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Simulation Data-Driven Semi-Supervised Fault Diagnosis Method for Rolling Bearings
2024 Global Reliability and Prognostics and Health Management Conference (PHM-Beijing)
Year: 2024 | Conference Paper |
In addressing the challenges of acquiring substantial and effective rolling bearing fault data in industrial environments, as well as the limited availability of labeled samples which hinders the generalization performance of intelligent diagnostic models, this paper proposes a semi-supervised fault diagnosis method driven by simulation data. Firstly, a fault-bearing simulation model under non-sta...Show More
The Multifactor Aging Characteristics and Aging State Assessment of Epoxy Composites for GIS
Shuang Wang;Yu Chen;Feng Jia;Bin Guo;Jiale Mao;Yonghong Cheng;Zengbin Wang;Ruilei Gong
Epoxy and its composites are widely used as raw materials for the solid insulations in gas-insulated switchgear (GIS) worldwide due to their excellent dielectric properties, thermal stability, corrosion resistance, and ease of processing. These insulation materials show aging phenomena under multiple stresses of electrical, thermal, and vibration during GIS operation, which reduces the electrical ...Show More
Speed Synchronization Control of Paver Traveling Motor Based on Optimized Fuzzy Adaptive PID
2023 IEEE International Conference on Mechatronics and Automation (ICMA)
Year: 2023 | Conference Paper |
The normal running and operation of pavers is affected by the motor speed synchronization control system. When the motor is subjected to mutation interference, the speed will fluctuate, which will cause the movement trajectory to deviate and reduce the working quality. To realize speed synchronization of traveling motor and the normal operation of pavers, a fuzzy adaptive PID co-simulation method ...Show More
Vertical Deformation Optimization for the Screed of Pavers Based on ABAQUS and Isight
2023 IEEE International Conference on Mechatronics and Automation (ICMA)
Year: 2023 | Conference Paper |
During the paving process, the posture of the screed is the main factor determining the evenness of the road surface. As the bottom of the screed is in direct contact with the high-temperature asphalt mixture at work, the bottom plate is heated and expands. This results in vertical deformation of the screed with upward heat deformation characteristics at both ends, thereby reducing the quality of ...Show More
Source-free domain adaptation network for rolling bearing fault diagnosis
2023 IEEE International Conference on Mechatronics and Automation (ICMA)
Year: 2023 | Conference Paper |
The current purpose of cross domain rolling bearing fault diagnosis is to transfer rich diagnosis features from marked source to unmarked target domain. Most domain adaptation methods require simultaneous access to both source domain data and target domain data. However, the following two shortcomings limit the development of cross-domain bearing fault diagnosis: (1) Source dataset takes up so muc...Show More
Few-shot fault diagnosis method for rolling bearing using local descriptors
Langfei An;Feng Jia;Bo Wang;Jingru Hou;Jianjun Shen;Xuding Song
2022 IEEE International Conference on Mechatronics and Automation (ICMA)
Year: 2022 | Conference Paper |
In the existing rolling bearing fault diagnosis methods based on few-shot learning, the focus is on the global information with high discrimination, and ignore the local information with low discrimination leads to its low diagnosis accuracy. To solve the above problem, we propose a few-shot fault diagnosis method for rolling bearing using local descriptors. Specifically, in the feature extraction...Show More
Temperature Monitoring for 500 kV Oil-Filled Submarine Cable Based on BOTDA Distributed Optical Fiber Sensing Technology: Method and Application
Yu Chen;Shuang Wang;Yi Hao;Kai Yao;Hanzhi Li;Feng Jia;Dongli Yue;Qingyun Shi;Yonghong Cheng;Xiaowei Huang
The 500 kV oil-filled ac submarine cables in the networking project of China’s southern coast are large capacity, ultrahigh-voltage cross-sea submarine power cables, which are 31 km long and bundled with submarine optical cables. Temperature monitoring on submarine cables is of great significance in controlling the ampacity and ensuring operational reliability. Referring to the submarine cables’ s...Show More
Variable Voltage Variable Frequency Modular Multilevel AC/AC Converter With High-Frequency Harmonics Filtering Capability
Yongqing Meng;Jialu Sun;Ziyue Duan;Feng Jia;Xiuli Wang;Xifan Wang
This article proposes a variable voltage variable frequency modular multilevel ac/ac converter (VVVF-MMC) with a high-frequency isolation link, which can realize voltage and frequency conversion simultaneously with insulated-gate bipolar transistors (IGBTs) of the same rated voltage and current level. The parallel connection of multiple modules is realized by coupling reactors, which can realize l...Show More
Damage Analysis of Suspended Submarine Cable under Current Force Based on Finite Element Simulation
Jingwen Zhang;Yu Chen;Feng Jia;Yifang Zhou;Hanzhi Li;Changxi Li;Xiaowei Huang;Qiang Guo
2021 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
In this paper, 500kV AC submarine cable was taken as the object, the finite element software COMSOL was used to establish the simulation model of fluid-structure coupling, and the force of the suspended submarine cable was analyzed. The stress distribution of the lead alloy sheath of the cable in four specific directions and the location of the maximum stress value was obtained. On this basis, the...Show More
The Open Set Weighted Domain Adaptation Method for Fault Diagnosis of Rolling Bearings
2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing)
Year: 2021 | Conference Paper |
The transfer fault diagnosis method can identify the fault of rolling bearing dataset collected under different working conditions or different equipment, which realized the cross-dataset application of fault knowledge. However, the existing transfer fault diagnosis methods all assume that the labeled training dataset and unlabeled testing dataset have the same fault label set. The above assumptio...Show More
Distribution-Invariant Deep Belief Network for Intelligent Fault Diagnosis of Machines Under New Working Conditions
As a deep learning model, a deep belief network (DBN) consists of multiple restricted Boltzmann machines (RBMs). Based on DBN, many intelligent fault diagnosis methods are proposed. However, these methods seldom considered the appearance of new working conditions during the operation of real machines. Varying working conditions lead to a change of feature distributions and finally result in low di...Show More
