Probabilistic Uncertainty-Aware Decision Fusion of Neural Network for Bearing Fault Diagnosis

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Published Jun 27, 2024
Atabak mostafavi Mohammad Siami Andreas Friedmann Tomasz Barszcz Radoslaw Zimroz

Abstract

Reliability is a central aspect of machine learning applications, especially in fault diagnosis systems, where only an accurate and reliable diagnosis system is economically justifiable, considering that any false diagnosis would lead to an increase in maintenance costs or a reduction in system efficiency. Recent advances in machine learning (ML) techniques have encouraged condition monitoring researchers to focus their efforts on finding suitable ML-based solutions for system condition assessment. However, to address the reliability issue, it is crucial to consider a larger amount of data measured by heterogeneous sensors on the system together with non-sensor information. The trend of data fusion has already started in other areas of ML application, and many of today's state-of-the-art models benefit from various types of fusion techniques to improve their accuracy. However, traditional classifiers do not provide any information about the prediction uncertainty, and they tend to show falsely high confidence when encountering low-quality data or previously unseen classes. Fusion of different data sources without considering the epistemic or aleatory uncertainty can lead to a deterioration of the result. Bayesian frameworks have traditionally been used to quantify uncertainty of systems; however, only recent advances made it possible to successfully implement Bayesian ML models.

The research methodology was investigated using the MAFAULDA dataset generated by SpectraQuest's Machinery Fault Simulator. This simulator experimentally simulated various bearing conditions, including normal operation and inner and outer ring bearing failures, at variable speeds. The dataset consists of 1951 instances measured using two triaxial accelerometers, a microphone, and a tachometer.

Diagnosis has been done via two multi label 1D Convolutional Neural Networks - each for a selected sensor - and their prediction along with their associated uncertainty quantity has been fused utilizing Bayesian model averaging. The methodology is capable of fusion of various decisions made based on different data sources and generate a unified decision with associated confidence level. Fusion process is uncertainty aware and application of 1D networks reduce the amount of data needed.

How to Cite

mostafavi, A., Siami, M. ., Friedmann, A. ., Barszcz, T. ., & Zimroz, R. . (2024). Probabilistic Uncertainty-Aware Decision Fusion of Neural Network for Bearing Fault Diagnosis. PHM Society European Conference, 8(1), 10. https://doi.org/10.36001/phme.2024.v8i1.4010
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Keywords

Bearing Fault Diagnosis, Bayesian Model Averaging, Uncertainty, Decision Fusion, multi-label classification

References
[1] David McMillan and Graham Ault, Eds., Towards Quantification of Condition Monitoring Benefit for Wind Turbine Generators, 2007.

[2] H. D. M. de Azevedo, A. M. Araújo, and N.

practical innovations, open solutions, vol. 9, pp. 23717–23725, 2021, doi:

10.1109/ACCESS.2021.3056767.

[10] G. J. Klir, Uncertainty and information:

Foundations of generalized information theory. Hoboken N.J.: Wiley-Interscience, 2006.

[11] S. Hassani, U. Dackermann, M. Mousavi, and J.

Bouchonneau, "A review of wind turbine bearing condition monitoring: State of the art and challenges," Renewable and Sustainable Energy Reviews, vol. 56, pp. 368–379, 2016, doi: 10.1016/j.rser.2015.11.032.

[3] M. Tiboni, C. Remino, R. Bussola, and C. Amici, "A Review on Vibration-Based Condition Monitoring of Rotating Machinery," Applied Sciences, vol. 12, no. 3, p. 972, 2022, doi:

Li, "A systematic review of data fusion techniques for optimized structural health monitoring," Information Fusion, vol. 103, p. 102136, 2024, doi:

10.1016/j.inffus.2023.102136.

[12] D. Wheeler, Esther D Meenken, Martin Espig, and Mos Sharifi, Eds., UNCERTAINTY -WHAT IS IT?, 2020.

10.3390/app12030972.

[13] Grigorios Tsoumakas, Ioannis Katakis, and Ioannis Vlahavas, "A Review of Multi-Label Classification Methods,"

[4] C. Zhou, K. Guo, and J. Sun, "An integrated wireless vibration sensing tool holder for milling tool condition monitoring with singularity analysis," Measurement, vol. 174, p. 109038, 2021, doi:

10.1016/j.measurement.2021.109038.

[5] N. Tandon, "A comparison of some vibration parameters for the condition monitoring of rolling element bearings," Measurement, vol. 12, no. 3, pp. 285–289, 1994, doi:

[14] Q. Guan and Y. Huang, "Multi-label chest X-ray image classification via category-wise residual attention learning," Pattern Recognition Letters, vol. 130, pp. 259–266, 2020, doi:

10.1016/j.patrec.2018.10.027.

[15] D. Wang and S. Zhang, "Unsupervised Person Re-identification via Multi-label Classification," 20-Apr-20. [Online]. Available: http:// arxiv.org/pdf/2004.09228

10.1016/0263-2241(94)90033-7.

[6] F. Jia et al., "A method of automatic feature extraction from massive vibration signals of machines," in 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings, 2016, pp. 1–6.

[7] C. Mongia, D. Goyal, and S. Sehgal, "Vibration response-based condition monitoring and fault diagnosis of rotary machinery," Materials Today: Proceedings, vol. 50, pp. 679–683, 2022, doi: 10.1016/j.matpr.2021.04.395.

[8] S. R. Saufi, Z. A. B. Ahmad, M. S. Leong, and M.

H. Lim, "Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample," IEEE Trans. Ind. Inf., vol. 16, no. 10, pp. 62636271, 2020, doi: 10.1109/TII.2020.2967822.

[9] J. Wang, D. Wang, S. Wang, W. Li, and K. Song, "Fault Diagnosis of Bearings Based on MultiSensor Information Fusion and 2D Convolutional Neural Network," IEEE access :

[16] A. Pal, M. Selvakumar, and M. Sankarasubbu, "MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network," 12th International Conference on Agents and Artificial Intelligence (ICAART, pp. 494–505, 22-Mar-2020, doi:10.5220/0008940304940505.

[17] A. C. P. L. F. de Carvalho and A. A. Freitas, "A Tutorial on Multi-label Classification Techniques," in Studies in Computational Intelligence, v. 201-206, Foundations of computational intelligence, A. E. Hassanien, Ed., Berlin: Springer, 2009-, pp. 177–195.

[18] T. M. Fragoso, W. Bertoli, and F. Louzada, "Bayesian Model Averaging: A Systematic Review and Conceptual Classification," Int Statistical Rev, vol. 86, no. 1, pp. 1–28, 2018, doi: 10.1111/insr.12243.
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Technical Papers