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Naipeng Li - IEEE Xplore Author Profile
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Naipeng Li

Publications
37
Citations
4,054
Publications by Year
20152024
Co-Authors:
Show All Co-Authors (43)
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Naipeng Li

Affiliation

Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System
Xi'an Jiaotong University
Xi'an, China

Publication Topics

Biography

Naipeng Li was born in Laiwu, Shandong Province, China. He received the B.S. degree in mechanical engineering from Shandong Agricultural University, Tai'an, China, in 2012, and the Ph.D. degree in mechanical engineering from Xi'an Jiaotong University, Xi'an, China, in 2019.,He is currently an Assistant Professor of mechanical engineering with Xi'an Jiaotong University. He was a Visting Scholar of the Georgia Institute of Technology, Atlanta, GA, USA. His research interests include machinery condition monitoring, intelligent fault diagnostics and remaining useful life prediction of rotating machinery.(Based on document published on 19 November 2020).
Publications
37
Citations
4,054
Publications by Year
20152024
Co-Authors:
Show All Co-Authors (43)
Author's Published Works

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Lithium-Ion Battery Health Estimation with Incomplete Charging Data and Spiking ResNet

Mingyang Wang;Naipeng Li;Yaguo Lei;Xiang Li;Bin Yang

2024 Global Reliability and Prognostics and Health Management Conference (PHM-Beijing)
Year: 2024 | Conference Paper |
The accurate estimation of state of health (SOH) plays a crucial role in ensuring the safe operation of batteries. Most existing SOH estimation methods are based on complete charging or discharging data. However, in real-world applications, data are often derived from partial charging or discharging processes influenced by varying user habits, which brings great challenges to the accurate SOH esti...Show More

Lithium-Ion Battery Health Estimation with Incomplete Charging Data and Spiking ResNet

Mingyang Wang;Naipeng Li;Yaguo Lei;Xiang Li;Bin Yang

Year: 2024 | Conference Paper |

New Look at Bayesian Prognostic Methods

Jie Liu;Dong Wang;Jin-Zhen Kong;Naipeng Li;Zhike Peng;Kwok-Leung Tsui

IEEE Transactions on Automation Science and Engineering
Year: 2024 | Early Access Article |
Online remaining useful life (RUL) prediction is a core function of prognostics and health management (PHM), which provides solutions for comprehensive and personalised system management. RUL is realised by extrapolating timely updated prognostic models to reach a user-defined failure threshold. As of today, there are mainly two kinds of Bayesian prognostic methods. The first kind of Bayesian prog...Show More

New Look at Bayesian Prognostic Methods

Jie Liu;Dong Wang;Jin-Zhen Kong;Naipeng Li;Zhike Peng;Kwok-Leung Tsui

Year: 2024 | Early Access Article |

Multimodal Correlation-Aware Fusion Framework for Enhanced Machinery Health Prognosis With Unlabeled and Low-Quality Data Exploitation

Yuan Wang;Yaguo Lei;Naipeng Li;Xiang Li;Bin Yang

IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Early Access Article |
Accurate machinery health prognosis, also known as remaining useful life (RUL) prediction, is critical for preventing catastrophic accidents and implementing predictive maintenance strategies. This makes it a highly attractive research area. Many existing studies have been developed on unimodal data, yet such data can only provide a restricted perspective and incomplete health state monitoring. So...Show More

Multimodal Correlation-Aware Fusion Framework for Enhanced Machinery Health Prognosis With Unlabeled and Low-Quality Data Exploitation

Yuan Wang;Yaguo Lei;Naipeng Li;Xiang Li;Bin Yang

Year: 2024 | Early Access Article |

Dynamic Vision-Based Machinery Fault Diagnosis with Cross-Modality Feature Alignment

Xiang Li;Shupeng Yu;Yaguo Lei;Naipeng Li;Bin Yang

IEEE/CAA Journal of Automatica Sinica
Year: 2024 | Volume: 11, Issue: 10 | Journal Article |
Cited by: Papers (7)
Intelligent machinery fault diagnosis methods have been popularly and successfully developed in the past decades, and the vibration acceleration data collected by contact accelerometers have been widely investigated. In many industrial scenarios, contactless sensors are more preferred. The event camera is an emerging bio-inspired technology for vision sensing, which asynchronously records per-pixe...Show More

Dynamic Vision-Based Machinery Fault Diagnosis with Cross-Modality Feature Alignment

