IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v247y2024ics0951832024002084.html
   My bibliography  Save this article

Reliability improvement of the dredging perception system: A sensor fault-tolerant strategy

Author

Listed:
  • Wang, Bin
  • Zio, Enrico
  • Chen, Xiuhan
  • Zhu, Hanhua
  • Guo, Yunhua
  • Fan, Shidong

Abstract

In the dredging industry, the automation and accuracy of the Dredging Perception System (DPS) are vital for operational efficiency and environmental safety. Current DPS implementations face challenges with sensor fault tolerance, leading to system unreliability and increased false alarm rates that can disrupt dredging operations. We propose a Hybrid Redundancy Sensor Fault Tolerance (HRSFT) strategy that integrates matching physical sensors (PS) with two distinct types of virtual sensors (VS) driven by multi-sensor association and time-series prediction models. The HRSFT employs a voting-cold storage strategy to address the false alarm issues commonly associated with single virtual sensor systems. Through experimental validation, the HRSFT strategy has demonstrated its capability to provide accurate replacement information during both single and multi-sensor failure scenarios, effectively managing abnormal sensor data and enhancing the operational reliability of the DPS. The implementation of the HRSFT strategy significantly improves the accuracy and stability of the DPS, suggesting a substantial advancement in sensor fault tolerance that could be applied to similar systems in various industries, leading to safer and more reliable operations.

