Applications of generative models with a latent observation subspace in vibrodiagnostics
More details
Hide details
Casimir Pulaski Radom University, Mechanical Faculty, Stasieckiego 54, 26-600 Radom, Poland
These authors had equal contribution to this work
Submission date: 2023-10-17
Final revision date: 2023-12-01
Acceptance date: 2023-12-12
Online publication date: 2023-12-12
Publication date: 2023-12-12
Corresponding author
Andrzej Puchalski   

Casimir Pulaski Radom University, Mechanical Faculty, Stasieckiego 54, 26-600 Radom, Poland
Diagnostyka 2023;24(4):2023413
The vibration signal is one of the most essential diagnostic signals, the analysis of which allows for determining the dynamic state of the monitored machine set. In the era of cyber-physical industrial systems, making diagnostic decisions involves the study of large databases from previous registers and data downloaded from machines in real-time. However, the recorded signals mainly concern the operational status of the monitored object. Insufficient training data regarding failure states hinders the operation of classification algorithms. Progress in machine learning has created a new avenue for the advancement of diagnostic methods based on models. These methods now have the capability to produce signals through random sampling from a hidden space or generate fresh instances of input data from noise. The article suggests the use of a Generative Adversarial Network (GAN) model as a tool to create synthetic measurement observations for vibration monitoring. The effectiveness of the synthetic data generation algorithm was verified on the example of the vibration signal recorded during tests of the drive system of a motor vehicle.
no founding
Abdel-Jaber H, Devassy D, Al Salam A, Hidaytallah L, EL-Amir M. A review of deep learning algorithms and their applications in healthcare. Algorithms 2022; 15(2): 71.
Alqahtani H, Kavakli-Thorne M, Kumar G. Applications of generative adversarial networks (GANs): an updated review. Archives of Computational Methods in Engineering 2021; 28(2): 525–552.
Dash A, Ye J, Wang G. A review of generative adversarial networks (GANs) and its applications in a wide variety of disciplines -- from medical to remote sensing. 2021
Donahue C, McAuley J, Puckette M. Adversarial audio synthesis. ICLR 2019; 1–16.
Goodfellow I, Bengio Y, Courville A. Deep learning. Massachusetts Institute of Technology Press 2016.
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S. Generative adversarial networks. Communications of the ACM 2020; 63(11): 139–44.
Goodfellow I. NIPS 2016 tutorial: generative adversarial networks. 2017.
Hassani H, Razavi-Far R, Saif M, Palade V. Generative adversarial network-based scheme for diagnosing faults in cyber-physical power systems. Sensors 2021;21(15):5173.
Jang K, Hong S, Kim M, Na J, Moon I. Adversarial autoencoder based feature learning for fault detection in industrial processes. IEEE Transactions on Industrial Informatics 2022; 18(2): 827–34.
Kingma DP, Rezende DJ, Mohamed S, Welling M. Semi-supervised learning with deep generative models. 2014.
Kingma DP, Welling M. Auto-encoding variational bayes . 2022.
Kingma DP. Fast gradient-based inference with continuous latent variable models in auxiliary form. 2013.
Nikolenko SI. Synthetic data for deep learning. Springer optimization and its applications 2021; 174.
Puchalski A, Komorska I. Generative modelling of vibration signals in machine maintenance. Eksploatacja i Niezawodnosc–Maintenance and Reliability 2023;25(4).
Ramires A, Chandna P, Favory X, G'omez E, Serra X. Neural percussive synthesis parameterised by high-level timbral features. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2019; 786-790.
Razghandi M, Zhou H, Erol-Kantarci M, Turgut D. Variational autoencoder generative adversarial network for synthetic data generation in smart home . 2022.
Regenwetter L, Nobari AH, Ahmed F. Deep generative models in engineering design: a review. 2022.
Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X. Improved techniques for training GANs. 2016.
Tomczak JM. Deep generative modeling. Deep Generative Modeling. Cham: Springer International Publishing 2022; 1–197.
Journals System - logo
Scroll to top