scholarly journals Visual Leakage Inspection in Chemical Process Plants Using Thermographic Videos and Motion Pattern Detection

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6659
Author(s):  
Mina Fahimipirehgalin ◽  
Birgit Vogel-Heuser ◽  
Emanuel Trunzer ◽  
Matthias Odenweller
Keyword(s):  
Chemical Process ◽  
Pattern Detection ◽  
Training Data ◽  
Detection Methods ◽  
Normal Operation ◽  
Inspection System ◽  
Leakage Detection ◽  
Motion Pattern ◽  
Motion Patterns ◽  
Process Plants

Liquid leakage from pipelines is a critical issue in large-scale chemical process plants since it can affect the normal operation of the plant and pose unsafe and hazardous situations. Therefore, leakage detection in the early stages can prevent serious damage. Developing a vision-based inspection system by means of IR imaging can be a promising approach for accurate leakage detection. IR cameras can capture the effect of leaking drops if they have higher (or lower) temperature than their surroundings. Since the leaking drops can be observed in an IR video as a repetitive phenomenon with specific patterns, motion pattern detection methods can be utilized for leakage detection. In this paper, an approach based on the Kalman filter is proposed to track the motion of leaking drops and differentiate them from noise. The motion patterns are learned from the training data and applied to the test data to evaluate the accuracy of the method. For this purpose, a laboratory demonstrator plant is assembled to simulate the leakages from pipelines, and to generate training and test videos. The results show that the proposed method can detect the leaking drops by tracking them based on obtained motion patterns. Furthermore, the possibilities and conditions for applying the proposed method in a real industrial chemical plant are discussed at the end.

scholarly journals Fault Detection Method Based on Global-Local Marginal Discriminant Preserving Projection for Chemical Process

Processes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 122
Author(s):  
Yang Li ◽  
Fangyuan Ma ◽  
Cheng Ji ◽  
Jingde Wang ◽  
Wei Sun
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Chemical Process ◽  
Detection Method ◽  
Detection Performance ◽  
Detection Methods ◽  
Normal Operation ◽  
Discriminative Feature ◽  
Inherent Feature ◽  
Global And Local

Feature extraction plays a key role in fault detection methods. Most existing methods focus on comprehensive and accurate feature extraction of normal operation data to achieve better detection performance. However, discriminative features based on historical fault data are usually ignored. Aiming at this point, a global-local marginal discriminant preserving projection (GLMDPP) method is proposed for feature extraction. Considering its comprehensive consideration of global and local features, global-local preserving projection (GLPP) is used to extract the inherent feature of the data. Then, multiple marginal fisher analysis (MMFA) is introduced to extract the discriminative feature, which can better separate normal data from fault data. On the basis of fisher framework, GLPP and MMFA are integrated to extract inherent and discriminative features of the data simultaneously. Furthermore, fault detection methods based on GLMDPP are constructed and applied to the Tennessee Eastman (TE) process. Compared with the PCA and GLPP method, the effectiveness of the proposed method in fault detection is validated with the result of TE process.

Static and dynamic novelty detection methods for jet engine health monitoring

2006 ◽  
Vol 365 (1851) ◽  
pp. 493-514 ◽  
Author(s):  
Paul Hayton ◽  
Simukai Utete ◽  
Dennis King ◽  
Steve King ◽  
Paul Anuzis ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Support Vector Machine Model ◽  
Novelty Detection ◽  
Training Data ◽  
Detection Methods ◽  
Normal Operation ◽  
Support Vector ◽  
Rotating Shaft ◽  
Machine Model ◽  
Jet Engine

Novelty detection requires models of normality to be learnt from training data known to be normal. The first model considered in this paper is a static model trained to detect novel events associated with changes in the vibration spectra recorded from a jet engine. We describe how the distribution of energy across the harmonics of a rotating shaft can be learnt by a support vector machine model of normality. The second model is a dynamic model partially learnt from data using an expectation–maximization-based method. This model uses a Kalman filter to fuse performance data in order to characterize normal engine behaviour. Deviations from normal operation are detected using the normalized innovations squared from the Kalman filter.

scholarly journals Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector

2019 ◽  
Vol 9 (6) ◽  
pp. 1128 ◽  
Author(s):  
Yundong Li ◽  
Wei Hu ◽  
Han Dong ◽  
Xueyan Zhang
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Hurricane Sandy ◽  
Training Data ◽  
Aerial Images ◽  
Detection Methods ◽  
Single Shot ◽  
Data Set ◽  
Augmentation Strategies ◽  
Post Disaster