Suggested Citation

  • Wang, Bin & Zio, Enrico & Chen, Xiuhan & Zhu, Hanhua & Guo, Yunhua & Fan, Shidong, 2024. "Reliability improvement of the dredging perception system: A sensor fault-tolerant strategy," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:reensy:v:247:y:2024:i:c:s0951832024002084
    DOI: 10.1016/j.ress.2024.110134
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024002084
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2024.110134?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Alanen, Jarmo & Linnosmaa, Joonas & Malm, Timo & Papakonstantinou, Nikolaos & Ahonen, Toni & Heikkilä, Eetu & Tiusanen, Risto, 2022. "Hybrid ontology for safety, security, and dependability risk assessments and Security Threat Analysis (STA) method for industrial control systems," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    2. Gao, Shan & Wang, Jinting & Zhang, Jie, 2023. "Reliability analysis of a redundant series system with common cause failures and delayed vacation," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    3. Wang, Shaoxuan & Yao, Yuantao & Ge, Daochuan & Lin, Zhixian & Wu, Jie & Yu, Jie, 2023. "Reliability evaluation of standby redundant systems based on the survival signatures methods," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    4. Yang, Zhe & Baraldi, Piero & Zio, Enrico, 2022. "A method for fault detection in multi-component systems based on sparse autoencoder-based deep neural networks," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    5. Peiravi, Abdossaber & Nourelfath, Mustapha & Zanjani, Masoumeh Kazemi, 2022. "Universal redundancy strategy for system reliability optimization," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    6. Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    7. Ouyang, Zhiyuan & Liu, Yu & Ruan, Sheng-Jia & Jiang, Tao, 2019. "An improved particle swarm optimization algorithm for reliability-redundancy allocation problem with mixed redundancy strategy and heterogeneous components," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 62-74.
    8. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    9. Cheng, Dawei & Lu, Zhong & Zhou, Jia & Liang, Xihui, 2023. "An optimizing maintenance policy for airborne redundant systems operating with faults by using Markov process and NSGA-II," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Xirui & Liu, Hui, 2025. "Domain correction for hydraulic internal pump leakage detection considering multiclass aberrant flow data," Reliability Engineering and System Safety, Elsevier, vol. 253(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zheng, Shuwen & Wang, Chong & Zio, Enrico & Liu, Jie, 2024. "Fault detection in complex mechatronic systems by a hierarchical graph convolution attention network based on causal paths," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. Zhou, Haoxuan & Wang, Bingsen & Zio, Enrico & Lei, Zihao & Wen, Guangrui & Chen, Xuefeng, 2025. "Unsupervised anomaly detection of machines operating under time-varying conditions: DCD-VAE enabled feature disentanglement of operating conditions and states," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    3. Aven, Terje & Rios Insua, David & Soyer, Refik & Zhu, Xiaoyan & Zio, Enrico, 2025. "Fifty years of reliability in operations research," European Journal of Operational Research, Elsevier, vol. 324(2), pages 361-381.
    4. Zheng, Shuwen & Pan, Kai & Liu, Jie & Chen, Yunxia, 2024. "Empirical study on fine-tuning pre-trained large language models for fault diagnosis of complex systems," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    5. Beal, John & Reihani, Seyed & Sakurahara, Tatsuya & Kee, Ernie & Mohaghegh, Zahra, 2025. "Modeling nuclear power plant piping reliability by coupling a human reliability analysis-based maintenance model with a physical degradation model," Reliability Engineering and System Safety, Elsevier, vol. 255(C).
    6. He, Jiahui & Cheng, Zhijun & Guo, Bo, 2024. "Anomaly detection in telemetry data using a jointly optimal one-class support vector machine with dictionary learning," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    7. Zheng, Jianfei & Ren, Jincheng & Pei, Hong & Zhang, Jianxun & Zhang, Zhengxin, 2024. "Lifetime prediction and replacement optimization for a standby system considering storage failures of spare parts," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    8. Mandelli, Diego & Wang, Congjian & Agarwal, Vivek & Lin, Linyu & Manjunatha, Koushik A., 2024. "Reliability modeling in a predictive maintenance context: A margin-based approach," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    9. Zhou, Haoxuan & Wang, Bingsen & Zio, Enrico & Wen, Guangrui & Liu, Zimin & Su, Yu & Chen, Xuefeng, 2023. "Hybrid system response model for condition monitoring of bearings under time-varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    10. Pinciroli, Luca & Baraldi, Piero & Zio, Enrico, 2023. "Maintenance optimization in industry 4.0," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    11. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2024. "Optimal tasks assignment policy in multi-task multi-attempt missions," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    12. Floreale, Giovanni & Baraldi, Piero & Lu, Xuefei & Rossetti, Paolo & Zio, Enrico, 2024. "Sensitivity analysis by differential importance measure for unsupervised fault diagnostics," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    13. Xia, Liqiao & Liang, Yongshi & Leng, Jiewu & Zheng, Pai, 2023. "Maintenance planning recommendation of complex industrial equipment based on knowledge graph and graph neural network," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    14. Llasag Rosero, Raúl & Silva, Catarina & Ribeiro, Bernardete & Albisser, Melania & Brutsche, Martin & Arias Chao, Manuel, 2025. "Label synchronization strategies for hybrid federated learning," Reliability Engineering and System Safety, Elsevier, vol. 256(C).
    15. Zaretalab, Arash & Sharifi, Mani & Guilani, Pedram Pourkarim & Taghipour, Sharareh & Niaki, Seyed Taghi Akhavan, 2022. "A multi-objective model for optimizing the redundancy allocation, component supplier selection, and reliable activities for multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    16. Mingfei Li & Jiajian Wu & Zhengpeng Chen & Jiangbo Dong & Zhiping Peng & Kai Xiong & Mumin Rao & Chuangting Chen & Xi Li, 2022. "Data-Driven Voltage Prognostic for Solid Oxide Fuel Cell System Based on Deep Learning," Energies, MDPI, vol. 15(17), pages 1-20, August.
    17. Zhu, Yunyi & Xie, Bin & Wang, Anqi & Qian, Zheng, 2025. "Wind turbine fault detection and identification via self-attention-based dynamic graph representation learning and variable-level normalizing flow," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    18. Dong, Jie & Li, Daye & Cong, Zhiyu & Peng, Kaixiang, 2025. "A new fault detection method based on an updatable hybrid model for hard-to-detect faults in nonstationary processes," Reliability Engineering and System Safety, Elsevier, vol. 259(C).
    19. Zhao, Xian & Wang, Xinlei & Dai, Ying & Qiu, Qingan, 2024. "Joint optimization of loading, mission abort and rescue site selection policies for UAV," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    20. Zhang, Dingyang & Zhang, Yiming & Li, Pei & Zhang, Shuyou, 2025. "Kernel Reinforcement Learning for sampling-efficient risk management of large-scale engineering systems," Reliability Engineering and System Safety, Elsevier, vol. 260(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:247:y:2024:i:c:s0951832024002084. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.