- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) - Macao, Macao (2019.12.1-2019.12.4)] 2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC) - Photovoltaic System Performance Model for Output Power Forecasting
摘要： This paper addresses the application of rotor speed signal for the detection and diagnosis of ball bearing faults in rotating electrical machines. Many existing techniques for bearing fault diagnosis (BFD) rely on vibration signals or current signals. However, vibration- or current-based BFD techniques suffer from various challenges that must be addressed. As an alternative, this paper takes the initial step of investigating the efficiency of rotor speed monitoring for BFD. The bearing failure modes are reviewed and their effects on the rotor speed signal are described. Based on this analysis, a novel BFD technique, the rotor speed-based BFD (RSB-BFD) method under variable speed and constant load conditions, is proposed to provide a benefit in terms of cost and simplicity. The proposed RSB-BFD method exploits the absolute value-based principal component analysis (PCA), which improves the performance of classical PCA by using the absolute value of weights and the sum square error. The performance and effectiveness of the RSB-BFD method is demonstrated using an experimental setup with a set of realistic bearing faults in the outer race, inner race, and balls.
关键词： principal component analysis (PCA),Bearing fault diagnosis (BFD),sum square error,variable speed,rotor speed
更新于2025-09-23 15:19:57
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Improved electro-optic chaotic system with nonlinear electrical coupling
摘要： Design disjunction is developed to offer a broad coverage, high resolution, and low overhead approach to online diagnosis and recovery of recon?gurable fabrics. Design disjunction leverages the condensed diagnosability of T logic resources to achieve self-recovery using partial recon?guration in O(log T ) steps. Recon?guration is guided by the constructive property of f-disjunctness which forms O(log T ) resource groups at design-time. Resolution of f simultaneous resource faults is shown to be guaranteed when the resource groups are mutually f-disjunct. This extends run-time fault resilience to a large resource space with certainty for up to f faults using a decision-free resolution process that also provides a high likelihood of identifying the fault’s location to a ?ne granularity. Finally, design disjunction is parameterized to accommodate the low coverage issue of functional testing for which inarticulate tests can otherwise impair fault isolation. Experimental results for MCNC and ISCAS benchmarks on a Xilinx 7-series ?eld programmable gate array (FPGA) demonstrate f-diagnosability at the individual slice level with a minimum average isolation accuracy of 96:4 percent (94:4 percent) for f ? 1 (f ? 2). Results have also demonstrated millisecond order recovery with a minimum increase of 83:6 percent in fault coverage compared to N-modular redundancy (NMR) schemes. Recovery is achieved while incurring an average critical path delay impact of only 1:49 percent and energy cost roughly comparable to conventional two-MR approaches.
关键词： online test,run-time fault diagnosis and recovery,design space exploration,Recon?gurable logic devices,autonomous fault handling,fault-tolerant systems,?eld programmable gate arrays
更新于2025-09-19 17:13:59
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Air-Filled Substrate Integrated Waveguide Leaky-Wave Antenna with Wideband and Fixed-Beam Characteristics
摘要： An output line-to-line voltage model-based fault diagnostic technique is presented in this paper. From the theoretical analysis of the output voltage under normal and faulty conditions, a preprocessing method is developed to extract fault features from diagnosis eigenvalue. A voltage envelope line is generated by the proposed voltage envelope function. By comparing the preprocessed diagnosis eigenvalue and the voltage envelope, single-switch open-circuit faults can be located precisely. Because the proposed method does not rely on the accurate amplitude of the output line-to-line voltage, the influence of the load changing is minimized and simple hardware is adopted, which has advantages of low cost, high reliability, and short diagnosis time. Moreover, as long as the inverter output voltages have the feature of periodic nonpositive and nonnegative, this method is valid no matter what control strategy is adopted by the inverter and no control signal is required for the diagnosis process. The prototype system is tested to validate the adaptability of the proposed method under different conditions, such as the diverse loads, various control strategies, and fault-occurrence time.
