalization is practically a standard for ANN use, and likely many other decision-based learning methods due to its ease of use and high impact on speed of convergence. A simple demonstration of ANN’s ease of use in solving a unique FID problem was also shown.

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The authors would like to thank MOSTI Grant Science Fund 0153AB-B67 for the funding provided for this work. The authors would also like to thank Universiti Teknologi PETRONAS (UTP) for the support provided for this research.

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Correspondence to Haslinda Zabiri.

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Mohd Amiruddin, A.A.A., Zabiri, H., Taqvi, S.A.A. et al. Neural network applications in fault diagnosis and detection: an overview of implementations in engineering-related systems. Neural Comput & Applic 32, 447–472 (2020).

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  • Artificial neural network
  • Fault detection
  • Fault diagnosis
  • Engineering application
  • Data preprocessing