Detecting defective electrical components in heterogeneous infra-red images by spatial control charts

Jamshidieini, Bahman; Fazaee, Reza

doi:10.1016/j.infrared.2016.04.013

Cited by 10 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We used the statistical kernel functions Triangular, Epanechnikov (parabolic), Quartic (bi-weight), Triweight, Tricube, and Gaussian to establish an exposure-response model (Jamshidieini B 2016). These kernels are plotted in Figure 2.…”

Section: Statistical Kernels For Aggregation Schemementioning

confidence: 99%

Aggregating the response in time series lag models using Quad weight kernel: Impact of Environmental Factors on Schizophrenia Admissions

Thomas,

Parameswaran

2024

Preprint

View full text Add to dashboard Cite

There has been a growing apprehension in the past few years concerning the issue of pollution and climate change. Several articles have shown the impact of air pollutants and atmosphere factors like temperature and relative humidity on health. This encompasses a deterioration in cognitive function and a heightened susceptibility to neurological diseases like Schizophrenia. This work constructed a new dataset for hospital admissions of schizophrenia patients and daily environmental values from various locations in Bangalore City, India. The Number of Admissions(NoA) to hospitals is used as a proxy for the incidence of schizophrenia emergence. In this kind of time series data where a response has a delayed impact on dependent variables, distributed lag models(DLM) are applicable. The newly created data is used to analyze the impact of pollution and climate factors on hospital admissions in Bengaluru, India. We have compared the applicability of the aggregated response technique using the Distributed lag non-linear Model(ARDLNM) in comparison with existing techniques, the Distributed lag non-linear Model and the Non-linear Auto regressive Distributed Lag Model(NARDL). ARDLNM using the Epanechnikov kernel showed improved performance over DLNM by 25%, 15%, 17% and 72% for performance metrics MSE, MAE, MAPE and R2 respectively. Within aggregation methods, we have introduced a quad-weight kernel that further improved performance over the Epanechnikov kernel by 17% in terms of MSE.

show abstract

Section: Statistical Kernels For Aggregation Schemementioning

confidence: 99%

Aggregating the response in time series lag models using Quad weight kernel: Impact of Environmental Factors on Schizophrenia Admissions

Thomas,

Parameswaran

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…With Nonlinear ARDL, Anderl et al( 2023) investigated the impact of economic policy uncertainty on oil price. Jamshidieini et al (2016) used multiple statistical kernels such as Uniform ("rectangular window"), Triangular, Epanechnikov (parabolic), Quartic (biweight), Triweight, Tricube, Gaussian, Cosine, Logistic and Sigmoid in a different context. Since the nature of data is similar, aggregation schemes with different such kernels may be relevant for our problem as well.…”

Section: Background Studymentioning

confidence: 99%

“…Triweight, Tricube and Gaussian to establish exposure-response model (Jamshidieini B 2016). These kernels plotted in figure 2.…”

Section: Statistical Kernels For Aggregation Schemementioning

confidence: 99%

Aggregating the response in time series lag models using Quad weight kernel: Impact of Environmental Factors on Schizophrenia Admissions

Thomas,

Parameswaran

2023

Preprint

View full text Add to dashboard Cite

In time series data where a data point has a delayed impact on dependent variables, distributed lag models (DLM) such as Distributed Lag Non-linear Model (DLNM) and Autoregressive Distributed Lag (ARDL) are applicable. Several articles have shown the impact of air pollutants and atmosphere factors like temperature and relative humidity on health. In this paper, we use environmental data (pollutants & climate variables) and daily hospital admissions of schizophrenia patients, to analyze the impact of pollution and climate factors on the hospital admissions in Bengaluru, India. In this work, we are using Number of Admissions (NoA) to hospitals as a proxy for the incidence of schizophrenia emergence. We have explored the applicability of Non-linear ARDL and aggregating responses in DLNM (DLNM-Ag.R) technique. DLNM-Ag.R using Epanechnikov kernel showed improved performance over DLNM by 25%, 15%, 17% and 72% for performance metrices MSE, MAE, MAPE and R2 respectively. Within aggregation methods, we have introduced a quad-weight kernel that further improved performance over Epanechnikov kernel by 17% in terms of MSE.

show abstract

“…The fault detection based on thermal imaging mainly contains three steps: detection of region of interest, feature extraction, and classification. Firstly, the region of interest is detected using scale-invariant feature transform [5], K-means [8,9], instance segmentation [10], and so forth. Then, statistical features such as bag-of-visual-word [11], frequency domain information based on two-dimensional Fourier transformation [12], features based on convolutional neural network (CNN) [13], or temperature data including maximum temperature, average temperature, the temperature difference between the target region and the reference region are extracted from the region.…”

Section: Introductionmentioning

confidence: 99%

Online Monitoring of Electrical Equipment Condition Based on Infrared Image Temperature Data Visualization

Wang

Fan

et al. 2021

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

Temperature is one of the most common indicators for the health of electrical equipment in the substation. By now, infrared thermography has been an important monitoring tool due to its real-time temperature measurement and noncontact manner. However, at present, portable thermal imagers are usually used to collect and store the status image of single equipment at a certain time, which cannot continuously track the condition of the equipment and the efficiency is very low. In order to solve this problem, our team proposed a method for online condition of electrical equipment based on the visualization and processing of infrared image temperature data. We developed video inspection software, equipped with an infrared camera for automatic inspection to obtain infrared images. After that, the target equipment in a whole image are automatically extracted based on the reference image and the equipment type of each target is classified. Then, in order to visualize temperature data and extract temperature information for different types of equipment, the concepts of heating spot, heating section, and heating area are defined. Finally, diagnosis rules based on relevant related thermal fault diagnosis standards and past experience are proposed to evaluate the condition of each target equipment, using the temperature rise and heat generation percentage. The method has been applied in a real substation and proved its effectiveness.

show abstract

Detecting defective electrical components in heterogeneous infra-red images by spatial control charts

Cited by 10 publications

References 17 publications

Aggregating the response in time series lag models using Quad weight kernel: Impact of Environmental Factors on Schizophrenia Admissions

Aggregating the response in time series lag models using Quad weight kernel: Impact of Environmental Factors on Schizophrenia Admissions

Aggregating the response in time series lag models using Quad weight kernel: Impact of Environmental Factors on Schizophrenia Admissions

Online Monitoring of Electrical Equipment Condition Based on Infrared Image Temperature Data Visualization

Contact Info

Product

Resources

About