关键词： Fault diagnosis,fault location,open-circuit fault,inverters
更新于2025-09-19 17:13:59
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[IEEE 2019 Photonics North (PN) - Quebec City, QC, Canada (2019.5.21-2019.5.23)] 2019 Photonics North (PN) - Continuous-wave Nd:YVO <sub/>4</sub> laser with conical refraction output
摘要： A particle ?lter (PF)-based robust navigation with fault diagnosis (FD) is designed for an underwater robot, where 10 failure modes of sensors and thrusters are considered. The nominal underwater robot and its anomaly are described by a switching-mode hidden Markov model. By extensively running a PF on the model, the FD and robust navigation are achieved. Closed-loop full-scale experimental results show that the proposed method is robust, can diagnose faults effectively, and can provide good state estimation even in cases where multiple faults occur. Comparing with other methods, the proposed method can diagnose all faults within a single structure, diagnose simultaneous faults, and it is easily implemented.
关键词： fault tolerance,Fault diagnosis (FD),remotely operated underwater vehicle (ROV),switch-mode hidden Markov model (HMM),particle ?lter (PF),underwater navigation
更新于2025-09-19 17:13:59
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[IEEE 2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC) - Nagercoil, India (2019.3.7-2019.3.8)] 2019 International Conference on Recent Advances in Energy-efficient Computing and Communication (ICRAECC) - Free Space Optical Communication and Laser Beam Propagation through Turbulent Atmosphere: A Brief Survey
摘要： A particle ?lter (PF)-based robust navigation with fault diagnosis (FD) is designed for an underwater robot, where 10 failure modes of sensors and thrusters are considered. The nominal underwater robot and its anomaly are described by a switching-mode hidden Markov model. By extensively running a PF on the model, the FD and robust navigation are achieved. Closed-loop full-scale experimental results show that the proposed method is robust, can diagnose faults effectively, and can provide good state estimation even in cases where multiple faults occur. Comparing with other methods, the proposed method can diagnose all faults within a single structure, diagnose simultaneous faults, and it is easily implemented.
关键词： fault tolerance,Fault diagnosis (FD),switch-mode hidden Markov model (HMM),remotely operated underwater vehicle (ROV),particle ?lter (PF),underwater navigation
更新于2025-09-19 17:13:59
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Multivariate feature extraction based supervised machine learning for fault detection and diagnosis in photovoltaic systems
摘要： Fault detection and diagnosis (FDD) in the photovoltaic (PV) array has become a challenge due to the magnitudes of the faults, the presence of maximum power point trackers, non-linear PV characteristics, and the dependence on isolation efficiency. Thus, the aim of this paper is to develop an improved FDD technique of PV systems faults. The common FDD technique generally has two main steps: feature extraction and selection, and fault classification. Multivariate feature extraction and selection is very important for multivariate statistical systems monitoring. It can reduce the dimension of modeling data and improve the monitoring accuracy. Therefore, in the proposed FDD approach, the principal component analysis (PCA) technique is used for extracting and selecting the most relevant multivariate features and the supervised machine learning (SML) classifiers are applied for faults diagnosis. The FDD performance is established via different metrics using data extracted from different operating conditions of the grid-connected photovoltaic (GCPV) system. The obtained results confirm the feasibility and effectiveness of the proposed approaches for fault detection and diagnosis.
关键词： fault classification,fault diagnosis,photovoltaic (PV) systems,feature extraction,Supervised machine learning (SML),principal component analysis (PCA)
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Sozopol, Bulgaria (2019.9.6-2019.9.8)] 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers (CAOL) - Development and validation of measurement techniques according to ISO/IEC 17025:2017
摘要： Regenerative braking is one of the most promising and environmentally friendly technologies used in electric and hybrid electric vehicles to improve energy efficiency and vehicle stability. This paper presents a systematic data-driven process for detecting and diagnosing faults in the regenerative braking system of hybrid electric vehicles. The diagnostic process involves signal processing and statistical techniques for feature extraction, data reduction for implementation in memory-constrained electronic control units, and variety of fault classification methodologies to isolate faults in the regenerative braking system. The results demonstrate that highly accurate fault diagnosis is possible with the classification methodologies. The process can be employed for fault analysis in a wide variety of systems, ranging from automobiles to buildings to aerospace systems.
