- 标题
- 摘要
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- 实验方案
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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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Condition Monitoring of Wind Turbine Blades Using Active and Passive Thermography
摘要： The failure of wind turbine blades is a major concern in the wind power industry due to the resulting high cost. It is, therefore, crucial to develop methods to monitor the integrity of wind turbine blades. Different methods are available to detect subsurface damage but most require close proximity between the sensor and the blade. Thermography, as a non-contact method, may avoid this problem. Both passive and active pulsed and step heating and cooling thermography techniques were investigated for different purposes. A section of a severely damaged blade and a small “plate” cut from the undamaged laminate section of the blade with holes of varying diameter and depth drilled from the rear to provide “known” defects were monitored. The raw thermal images captured by both active and passive thermography demonstrated that image processing was required to improve the quality of the thermal data. Different image processing algorithms were used to increase the thermal contrasts of subsurface defects in thermal images obtained by active thermography. A method called “Step Phase and Amplitude Thermography”, which applies a transform-based algorithm to step heating and cooling data was used. This method was also applied, for the ?rst time, to the passive thermography results. The outcomes of the image processing on both active and passive thermography indicated that the techniques employed could considerably increase the quality of the images and the visibility of internal defects. The signal-to-noise ratio of raw and processed images was calculated to quantitatively show that image processing methods considerably improve the ratios.
关键词： wind turbine blades,defects,thermography,image processing,condition monitoring
更新于2025-09-19 17:15:36
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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) - Nonlinear Wave Dynamics of Unstable Atherosclerotic Plaque
摘要： Fault diagnosis of inductions motors has received much attention recently. Most of the works use data obtained either from the time domain or by applying advanced techniques in the frequency domain. Some researchers have employed a considerable effort in designing sophisticated algorithms to achieve the best performance of the diagnosis system. However, some contributions in the field have not taken advantage of the benefits that a good evaluation stage can bring to the developing of classifiers for fault diagnosis. In this paper, novel insights for the classifier evaluation are presented to promote better assessment practices in the field of electric machine diagnosis based on supervised classification. A case of study consisting of a motor with a broken rotor bar is described to analyze the performance of two classifiers by using scores focused on the fault detection. Also, different error estimation methods are considered to obtain unbiased predictive performances. Two statistical tests are also discussed to confirm the significance of the results under a single data set.
关键词： fault diagnosis,Broken rotor bar,electric machines,classification algorithm,condition monitoring,performance evaluation
更新于2025-09-19 17:13:59
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Irreversible Photobleaching of BAC-Si in Bi/Er Co-Doped Optical Fiber under 830 nm Pumping
摘要： A novel empirical data analysis methodology based on the random matrix theory (RMT) and time series analysis is proposed for the power systems. Among the ongoing research studies of big data in the power system applications, there is a strong necessity for new mathematical tools that describe and analyze big data. This paper used RMT to model the empirical data which also treated as a time series. The proposed method extends traditional RMT for applications in a non-Gaussian distribution environment. Three case studies, i.e., power equipment condition monitoring, voltage stability analysis and low-frequency oscillation detection, illustrate the potential application value of our proposed method for multi-source heterogeneous data analysis, sensitive spot awareness and fast signal detection under an unknown noise pattern. The results showed that the empirical data from a power system modeled following RMT and in a time series have high sensitivity to dynamically characterized system states as well as observability and efficiency in system analysis compared with conventional equation-based methods.
关键词： low frequency oscillation,non-Gaussian,static voltage stability,Random matrix theory,time series analysis,data mining,condition monitoring
更新于2025-09-19 17:13:59
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Accelerator for Ultrafast Laser Serial Production
摘要： The world of serial applications has long been denied the possibility of ablation processes with ultrafast pulsed laser radiation due to high investment costs and low ablation rates. Nowadays, process scaling with advanced beam shaping technologies make use of the available high laser powers and therefore opens up possibilities for serial processing of individual components. But the exclusive focus on the main time of laser processing is not enough for a production system. Only the combined operation of handling systems, measurement technology, and process automation allow the ultrafast laser technology to spread further into volume markets.
