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Luigi Romano - IEEE Xplore Author Profile
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Luigi Romano

Also published under: L. Romano

Publications
51
Citations
478
Publications by Year
19972025
Co-Authors:
Show All Co-Authors (81)
Image of author Luigi Romano

Luigi Romano

Also published under: L. Romano

Affiliation

University of Naples “Parthenope” Centro Direzionale, Naples, Italy

Publication Topics

Biography

Luigi Romano received the PhD degree. He is a full professor with the University of Naples Parthenope, Italy. His research interests are system security and dependability, with focus on Critical Infrastructure Protection. He has worked extensively as a consultant for industry leaders in the field of security- and safety-critical computer systems. He was one of the members of the ENISA expert group on Priorities of Research On Current and Emerging Network Technologies (PROCENT).(Based on document published on 18 May 2020).
Publications
51
Citations
478
Publications by Year
19972025
Co-Authors:
Show All Co-Authors (81)
Author's Published Works

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Biomimetic Underwater Soft Snake Robot: Self-Motion Sensing and Online Gait Control

Hang Shi;Yali Meng;Wenlong Cui;Meng Rao;Shuting Wang;Yangmin Xie

IEEE Transactions on Robotics
Year: 2025 | Volume: 41 | Journal Article |
This study draws inspiration from the locomotion and adaptability of aquatic snakes to develop an innovative soft-bodied, hydraulic-driven untethered underwater snake robot “BaiLong.” The robot consists of a segmented soft structure and embeds actuation, control, and power modules in the head. Featuring the self-shape perception capability, it leverages an online iterative learning control method ...Show More

Biomimetic Underwater Soft Snake Robot: Self-Motion Sensing and Online Gait Control

Hang Shi;Yali Meng;Wenlong Cui;Meng Rao;Shuting Wang;Yangmin Xie

Year: 2025 | Volume: 41 | Journal Article |

Modified Varying Index Coefficient Autoregression Model for Representation of the Nonstationary Vibration From a Planetary Gearbox

Yuejian Chen;Meng Rao;Ke Feng;Gang Niu

IEEE Transactions on Instrumentation and Measurement
Year: 2023 | Volume: 72 | Journal Article |
Cited by: Papers (22)
Planetary gearbox fault detection is important in terms of life-threatening failure prevention and maintenance optimization. This article focuses on the representation of the planetary gearbox baseline vibration signals via time series models. Faults can be detected by examining any changes in model residuals or parameters. The varying index coefficient autoregression (VICAR) model is a good optio...Show More

Modified Varying Index Coefficient Autoregression Model for Representation of the Nonstationary Vibration From a Planetary Gearbox

Yuejian Chen;Meng Rao;Ke Feng;Gang Niu

Year: 2023 | Volume: 72 | Journal Article |

Speed Adaptive Gates for Deep-Learning-based Fault Classification of Rotating Machinery under Varying Speed Conditions: Comparison of Implementations

Meng Rao;Ming J. Zuo;Zhigang Tian;Dali Qin

2022 Global Reliability and Prognostics and Health Management (PHM-Yantai)
Year: 2022 | Conference Paper |
Deep-learning-based rotating machinery fault classification often suffers from the problem of speed induced fault information imbalance when applied to varying speed conditions. The speed adaptive gate (SAG) is an effective auxiliary branch that assists existing deep learning models addressing that problem. This paper presents four types of implementations for SAGs, i.e., the element-wise, channel...Show More

Speed Adaptive Gates for Deep-Learning-based Fault Classification of Rotating Machinery under Varying Speed Conditions: Comparison of Implementations

Meng Rao;Ming J. Zuo;Zhigang Tian;Dali Qin

Year: 2022 | Conference Paper |

Virtual rotating speed meter: extracting machinery rotating speed from vibration signals based on deep learning and transfer learning

Meng Rao;Qing Li;Dongdong Wei;Ming J. Zuo

2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
Rotating machinery like wind turbines often operates under varying speed conditions. Measuring speed signals is critical for vibration-based condition monitoring of rotating machines. However, in real applications, sometimes it is difficult to install speed sensors to collect speed signals due to physical space and/or cost restrictions. Considering vibration signals are widely collected for condit...Show More

Virtual rotating speed meter: extracting machinery rotating speed from vibration signals based on deep learning and transfer learning

Meng Rao;Qing Li;Dongdong Wei;Ming J. Zuo

Year: 2020 | Conference Paper |

A New Strategy for Rotating Machinery Fault Diagnosis Under Varying Speed Conditions Based on Deep Neural Networks and Order Tracking

Meng Rao;Ming J Zuo

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)
Year: 2018 | Conference Paper |
Cited by: Papers (15)
Rotating machines are widely used in industry and often work under harsh and varying speed conditions. Fault diagnosis under varying speed conditions is needed to prevent major shutdowns. This paper aims to develop an intelligent rotating machinery fault diagnosis strategy based on deep neural networks (DNNs) and order tracking (OT). The developed strategy can automatically conduct rotating machin...Show More

A New Strategy for Rotating Machinery Fault Diagnosis Under Varying Speed Conditions Based on Deep Neural Networks and Order Tracking

Meng Rao;Ming J Zuo

Year: 2018 | Conference Paper |

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