Xiang Li;Shupeng Yu;Yaguo Lei;Naipeng Li;Bin Yang

Year: 2024 | Volume: 11, Issue: 10 | Journal Article |

Self-data-driven Electromechanical Coupling Model Construction of Industrial Robots

Yang Tan;Naipeng Li;Yaguo Lei;Huitong Li;Bin Yang;Xiang Li

2024 International Symposium on Intelligent Robotics and Systems (ISoIRS)
Year: 2024 | Conference Paper |
As a core component of industrial robots, the health state of RV reducers directly determines the operating accuracy and response speed of industrial robots. To ensure the reliability of robot operation, it is necessary to monitor the health state of RV reducers. Generally, the built-in sensors, such as encoders and current sensors, are integrated in the motor side of the industrial robot. Therefo...Show More

Self-data-driven Electromechanical Coupling Model Construction of Industrial Robots

Yang Tan;Naipeng Li;Yaguo Lei;Huitong Li;Bin Yang;Xiang Li

Year: 2024 | Conference Paper |

Graph Continual Learning Network: An Incremental Intelligent Diagnosis Method of Machines for New Fault Detection

Shuhui Wang;Yaguo Lei;Na Lu;Bin Yang;Xiang Li;Naipeng Li

IEEE Transactions on Automation Science and Engineering
Year: 2024 | Early Access Article |
Streaming data of machines is continuously collected in practical applications, which produces new fault information with respect to the health change. Therefore, a lifelong-learning intelligent diagnosis model is desired for new fault type recognition based on the streaming data. However, existing research in intelligent fault diagnosis always treats new fault type detection and class incremental...Show More

Graph Continual Learning Network: An Incremental Intelligent Diagnosis Method of Machines for New Fault Detection

Shuhui Wang;Yaguo Lei;Na Lu;Bin Yang;Xiang Li;Naipeng Li

Year: 2024 | Early Access Article |

Intelligent Prototype Generation Method On Machine Fault Diagnosis Through Compressing Large Dataset

Yixiao Xu;Xiang Li;Yaguo Lei;Bin Yang;Naipeng Li

2024 7th International Symposium on Autonomous Systems (ISAS)
Year: 2024 | Conference Paper |
In modern industries, machine condition monitoring data have been available for improved maintenance. While big data generally benefits intelligent fault diagnosis performance, the significantly increased data amount inevitably poses high requirements for storage and computation. As a consequence, it is very difficult for the fault diagnosis model to be updated and applied efficiently. In order to...Show More

Intelligent Prototype Generation Method On Machine Fault Diagnosis Through Compressing Large Dataset

Yixiao Xu;Xiang Li;Yaguo Lei;Bin Yang;Naipeng Li

Year: 2024 | Conference Paper |

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

Bin Yang;Yaguo Lei;Xiang Li;Naipeng Li;Asoke K. Nandi

IEEE/CAA Journal of Automatica Sinica
Year: 2024 | Volume: 11, Issue: 4 | Journal Article |
Cited by: Papers (11)
The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source domain. However, in engineering scenarios, achieving such high-quality label annotation is difficult and expensive. The incorrect label annotation produces two negative effects: 1) the complex decision boundary of diagnosis models lowers the generalization performan...Show More

Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

Bin Yang;Yaguo Lei;Xiang Li;Naipeng Li;Asoke K. Nandi

Year: 2024 | Volume: 11, Issue: 4 | Journal Article |

An Optimal-Subdomain Generalization Method for Remaining Useful Life Prediction of Machinery Under Time-Varying Operation Conditions

Xiaofei Liu;Yaguo Lei;Naipeng Li;Xiang Li;Bin Yang

IEEE Transactions on Industrial Informatics
Year: 2024 | Volume: 20, Issue: 4 | Journal Article |
Cited by: Papers (6)
Machinery often operates under time-varying conditions, which can lead to distribution discrepancies in degradation samples. However, most existing domain generalization-based methods for predicting remaining useful life (RUL) are applied to constant operation conditions, and they may demonstrate performance deteriorations across alternating operation conditions. This article proposes an optimal-s...Show More

An Optimal-Subdomain Generalization Method for Remaining Useful Life Prediction of Machinery Under Time-Varying Operation Conditions