关键词： inference,regenerative braking system,Automotive systems,fault classification,distance measure,multiple fault diagnosis
更新于2025-09-19 17:13:59
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[IEEE 2019 28th Wireless and Optical Communications Conference (WOCC) - Beijing, China (2019.5.9-2019.5.10)] 2019 28th Wireless and Optical Communications Conference (WOCC) - A Modified DAG-SVM Algorithm for the Fault Diagnosis in Satellite Communication System
摘要： With the continuous development of satellite industry, online monitoring and fault diagnosis for satellite communication system becomes more important. Due to the difficulty in obtaining sufficient features of communication system, conventional multi-classification algorithm Directed Acyclic Graph Support Vector Machine (DAG-SVM) has low diagnostic efficiency and poor coupling diagnosis performance. On the other hand, it has been proved that extending the feature space can effectively improve the classification performance. Therefore, this paper proposed a modified multi-classification algorithm called Feature-Extended Directed Acyclic Graph Least Square Twin Support Vector Machine (FEDAG-LSTSVM). The new algorithm combined all features and their random combinations to establish coupling and redundancy for every feature, and then constructed the Separable Metric (SM) as classification measurement to arrange the structure sequencing of DAG-LSTSVM. To verify the utility of the algorithm, the satellite communication system were taken as experimental data. Preliminary simulation results demonstrate that the proposed algorithm improves the fault diagnosis accuracy to 89.69% but with 54.20% less computational time in 10-fold cross-validation compared with DAG-SVM, which means it can be well applied to diagnose fault for satellites communication system.
关键词： Directed Acyclic Graph Support Vector Machine,Fault diagnosis,Feature extension,Multi-Classification
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 10th International Symposium on Power Electronics for Distributed Generation Systems (PEDG) - Xi'an, China (2019.6.3-2019.6.6)] 2019 IEEE 10th International Symposium on Power Electronics for Distributed Generation Systems (PEDG) - A Short-term Photovoltaic Output Prediction Method Based on Improved PSO-RVM Algorithm
摘要： The memristor was first theorized as an electrical element, which provided the missing link between the charge and the flux. Due to the advantages of nano-scale size, multiple interconnected memristors have demonstrated unique overall characteristics, which are ideal for the utilization in neuromorphic systems. However, compared with the individual memristor circuit, a little work is explored about the overall behavior of the multiple memristive systems. In particular, the lack of a fault diagnosis approach for composite memristive network structures makes all the corresponding applications unstable and shaky. In this paper, the extraordinary properties of multiple memristor circuits are further investigated with comprehensive formula derivation and scientific computer simulations. Furthermore, a special feedback-control doublet generator is designed for implementing the fuzzy-based parametric fault diagnosis of multiple memristor circuits, which offers huge benefits in terms of accuracy and time consumption. Finally, the entire scheme is validated by an illustrative example.
关键词： Multiple memristor circuits,parametric fault diagnosis,feedback-control,doublet generator,fuzzy-based
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Impact of Transportation on Indian Roads, on PV Modules
摘要： This paper addresses the application of rotor speed signal for the detection and diagnosis of ball bearing faults in rotating electrical machines. Many existing techniques for bearing fault diagnosis (BFD) rely on vibration signals or current signals. However, vibration- or current-based BFD techniques suffer from various challenges that must be addressed. As an alternative, this paper takes the initial step of investigating the efficiency of rotor speed monitoring for BFD. The bearing failure modes are reviewed and their effects on the rotor speed signal are described. Based on this analysis, a novel BFD technique, the rotor speed-based BFD (RSB-BFD) method under variable speed and constant load conditions, is proposed to provide a benefit in terms of cost and simplicity. The proposed RSB-BFD method exploits the absolute value-based principal component analysis (PCA), which improves the performance of classical PCA by using the absolute value of weights and the sum square error. The performance and effectiveness of the RSB-BFD method is demonstrated using an experimental setup with a set of realistic bearing faults in the outer race, inner race, and balls.
关键词： principal component analysis (PCA),rotor speed,sum square error,Bearing fault diagnosis (BFD),variable speed
更新于2025-09-19 17:13:59