Neutrosophic and Information Fusion

Journal DOI

https://doi.org/10.54216/NIF

Submit Your Paper

2836-7863ISSN (Online)

Volume 2 , Issue 2 , PP: 32-41, 2023 | Cite this article as | XML | Html | PDF | Full Length Article

A Comprehensive Approach to Asset Degradation Modeling via Sensory Data Fusion for Remaining Useful Life Prediction

Durdona Davletova 1 *

  • 1 Department of International Business Management, Tashkent State University of Economics, Uzbekistan - (d.davletova@tsue.uz)
  • Doi: https://doi.org/10.54216/NIF.020204

    Received: June 25, 2023 Accepted: November 28, 2023
    Abstract

    For effective management of assets, accurate forecasting for system failures is necessary. Sensory data fusion is a viable option to predict Remaining Useful Life (RUL) in assets by combining multiple data sources for improved prediction capabilities. This research paper aims at predicting RUL integrating various sensory data streams. Using Artificial Neural Networks (ANN), this research aims at synthesizing, learning from, and fusing information emanating from different sensors leading to accurate RUL estimations required for proactive maintenance strategies. The methodology in this study involves the use of ANN architectures for processing multivariate time-series data collected from sensors. By iterative training, the ANN captures complex relationships within the data allowing the integration of different information sources thus aiding in RUL predictions. Through the synthesis of sensory data by the ANN model, promising results have been achieved in predicting RUL. The model effectively learns from multiple sources demonstrating enhanced accuracy in estimating remaining operational cycles before asset failure.

    Keywords :

    Sensory Information Fusion , Remaining Useful Life , Predictive Maintenance , Condition Monitoring , Sensor Integration , Prognostics and Health Management (PHM) , Multi-Sensor Fusion.

    References

    [1]    Coble, Jamie Baalis. 2010. “Merging Data Sources to Predict Remaining Useful Life--an Automated Method to Identify Prognostic Parameters.”

    [2]    Roman, Darius V, Ross W Dickie, David Flynn, and Valentin Robu. 2017. “A Review of the Role of Prognostics in Predicting the Remaining Useful Life of Assets.” In 27th European Safety and Reliability Conference 2017, 897–904.

    [3]    Mosallam, Ahmed, Kamal Medjaher, and Noureddine Zerhouni. 2016. “Data-Driven Prognostic Method Based on Bayesian Approaches for Direct Remaining Useful Life Prediction.” Journal of Intelligent Manufacturing 27: 1037–48.

    [4]    Liao, Linxia, and Felix Köttig. 2014. “Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction.” IEEE Transactions on Reliability 63 (1): 191–207.

    [5]    Sayyad, Sameer, Satish Kumar, Arunkumar Bongale, Pooja Kamat, Shruti Patil, and Ketan Kotecha. 2021. “Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions.” IEEE Access 9: 110255–86.

    [6]    Galar, Diego, Kai Goebel, Peter Sandborn, and Uday Kumar. 2021. Prognostics and Remaining Useful Life (RUL) Estimation: Predicting with Confidence. CRC Press.

    [7]    Li, Lun, Jiaoyue Liu, Sixiao Wei, Genshe Chen, Erik Blasch, and Khanh Pham. 2021. “Smart Robot-Enabled Remaining Useful Life Prediction and Maintenance Optimization for Complex Structures Using Artificial Intelligence and Machine Learning.” In Sensors and Systems for Space Applications XIV, 11755:100–108.

    [8]    Chen, Zhenghua, Min Wu, Rui Zhao, Feri Guretno, Ruqiang Yan, and Xiaoli Li. 2020. “Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach.” IEEE Transactions on Industrial Electronics 68 (3): 2521–31.

    [9]    Xiong, Jiawei, Jian Zhou, Yizhong Ma, Fengxia Zhang, and Chenglong Lin. 2023. “Adaptive Deep Learning-Based Remaining Useful Life Prediction Framework for Systems with Multiple Failure Patterns.” Reliability Engineering \& System Safety 235: 109244.

    [10] Niu, Gang, Bo-Suk Yang, and Michael Pecht. 2010. “Development of an Optimized Condition-Based Maintenance System by Data Fusion and Reliability-Centered Maintenance.” Reliability Engineering \& System Safety 95 (7): 786–96.

    [11] Loutas, Theodoros H, Dimitrios Roulias, and George Georgoulas. 2013. “Remaining Useful Life Estimation in Rolling Bearings Utilizing Data-Driven Probabilistic E-Support Vectors Regression.” IEEE Transactions on Reliability 62 (4): 821–32.

    [12] Okoh, Caxton, Rajkumar Roy, Jorn Mehnen, and L Redding. 2014. “Overview of Remaining Useful Life Prediction Techniques in Through-Life Engineering Services.” Procedia Cirp 16: 158–63.

    [13] Si, Xiao-Sheng, Wenbin Wang, Chang-Hua Hu, and Dong-Hua Zhou. 2011. “Remaining Useful Life Estimation--a Review on the Statistical Data Driven Approaches.” European Journal of Operational Research 213 (1): 1–14.

    [14] Sikorska, Joanna Z, Melinda Hodkiewicz, and Lin Ma. 2011. “Prognostic Modelling Options for Remaining Useful Life Estimation by Industry.” Mechanical Systems and Signal Processing 25 (5): 1803–36.