Xiaofei Liu;Yaguo Lei;Naipeng Li;Xiang Li;Bin Yang

Year: 2024 | Volume: 20, Issue: 4 | Journal Article |

Intelligent Machinery Fault Diagnosis With Event-Based Camera

Xiang Li;Shupeng Yu;Yaguo Lei;Naipeng Li;Bin Yang

IEEE Transactions on Industrial Informatics
Year: 2024 | Volume: 20, Issue: 1 | Journal Article |
Cited by: Papers (59)
Event-based cameras are the emerging bioinspired technology in vision sensing. Different from the traditional standard cameras, the event-based cameras asynchronously record the brightness change per pixel, and have the great merits of high temporal resolution, low energy consumption, high dynamic range, etc. While the event-based cameras have been initially exploited in several common vision-base...Show More

Intelligent Machinery Fault Diagnosis With Event-Based Camera

Xiang Li;Shupeng Yu;Yaguo Lei;Naipeng Li;Bin Yang

Year: 2024 | Volume: 20, Issue: 1 | Journal Article |

Health Prognostics of Multivariate Deteriorating Machinery Considering Unit-to-Unit Variability

Pengcheng Xu;Yaguo Lei;Naipeng Li;Xiang Li;Bin Yang

2023 Global Reliability and Prognostics and Health Management Conference (PHM-Hangzhou)
Year: 2023 | Conference Paper |
Multivariable deteriorating machinery (MDM) is commonly encountered in modern industrial environments, where its failure can be attributed to the gradual degradation of multiple health indicators (HIs). More and more researchers have directed their attention toward exploring the dependencies among these HIs and establishing multivariate degradation models. Nevertheless, existing methodologies have...Show More

Health Prognostics of Multivariate Deteriorating Machinery Considering Unit-to-Unit Variability

Pengcheng Xu;Yaguo Lei;Naipeng Li;Xiang Li;Bin Yang

Year: 2023 | Conference Paper |

Non-Contact Machine Vibration Sensing and Fault Diagnosis Method Based on Event Camera

Ruiyi Guang;Xiang Li;Yaguo Lei;Naipeng Li;Bin Yang

2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
Rotating machinery plays a key role in the field of industrial manufacturing and is an indispensable equipment in the production line. Many rotating machines work in extreme environments for a long time, which makes them more prone to various failures. Now it is popular to use the vibration signal collected by the accelerometer for fault diagnosis. However, accelerometers are contact sensors, and ...Show More

Non-Contact Machine Vibration Sensing and Fault Diagnosis Method Based on Event Camera

Ruiyi Guang;Xiang Li;Yaguo Lei;Naipeng Li;Bin Yang

Year: 2023 | Conference Paper |

Machinery Health Prognostics With Multimodel Fusion Degradation Modeling

Naipeng Li;Yaguo Lei;Xiaofei Liu;Tao Yan;Pengcheng Xu

IEEE Transactions on Industrial Electronics
Year: 2023 | Volume: 70, Issue: 11 | Journal Article |
Cited by: Papers (9)
Degradation modeling aims to formulate the health state degradation process of machinery. Commonly used degradation models pay more attention to describing the global increasing or decreasing trend without considering the local fluctuation in the degradation process. To deal with the above-mentioned issue, this article proposes a multimodel fusion degradation modeling method. The basic idea is to ...Show More

Machinery Health Prognostics With Multimodel Fusion Degradation Modeling

Naipeng Li;Yaguo Lei;Xiaofei Liu;Tao Yan;Pengcheng Xu

Year: 2023 | Volume: 70, Issue: 11 | Journal Article |

Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

Xiang Li;Yixiao Xu;Naipeng Li;Bin Yang;Yaguo Lei

IEEE/CAA Journal of Automatica Sinica
Year: 2023 | Volume: 10, Issue: 1 | Journal Article |
Cited by: Papers (82)
In recent years, intelligent data-driven prognostic methods have been successfully developed, and good machinery health assessment performance has been achieved through explorations of data from multiple sensors. However, existing data-fusion prognostic approaches generally rely on the data availability of all sensors, and are vulnerable to potential sensor malfunctions, which are likely to occur ...Show More

Remaining Useful Life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

Xiang Li;Yixiao Xu;Naipeng Li;Bin Yang;Yaguo Lei

Year: 2023 | Volume: 10, Issue: 1 | Journal Article |

A new FPCA-based remaining useful life prediction method for truncated degradation signals

