- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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IRT image segmentation and enhancement using FCM-MALO approach
摘要： Infrared Thermography (IRT) is a method that has modernized the way for monitoring the thermal conditions, finding some potential faults or defects that could be available in electrical systems. In the proposed work, IRT electrical images are taken for diagnosing the faults by the image pre-processing and segmentation process. Initially, the IRT images are changed over into a grayscale image, trailed by image pre-processing is performed where histogram equalization is applied. With the intention of segmenting the faulty portion (high temperature zone) from the electrical equipment, Fuzzy C Means (FCM) strategy is introduced. For optimizing the centroid of FCM algorithm Modified Ant Lion Optimization (MALO) is proposed. From the segmented images, small size portions are removed by using Region Props function. This operation can remove the isolated pixels from the image and extract image components for better representation of images. The optimum results show that the proposed work accomplishes maximum segmentation accuracy compared to existing segmentation algorithms.
关键词： Pre-processing,Infrared thermography images,Fault diagnosis,Segmentation,Region props function,Electrical equipment
更新于2025-09-23 15:23:52
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Automatic signal quality check and equipment condition surveillance based on trivalent logic diagnosis theory
摘要： In the field of fault diagnosis, inadequate signals measured for equipment condition monitoring may cause incorrect diagnostic results and reduce the accuracy and reliability of the equipment diagnosis system. This paper proposes a method of signal quality check and equipment condition surveillance based on trivalent logic theory, signal histogram analysis and principal component analysis (PCA), in order to automatically evaluate the quality of measured signals to ensure that the signals are real and valid for the condition diagnosis of equipment, and automatically judge the equipment state for condition surveillance. The novelty of this paper are summarized as: (1) Trivalent logic has been expanded appropriately into the trivalent logic diagnosis theory, so that it can be applied to verify the signal quality in the acquisition process for fault diagnosis and equipment condition surveillance; (2) In order to directly and effectively extract features of a signal following any probability density distribution, the histograms of the signal measured for equipment condition diagnosis is used to substitute time domain symptom parameters which have been generally used in equipment diagnosis technology; (3) PCA is used to integrate the histograms to realize signal quality check and equipment condition surveillance on the basis of the trivalent logic diagnosis theory. By the method proposed in this paper, the moment when the signal for equipment condition diagnosis is relatively stable can be found, and the unfavorable signal can be avoided for ensuring the accuracy and reliability of the equipment condition diagnosis. Simulation signals and real signals measured in various conditions from a blower are respectively used to verify the effectiveness of the proposed method.
关键词： Condition monitoring,Fault diagnosis,Measurement errors,Histograms,Vibration measurement
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) - Houston, TX (2018.5.14-2018.5.17)] 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) - Multi-core cable fault diagnosis using cluster time-frequency domain reflectometry
摘要： Guaranteeing the integrity and functionality of the control and instrumentation (C&I) cable system is essential in ensuring safe nuclear power plant (NPP) operation. When a fault occurs in a multi-core cable, it not only affects the signals of faulty lines but in fact, disturbs the rest as well due to crosstalk and noise interference. Therefore, this results in C&I signal errors in NPP operation and further leads to a rise in concern regarding the NPP operation. Thus, it is necessary for diagnostic technologies of multi-core C&I cables to classify the faulty line and detect the fault to assure the safety and reliability of NPP operation. We propose a diagnostic method that detects the fault location and faulty line in multi-core C&I cable using a clustering algorithm based on TFDR results. The faulty line detection clustering algorithm uses TFDR cross-correlation and phase synchrony results as input feature data altogether which can detect the faulty line and identify the fault point successfully. The proposed clustering algorithm is verified by experiments with two possible fault scenarios in NPP operation.