关键词： Production Data Acquisition,Process Automation,Ultrafast Laser Machining,Beam Shaping,Condition Monitoring
更新于2025-09-16 10:30:52
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Novel methodology for detecting non-ideal operating conditions for grid-connected photovoltaic plants using Internet of Things architecture
摘要： The use of photovoltaic solar power generation is rising as worldwide energy demand increases. Therefore, reliability, safety, life cycle, and improved efficiency of photovoltaic plants have all become a major concern in research nowadays. In this context, monitoring systems are necessary to guarantee the required operating productivity and to avoid overpriced maintenance costs. This paper studies the non-ideal operating conditions for grid-connected photovoltaic plants and proposes an anomaly detection methodology that combines the advantages of the 2-sigma, short-window simple-moving average control charts with shading strength and irradiance transition parameters to detect early deviation in photovoltaic plant operational data. The key aspect of proposed methodology is that it requires neither historical data for model training procedure nor parameters from previous simulation. Only instantaneous meteorological and electrical parameters are required. The efficiency of the condition monitoring methodology has been validated through experimental results conducted in actual operating conditions. Results demonstrated that the proposed methodology is effective to identify non-ideal operating conditions for grid-connected photovoltaic plants, i.e., (i) normal operating condition, (ii) natural dynamic shading, (iii) artificial dynamic shading, and (iv) artificial static shading. Moreover, a low-cost and non-invasive internet-of-things-based embedded architecture is proposed to monitor photovoltaic plant operation in real-time.
关键词： Condition monitoring,Shading types,Non-ideal operating conditions,Anomaly detection,Grid-connected photovoltaic plants
更新于2025-09-16 10:30:52
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Embedding electronics into additive manufactured components using laser metal deposition and selective laser melting
摘要： The paper deals with the integration of a light emitting diode (LED) into an additive manufactured metal component. Selective laser melting (SLM) and laser metal deposition (LMD) are used. The material used is the chrome-nickel steel 316L. The basic component is manufactured by means of SLM and consists of a solid body and an area with grid structure. The solid body includes a duct in the shape of a groove with a recess for the positioning of the power cable. The LED is embedded in the grid structure via an inlet from the solid body. In further processing, the groove is filled with LMD. Two strategies with different parameter combinations were investigated. It shows that a high energy input near the power cable leads to its destruction. By using multiple parameter combinations during the manufacturing process, this destruction can be prevented. There was a comparison of both strategies with regard to the necessary number of tracks and duration of welding time.
关键词： condition monitoring,embedded electronics,additive manufacturing,laser-metal-deposition,selektive-laser-melting,process chain
更新于2025-09-12 10:27:22
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Encyclopedia of Maritime and Offshore Engineering || Optical Fiber Sensors for Marine Structural Condition Monitoring
摘要： Effective structural condition monitoring, and the action from it, is likely to result in significant economic savings from a better determination of structural integrity and use of the structure itself. It is also essential for the enhanced prediction of structural service lifetimes based on real-time measurement data obtained. Fiber optic-based sensors have shown real promise for structural condition monitoring, due to the attractive features they possess, such as small size, geometric versatility, multiplexing capability, and resistance to corrosive and hazardous environments. As a result, these sensors have been explored widely for monitoring a wide range of structural materials, including composites, concrete, limestones, carbon/aluminum, and metals (Scott et al., 2013; Sun et al., 2012; Nguyen et al., 2014; Kerrouche et al., 2009a), and the key parameters that have been measured include temperature, strain, vibration, relative humidity, pH, and chlorides, and so on.