Comprehensive Analysis of Temperature and Stress Distribution in Optical Fiber Composite Low Voltage Cable Using Finite Element Method
Ahsan Ashfaq;Yu Chen;Kai Yao;Feng Jia;Jing Yu;Yonghong Cheng
Optical fiber composite low voltage cable (OPLC) is an optimized way of carrying out the function of supplying electrical power and communication signals in a single cable. In this paper, the temperature and stress distribution in OPLC cable is analyzed by using the finite element method as the current increases to maximum capacity. The increase of temperature and stress are the two main factors t...Show More
The 500kV Oil-filled Submarine Cable Temperature Monitoring System Based on BOTDA Distributed Optical Fiber Sensing Technology
Yu Chen;Shuang Wang;Yi Hao;Kai Yao;Hanzhi Li;Feng Jia;Qingyun Shi;Dongli Yue;Yonghong Cheng
2020 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)
Year: 2020 | Conference Paper |
Cited by: Papers (8)
The 500 kV oil-filled AC submarine cable in Hainan Networking System is the first large capacity, ultra-high voltage cross-sea submarine power cable in China, which is 31 kilometers long and bundled with a submarine optical cable. And temperature monitoring on the submarine cable is of great significance to control the current rating and ensure the operation reliability. Based on the structural ch...Show More
Imbalanced classification using deep learning has attracted much attention in intelligent fault diagnosis of machinery. However, the existing methods use individual deep neural network to extract features and recognize the health conditions under imbalanced dataset, which may easily over-fit the mechanical data and affect the diagnosis accuracy. To deal with this problem, this paper takes the adva...Show More
Deep learning has been applied in intelligent fault diagnosis of machines since it trains deep neural networks to simultaneously learn features and recognize faults. In the intelligent fault diagnosis methods based on deep learning, feature learning and fault recognition are achieved by solving a multi-class classification problem. The multi-class classification, however, has not considered the re...Show More
A Polynomial Kernel Induced Distance Metric to Improve Deep Transfer Learning for Fault Diagnosis of Machines
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
Small Signal Stability Analysis and Control Optimization of Y-connected Modular Multilevel Converter for Fractional Frequency Offshore Wind Power Application
Feng Jia;Yongqing Meng;Jianyang Yu;Shuhao Yan;Rong Ye;Yuchen Tang
2019 IEEE 8th International Conference on Advanced Power System Automation and Protection (APAP)
Year: 2019 | Conference Paper |
Cited by: Papers (1)
Y-connected modular multilevel converter(Y-MMC) is a new circuit topology of direct AC/AC power conversion, with competitive application prospects in fractional frequency offshore wind power system. Different from the previous converter topology, Y-MMC realizes the frequency conversion by single-phase voltage cancellation. Simultaneously, the voltages between main bridges and auxiliary bridges can...Show More
A Transfer Learning Method for Intelligent Fault Diagnosis from Laboratory Machines to Real-Case Machines
2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC)
Year: 2018 | Conference Paper |
Cited by: Papers (16)
It is difficult to train a reliable intelligent fault diagnosis model for machines used in real cases (MURC) because there are not sufficient labeled data. However, we can easily simulate various faults in a laboratory, and the data from machines used in the laboratory (MUL) contain fault knowledge related to the data from MURC. Thus, it is possible to identify the health states of MURC by using r...Show More
Intelligent fault diagnosis of rotating machinery using locally connected restricted boltzmann machine in big data era
2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)
Year: 2017 | Conference Paper |
Cited by: Papers (5)
In intelligent fault diagnosis, unsupervised feature learning is a potential tool to replace the manual feature extraction in big data era. Therefore, we first develop a locally connected restricted Boltzmann machine (LCRBM) from the traditional RBM in order to handle the periodic appearance of fault characteristics in the raw signals of rotating machinery. Then, using LCRBM, we propose a method f...Show More
A dirty data recognition method for machinery condition monitoring in big data era
IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society
Year: 2017 | Conference Paper |
Cited by: Papers (4)
Condition monitoring of machinery has entered the big data era, while the existence of dirty data reduces the quality of the whole data. In order to recognize the dirty data included in machinery monitoring data, a new method is proposed in this paper. First, a feature named sampled power index (SPI) is designed to transform the dirty data recognition issue into the outlier recognition. Then the w...Show More
Reconstruction independent component analysis-based methods for intelligent fault diagnosis
2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD)
Year: 2016 | Conference Paper |
Cited by: Papers (3)
Based on machine learning techniques, this paper presents a novel intelligent fault diagnosis method, which is an integrated framework concerning reconstruction independent component analysis (RICA) and multiclass relevance vector machine (MRVM). In this method, the RICA is first used to automatically extract features from raw vibration signals. Then, the learned features are used as the input dat...Show More
A method of automatic feature extraction from massive vibration signals of machines
2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings
Year: 2016 | Conference Paper |
Cited by: Papers (4)
In the studies of intelligent fault diagnosis of machines, lots of effort goes into designing effective feature extraction algorithms. Such processes would consume plenty of human labor, especially when dealing with massive vibration signals. So it is interesting to automatically extract features using machine learning techniques, instead of manually extracting them. To deal with the problem, this...Show More
An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data
Intelligent fault diagnosis is a promising tool to deal with mechanical big data due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In traditional intelligent diagnosis methods, however, the features are manually extracted depending on prior knowledge and diagnostic expertise. Such processes take advantage of human ingenuity but are...Show More
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