Naipeng Li;Yaguo Lei;Mingyang Wang;Xiaofei Liu;Zongyao Liu

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2022 | Conference Paper |
Functional principal component analysis (FPCA) is a commonly used nonparametric degradation modeling technique. A basic requirement of FPCA is that different units must share the same scale in X-axis. A FPCA-based degradation modeling method has been specially designed for truncated degradation signals whose amplitudes are truncated at the failure threshold. This method formulates the degradation ...Show More

A new FPCA-based remaining useful life prediction method for truncated degradation signals

Naipeng Li;Yaguo Lei;Mingyang Wang;Xiaofei Liu;Zongyao Liu

Year: 2022 | Conference Paper |

Subdomain Adaptation Transfer Learning Network for Fault Diagnosis of Roller Bearings

Zhijian Wang;Xinxin He;Bin Yang;Naipeng Li

IEEE Transactions on Industrial Electronics
Year: 2022 | Volume: 69, Issue: 8 | Journal Article |
Cited by: Papers (155)
Due to the data distribution discrepancy, fault diagnosis models, trained with labeled data in one scene, likely fails in classifying by unlabeled data acquired from the other scenes. Transfer learning is capable to generalize successful application trained in one scene to the fault diagnosis in the other scenes. However, the existing transfer methods do not pay much attention to reduce adaptively...Show More

Subdomain Adaptation Transfer Learning Network for Fault Diagnosis of Roller Bearings

Zhijian Wang;Xinxin He;Bin Yang;Naipeng Li

Year: 2022 | Volume: 69, Issue: 8 | Journal Article |

Remaining Useful Life Prediction Based on Multi-channel Attention Bidirectional Long Short-term Memory Network

Wenting Wang;Biao Wang;Naipeng Li;Yaguo Lei;Tao Yan

2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
Recently, a large number of deep learning-based prognostics methods for machinery remaining useful life (RUL) have been proposed. And massive monitoring data is the basis of deep learning-based RUL prediction methods. However, most existing methods usually assume that the monitoring data acquired from different sensors contain similar degradation information, and they lack consideration on effecti...Show More

Remaining Useful Life Prediction Based on Multi-channel Attention Bidirectional Long Short-term Memory Network

Wenting Wang;Biao Wang;Naipeng Li;Yaguo Lei;Tao Yan

Year: 2021 | Conference Paper |

Multi-Sensor Data-Driven Remaining Useful Life Prediction of Semi-Observable Systems

Naipeng Li;Yaguo Lei;Nagi Gebraeel;Zhijian Wang;Xiao Cai;Pengcheng Xu;Biao Wang

IEEE Transactions on Industrial Electronics
Year: 2021 | Volume: 68, Issue: 11 | Journal Article |
Cited by: Papers (45)
Semi-observable systems are referred to as a kind of widely used industrial equipment whose physical degradation state is only observable via shutdown inspection. To monitor the degradation process of semi-observable systems online, different types of sensors are generally employed to collect monitoring signals. Lots of studies have been conducted to fuse multi-sensor signals to predict remaining ...Show More

Multi-Sensor Data-Driven Remaining Useful Life Prediction of Semi-Observable Systems

Naipeng Li;Yaguo Lei;Nagi Gebraeel;Zhijian Wang;Xiao Cai;Pengcheng Xu;Biao Wang

Year: 2021 | Volume: 68, Issue: 11 | Journal Article |

Multiscale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery

Biao Wang;Yaguo Lei;Naipeng Li;Wenting Wang

IEEE Transactions on Industrial Electronics
Year: 2021 | Volume: 68, Issue: 8 | Journal Article |
Cited by: Papers (174)
To integrate the complete degradation information of machinery, deep learning-based prognostics approaches usually use monitoring data acquired by different sensors as the inputs of networks. These approaches, however, lack an explicit learning mechanism to effectively identify the distinctions of different sensor data and highlight the important degradation information, thereby affecting the accu...Show More

Multiscale Convolutional Attention Network for Predicting Remaining Useful Life of Machinery

Biao Wang;Yaguo Lei;Naipeng Li;Wenting Wang

Year: 2021 | Volume: 68, Issue: 8 | Journal Article |

An Adaptable Cost-sensitive Imbalanced Fault Diagnosis Method Considering Sample-significance Information