关键词： Moisture Ingress Sensor,pH Sensor,Fiber Bragg Grating,Optical Fiber Sensors,Vibration Sensors,Marine Structural Condition Monitoring
更新于2025-09-12 10:27:22
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Online Monitoring of Aluminum Electrolytic Capacitors in Photovoltaic Systems by Magnetoresistive Sensors
摘要： Due to the environmental concerns and new energy policies, worldwide expectations for energy production utilizing photovoltaic (PV) systems are increasing significantly. The aluminum electrolytic capacitor (AEC) is extensively used in filtering application for power electronic converters in PV systems since they can achieve the highest energy density with the lowest cost. However, the lifetime of an AEC is limited due to the electrolyte vaporization. The degradation of AECs challenges the efficiency and reliability of a PV system. Therefore, the health-monitoring of AECs is indispensable for the PV systems to operate reliably. In this paper, an online AEC-monitoring scheme based on magnetic-field sensing is proposed for PV systems under various working conditions. The AEC-monitoring technique using the equivalent series resistance (ESR) and capacitance (C) as the health indicators were developed for the power electronic converters in PV systems. The proposed methodology considering the voltage drops on C can improve the accuracy in ESR-estimation and achieve the estimation of C. The simulation results with Simulink verified that the proposed method was capable of estimating the health indicators accurately over various levels of solar irradiance and ambient temperature. The tunneling magnetoresistive (TMR) sensors were pre-calibrated from -25 to 100 oC for implementation in PV systems. The experimental results proved that TMR sensors could measure the current of AECs effectively to achieve the precise estimations of the health indicators using the proposed technique. This technique is non-invasive, compact, and cost-effective since it can be realized with the TMR sensors or other MR sensors.
关键词： Aluminum electrolytic capacitor,tunneling magnetoresistive sensor,PV system,condition monitoring
更新于2025-09-12 10:27:22
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[IEEE 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hangzhou (2018.8.6-2018.8.9)] 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Assessment of Monitoring Regional Cropping System with Temporal Extraction Model Based on GF-1/WFV Imagery
摘要： Obtaining the information on the cropping system in a region accurately and timely is important for optimizing the regional agricultural resource allocation and crop layout. However, there are still technical bottlenecks such as poor spatial resolution, low precision and the lack of imagery in the application of remote sensing research in the spatial and temporal distribution of the cropping system. In this study, a more accurate remote sensing monitoring method for the regional cropping system was proposed. The imagery with wide field of view (WFV) of multi-temporal GF-1 satellite was used to construct a temporal extraction model of the cropping system, which was based on normalized difference vegetation index (NDVI). Using the method proposed in this paper, remote sensing monitoring of the main farming system in Suqian City, Jiangsu Province was carried out, which could provide reference for the extraction of the crop system in southern cloud and rain regions. The results showed that the whole crop development period of the main planting system (rice-winter wheat and winter wheat-summer maize) in the study area was covered by selecting the high time density GF-1 / WFV with 16 m resolution remote sensing imagery acquired from 2016 the complementarity of the multi-temporal imagery avoided the imagery loss caused by the cloudy climate in the middle and lower reaches of the Yangtze River in China. By building a mask and marking the "polluted" areas of the cloud, the interference of cloud imagery on crop information extraction was reduced effectively. In addition, the decision condition was optimized several times by the human-computer interaction, and the key parameter was determined to ensure the accuracy of the temporal extraction model. According to the result of monitoring the major cropping system in Suqian City, the overall classification accuracy was 93.56%, Kappa coefficient was 0.85 and the relative margin of error for individual crops was 7.53%, which met the accuracy requirements for application of agricultural achievements. These results showed that, in comparison with previous remote sensing methods, the method proposed in this study can monitor the regional main crop planting system accurately and can be used to monitor the cropping system based on the high-resolution imagery in multiple ripening areas. In this way, this approach will provide theoretical basis and technical support for the development of the precision agriculture, the optimization of regional cropping patterns and the efficient utilization of agricultural resources.
关键词： condition monitoring,NDVI,cropping system,GF-1/WFV imagery
更新于2025-09-10 09:29:36