关键词： fault diagnosis,reflectometry,control and instrumentation cable,K-means clustering,crosstalk,time-frequency analysis
更新于2025-09-23 15:23:52
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A Novel Stereo Vision Measurement System Using Both Line Scan Camera and Frame Camera
摘要： In the product surface defect detection system, the detection accuracy can be greatly improved if the surface grayscale image and depth image can be adopted simultaneously. Existing 3-D cameras have the problem in precise registration of 2-D images and 3-D data, which results in inflexibility and inevitable error in practical vision tasks. Hence, we propose a novel stereo vision measurement system which is able to simultaneously acquire registered scan grayscale images and depth images without special data registration algorithm as well as the calibration method. The proposed measurement system mainly consists of a line laser system, a line scan camera, and a frame camera. The line scan camera captures clear images of objects illuminated by the line laser. In addition, the line scan camera and the frame camera form a stereo vision sensor. In this case, the line laser stripe provides location feature of the matching point for frame camera. The epipolar constraint implements the matching process between the line scan camera and the frame camera. Finally, the stereo vision measurement model is utilized to realize the 3-D coordinate reconstruction, and the z-axis coordinate is used to generate depth image. The proposed system can simultaneously obtain the image and depth information corresponding to each pixel by means of a push sweep. In the experiment, the measurement system can achieve the accuracy of 0.13 mm within a measurement range of 500 mm × 300 mm × 200 mm.
关键词： Fault diagnosis,machine vision,visual system,measurement,laser measurement applications
更新于2025-09-23 15:22:29
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[IEEE 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Rome, Italy (2019.6.17-2019.6.20)] 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - System Level Analysis of Millimetre-wave GaN-based MIMO Radar for Detection of Micro Unmanned Aerial Vehicles
摘要： A particle filter (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),particle filter (PF),remotely operated underwater vehicle (ROV),underwater navigation
更新于2025-09-23 15:21:01
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A Photovoltaic Array Fault Diagnosis Method Considering the Photovoltaic Output Deviation Characteristics
摘要： There are a large number of photovoltaic (PV) arrays in large-scale PV power plants or regional distributed PV power plants, and the output of different arrays fluctuates with the external conditions. The deviation and evolution information of the array output are easily covered by the random fluctuations of the PV output, which makes the fault diagnosis of PV arrays difficult. In this paper, a fault diagnosis method based on the deviation characteristics of the PV array output is proposed. Based on the current of the PV array on the DC (direct current) side, the deviation characteristics of the PV array output under different arrays and time series are analyzed. Then, the deviation function is constructed to evaluate the output deviation of the PV array. Finally, the fault diagnosis of a PV array is realized by using the probabilistic neural network (PNN), and the effectiveness of the proposed method is verified. The main contributions of this paper are to propose the deviation function that can extract the fault characteristics of PV array and the fault diagnosis method just using the array current which can be easily applied in the PV plant.
关键词： photovoltaic array,deviation characteristics,fault diagnosis,probabilistic neural network
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE 58th Conference on Decision and Control (CDC) - Nice, France (2019.12.11-2019.12.13)] 2019 IEEE 58th Conference on Decision and Control (CDC) - Laser Sintering Control for Metal Additive Manufacturing by PDE Backstepping
摘要： The dynamic response of the pitch angle slows down when the pitch actuator of wind turbines fails, and the fault leads to ?uctuations in the generator speed and in the output power. According to identi?cation and analytical theories, a fault-tolerant control combined with fault estimation is proposed to solve this problem. The simpli?ed two-order transform function of the pitch actuator is transformed into the identi?cation equation by means of the Euler transformation method. Next, the time-varying natural frequency and the damping ratio of pitch actuators are regarded as gradual change and abrupt change parameters; these two parameters are estimated using the proposed sliding data window least squares-based iterative (SDW-LSI) identi?cation algorithm. The sliding data window length is adjusted when changes in the system parameters are detected. Then, according to analytical theory, a compensation equation is derived in the pitch actuator, and the estimated values are fed back to the compensation module to adjust the relations before and after fault occurrences in the pitch actuator to eliminate the effects caused by the pitch actuator failure. Finally, the feasibility of the SDW-LSI algorithm is validated by choosing the fault of high air content in the hydraulic oil.