    [15] Si, Xiao-Sheng, Wenbin Wang, Chang-Hua Hu, Mao-Yin Chen, and Dong-Hua Zhou. 2013. “A Wiener-Process-Based Degradation Model with a Recursive Filter Algorithm for Remaining Useful Life Estimation.” Mechanical Systems and Signal Processing 35 (1–2): 219–37.

    [16] Costa, Paulo Roberto de Oliveira da, Alp Akçay, Yingqian Zhang, and Uzay Kaymak. 2020. “Remaining Useful Lifetime Prediction via Deep Domain Adaptation.” Reliability Engineering \& System Safety 195: 106682.

    [17] Si, Xiao-Sheng, Wenbin Wang, Chang-Hua Hu, Dong-Hua Zhou, and Michael G Pecht. 2012. “Remaining Useful Life Estimation Based on a Nonlinear Diffusion Degradation Process.” IEEE Transactions on Reliability 61 (1): 50–67.

    [18] Ruiz-Tagle Palazuelos, Andres, Enrique López Droguett, and Rodrigo Pascual. 2020. “A Novel Deep Capsule Neural Network for Remaining Useful Life Estimation.” Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234 (1): 151–67.

    [19] Mao, Wentao, Jianliang He, and Ming J Zuo. 2019. “Predicting Remaining Useful Life of Rolling Bearings Based on Deep Feature Representation and Transfer Learning.” IEEE Transactions on Instrumentation and Measurement 69 (4): 1594–1608.

    [20] Mazurkiewicz, Dariusz, Yi Ren, and Cheng Qian. 2023. “Novel Approach to Prognostics and Health Management to Combine Reliability and Process Optimisation.” In Advances in Reliability and Maintainability Methods and Engineering Applications: Essays in Honor of Professor Hong-Zhong Huang on His 60th Birthday, 559–80. Springer.

    [21] Zhang, Zhengxin, Xiaosheng Si, Changhua Hu, and Xiangyu Kong. 2015. “Degradation Modeling--Based Remaining Useful Life Estimation: A Review on Approaches for Systems with Heterogeneity.” Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 229 (4): 343–55.

    [22] Si, Xiao-Sheng, Zheng-Xin Zhang, Chang-Hua Hu, and others. 2017. “Data-Driven Remaining Useful Life Prognosis Techniques.” Beijing, China: National Defense Industry Press and Springer-Verlag GmbH.

    [23] Jardine, Andrew K S, Daming Lin, and Dragan Banjevic. 2006. “A Review on Machinery Diagnostics and Prognostics Implementing Condition-Based Maintenance.” Mechanical Systems and Signal Processing 20 (7): 1483–1510.

    [24] Kwon, Daeil, Melinda R Hodkiewicz, Jiajie Fan, Tadahiro Shibutani, and Michael G Pecht. 2016. “IoT-Based Prognostics and Systems Health Management for Industrial Applications.” IEEE Access 4: 3659–70.

    [25] Kontar, Raed, Junbo Son, Shiyu Zhou, Chaitanya Sankavaram, Yilu Zhang, and Xinyu Du. 2017. “Remaining Useful Life Prediction Based on the Mixed Effects Model with Mixture Prior Distribution.” IISE Transactions 49 (7): 682–97.

    [26] Chao, Manuel Arias, Chetan Kulkarni, Kai Goebel, and Olga Fink. 2022. “Fusing Physics-Based and Deep Learning Models for Prognostics.” Reliability Engineering \& System Safety 217: 107961.

    Cite This Article As :
    Davletova, Durdona. A Comprehensive Approach to Asset Degradation Modeling via Sensory Data Fusion for Remaining Useful Life Prediction. Neutrosophic and Information Fusion, vol. , no. , 2023, pp. 32-41. DOI: https://doi.org/10.54216/NIF.020204
    Davletova, D. (2023). A Comprehensive Approach to Asset Degradation Modeling via Sensory Data Fusion for Remaining Useful Life Prediction. Neutrosophic and Information Fusion, (), 32-41. DOI: https://doi.org/10.54216/NIF.020204
    Davletova, Durdona. A Comprehensive Approach to Asset Degradation Modeling via Sensory Data Fusion for Remaining Useful Life Prediction. Neutrosophic and Information Fusion , no. (2023): 32-41. DOI: https://doi.org/10.54216/NIF.020204
    Davletova, D. (2023) . A Comprehensive Approach to Asset Degradation Modeling via Sensory Data Fusion for Remaining Useful Life Prediction. Neutrosophic and Information Fusion , () , 32-41 . DOI: https://doi.org/10.54216/NIF.020204
    Davletova D. [2023]. A Comprehensive Approach to Asset Degradation Modeling via Sensory Data Fusion for Remaining Useful Life Prediction. Neutrosophic and Information Fusion. (): 32-41. DOI: https://doi.org/10.54216/NIF.020204
    Davletova, D. "A Comprehensive Approach to Asset Degradation Modeling via Sensory Data Fusion for Remaining Useful Life Prediction," Neutrosophic and Information Fusion, vol. , no. , pp. 32-41, 2023. DOI: https://doi.org/10.54216/NIF.020204