Xinwei Jiang;Biao Wang;Yaguo Lei;Naipeng Li

2020 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
Imbalanced datasets acquired from machinery are common in real cases and some cost-sensitive learning-based methods have been presented to tackle the imbalanced fault diagnosis in recent years. However, these methods manually assign misclassification costs that remain unchanged during training for each health condition and therefore they cannot be applied generally in different scenarios. Moreover...Show More

An Adaptable Cost-sensitive Imbalanced Fault Diagnosis Method Considering Sample-significance Information

Xinwei Jiang;Biao Wang;Yaguo Lei;Naipeng Li

Year: 2020 | Conference Paper |

A Polynomial Kernel Induced Distance Metric to Improve Deep Transfer Learning for Fault Diagnosis of Machines

Bin Yang;Yaguo Lei;Feng Jia;Naipeng Li;Zhaojun Du

IEEE Transactions on Industrial Electronics
Year: 2020 | Volume: 67, Issue: 11 | Journal Article |
Cited by: Papers (182)
Deep transfer-learning-based diagnosis models are promising to apply diagnosis knowledge across related machines, but from which the collected data follow different distribution. To reduce the distribution discrepancy, Gaussian kernel induced maximum mean discrepancy (GK-MMD) is a widely used distance metric to impose constraints on the training of diagnosis models. However, the models using GK-MM...Show More

A Polynomial Kernel Induced Distance Metric to Improve Deep Transfer Learning for Fault Diagnosis of Machines

Bin Yang;Yaguo Lei;Feng Jia;Naipeng Li;Zhaojun Du

Year: 2020 | Volume: 67, Issue: 11 | Journal Article |

A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

Biao Wang;Yaguo Lei;Naipeng Li;Ningbo Li

IEEE Transactions on Reliability
Year: 2020 | Volume: 69, Issue: 1 | Journal Article |
Cited by: Papers (988)
Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely represented using relevance vector machine regressions...Show More

A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

Biao Wang;Yaguo Lei;Naipeng Li;Ningbo Li

Year: 2020 | Volume: 69, Issue: 1 | Journal Article |

Remaining Useful Life Prediction Based on Deep Residual Attention Network

Biao Wang;Tianyu Han;Yaguo Lei;Naipeng Li

2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)
Year: 2019 | Conference Paper |
Cited by: Papers (4)
Deep learning is gaining growing interests in the field of remaining useful life (RUL) prediction and has achieved state-of-the-art results. Current deep learning-based prognostics approaches, however, do not consider the distinctions of different sensor data during representation learning, which affects their prediction accuracy and limits their generalization. To overcome this weakness, a new de...Show More

Remaining Useful Life Prediction Based on Deep Residual Attention Network

Biao Wang;Tianyu Han;Yaguo Lei;Naipeng Li

Year: 2019 | Conference Paper |

A Hybrid Transfer Learning Method for Fault Diagnosis of Machinery under Variable Operating Conditions

Zhaojun Du;Bin Yang;Yaguo Lei;Xiwei Li;Naipeng Li

2019 Prognostics and System Health Management Conference (PHM-Qingdao)
Year: 2019 | Conference Paper |
Cited by: Papers (8)
Intelligent fault diagnosis has been a research hotspot in recent years. However, most of the works are conducted based on the hypothesis that training and testing data subject to the same distribution. In engineering scenarios, machines usually work under variable operation conditions, which results in the data from different conditions subject to distribution discrepancy. Since transfer learning...Show More

A Hybrid Transfer Learning Method for Fault Diagnosis of Machinery under Variable Operating Conditions

Zhaojun Du;Bin Yang;Yaguo Lei;Xiwei Li;Naipeng Li

Year: 2019 | Conference Paper |

Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data

Liang Guo;Yaguo Lei;Saibo Xing;Tao Yan;Naipeng Li

IEEE Transactions on Industrial Electronics
Year: 2019 | Volume: 66, Issue: 9 | Journal Article |
Cited by: Papers (896)
The success of intelligent fault diagnosis of machines relies on the following two conditions: 1) labeled data with fault information are available; and 2) the training and testing data are drawn from the same probability distribution. However, for some machines, it is difficult to obtain massive labeled data. Moreover, even though labeled data can be obtained from some machines, the intelligent f...Show More

Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data

Liang Guo;Yaguo Lei;Saibo Xing;Tao Yan;Naipeng Li

Year: 2019 | Volume: 66, Issue: 9 | Journal Article |

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