关键词： Fault diagnosis,system identi?cation,wind power generation,fault tolerant,hydraulic actuators
更新于2025-09-23 15:19:57
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[IEEE 2019 24th Microoptics Conference (MOC) - Toyama, Japan (2019.11.17-2019.11.20)] 2019 24th Microoptics Conference (MOC) - Optimized LED-based Optical Wireless Power Transmission System Configuration for Compact IoT
摘要： The dynamic response of the pitch angle slows down when the pitch actuator of wind turbines fails, and the fault leads to ?uctuations in the generator speed and in the output power. According to identi?cation and analytical theories, a fault-tolerant control combined with fault estimation is proposed to solve this problem. The simpli?ed two-order transform function of the pitch actuator is transformed into the identi?cation equation by means of the Euler transformation method. Next, the time-varying natural frequency and the damping ratio of pitch actuators are regarded as gradual change and abrupt change parameters; these two parameters are estimated using the proposed sliding data window least squares-based iterative (SDW-LSI) identi?cation algorithm. The sliding data window length is adjusted when changes in the system parameters are detected. Then, according to analytical theory, a compensation equation is derived in the pitch actuator, and the estimated values are fed back to the compensation module to adjust the relations before and after fault occurrences in the pitch actuator to eliminate the effects caused by the pitch actuator failure. Finally, the feasibility of the SDW-LSI algorithm is validated by choosing the fault of high air content in the hydraulic oil.
关键词： Fault diagnosis,system identi?cation,wind power generation,fault tolerant,hydraulic actuators
更新于2025-09-23 15:19:57
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[IEEE 2019 International Conference on Power Electronics, Control and Automation (ICPECA) - New Delhi, India (2019.11.16-2019.11.17)] 2019 International Conference on Power Electronics, Control and Automation (ICPECA) - Fault Identification Algorithm for Grid Connected Photovoltaic Systems using Machine Learning Techniques
摘要： The motivation and background behind the fault detection for grid connected solar power plant is presented in this paper. The major issues encountered when integrating a PV system to the grid include multi-peak phenomenon due to partial shading, regulation of circulating currents, the impact of grid impedances on PV system stability, Fault Ride-Through (FRT) Capability, and anti-islanding detection. Hence, fault detection and condition monitoring system are necessary for smooth operation. In this paper, a fault classification technique for single-phase grid connected PV systems is developed. Wavelet Transform and Neural network approaches are used for developing the fault classification algorithm. The results depicted that the developed fault detection algorithm shows a significant improvement in the classification accuracy with 98.4%.
关键词： Wavelet Transform,fault classification,fault diagnosis,Neural Network,Photovoltaic System
更新于2025-09-23 15:19:57
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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) - Minimum material quality threshold for intermediate band solar cells using a multi-band device simulator with fully coupled optics
摘要： One of the common ways to perform data-driven fault diagnosis is to employ statistical models, which can classify the data into nominal (healthy) and a fault class or distinguish among different fault classes. The former is termed fault (anomaly) detection, and the latter is termed fault isolation (classi?cation, diagnosis). Traditionally, statistical classi?ers are trained using data from faulty and nominal behaviors in a batch mode. However, it is dif?cult to anticipate, a priori, all the possible ways in which failures can occur, especially when a new vehicle model is introduced. Therefore, it is imperative that diagnostic algorithms adapt to new cases on an ongoing basis. In this paper, a uni?ed methodology to incrementally learn new information from evolving databases is presented. The performance of adaptive (or incremental learning) classi?cation techniques is discussed when: 1) the new data has the same fault classes and same features and 2) the new data has new fault classes, but with the same set of observed features. The proposed methodology is demonstrated on data sets derived from an automotive electronic throttle control subsystem.
关键词： fault diagnosis,automotive systems,incremental classi?ers,Adaptive learning,ensemble systems
更新于2025-09-23 15:19:57