Using aerial cameras, satellite remote sensing or unmanned aerial vehicles (UAV) equipped with cameras can facilitate search and rescue tasks after disasters. The traditional manual interpretation of huge aerial images is inefficient and could be replaced by machine learning-based methods combined with image processing techniques. Given the development of machine learning, researchers find that convolutional neural networks can effectively extract features from images. Some target detection methods based on deep learning, such as the single-shot multibox detector (SSD) algorithm, can achieve better results than traditional methods. However, the impressive performance of machine learning-based methods results from the numerous labeled samples. Given the complexity of post-disaster scenarios, obtaining many samples in the aftermath of disasters is difficult. To address this issue, a damaged building assessment method using SSD with pretraining and data augmentation is proposed in the current study and highlights the following aspects. (1) Objects can be detected and classified into undamaged buildings, damaged buildings, and ruins. (2) A convolution auto-encoder (CAE) that consists of VGG16 is constructed and trained using unlabeled post-disaster images. As a transfer learning strategy, the weights of the SSD model are initialized using the weights of the CAE counterpart. (3) Data augmentation strategies, such as image mirroring, rotation, Gaussian blur, and Gaussian noise processing, are utilized to augment the training data set. As a case study, aerial images of Hurricane Sandy in 2012 were maximized to validate the proposed method’s effectiveness. Experiments show that the pretraining strategy can improve of 10% in terms of overall accuracy compared with the SSD trained from scratch. These experiments also demonstrate that using data augmentation strategies can improve mAP and mF1 by 72% and 20%, respectively. Finally, the experiment is further verified by another dataset of Hurricane Irma, and it is concluded that the paper method is feasible.

Classification method of marine target motion pattern based on spatial-temporal trajectories

2021 ◽  
pp. 1-15
Author(s):  
Baichen Jiang ◽  
Wei Zhou ◽  
Jian Guan ◽  
Jialong Jin
Keyword(s):  
Pattern Classification ◽  
Training Sample ◽  
Classification Algorithm ◽  
Classification Model ◽  
Motion Pattern ◽  
Motion Patterns ◽  
Lp Norm ◽  
Sparse Representation Classification ◽  
Route Safety

Classifying the motion pattern of marine targets is of important significance to promote target surveillance and management efficiency of marine area and to guarantee sea route safety. This paper proposes a moving target classification algorithm model based on channel extraction-segmentation-LCSCA-lp norm minimization. The algorithm firstly analyzes the entire distribution of channels in specific region, and defines the categories of potential ship motion patterns; on this basis, through secondary segmentation processing method, it obtains several line segment trajectories as training sample sets, to improve the accuracy of classification algorithm; then, it further uses the Leastsquares Cubic Spline Curves Approximation (LCSCA) technology to represent the training sample sets, and builds a motion pattern classification sample dictionary; finally, it uses lp norm minimized sparse representation classification model to realize the classification of motion patterns. The verification experiment based on real spatial-temporal trajectory dataset indicates that, this method can effectively realize the motion pattern classification of marine targets, and shows better time performance and classification accuracy than other representative classification methods.

scholarly journals A Study on Pre-processing Algorithms for Metal Parts Inspection

2011 ◽  
Vol 7 (1) ◽  
pp. 1-4
Author(s):  
Haider Hashim ◽  
Anton Prabuwono ◽  
Siti Norul Huda Abdullah
Keyword(s):  
Image Processing ◽  
Morphological Analysis ◽  
Image Understanding ◽  
Detection Methods ◽  
Experimental Result ◽  
Inspection System ◽  
Metal Parts ◽  
Analysis Task ◽  
Processing Step ◽  
Filter Noise

Pre-processing is very useful in a variety of situations since it helps to suppress information that is not related to the exact image processing or analysis task. Mathematical morphology is used for analysis, understanding and image processing. It is an influential method in the geometric morphological analysis and image understanding. It has befallen a new theory in the digital image processing domain. Edges detection and noise reduction are a crucial and very important pre-processing step. The classical edge detection methods and filtering are less accurate in detecting complex edge and filtering various types of noise. This paper proposed some useful mathematic morphological techniques to detect edge and to filter noise in metal parts image. The experimental result showed that the proposed algorithm helps to increase accuracy of metal parts inspection system.

scholarly journals EVALUATION OF SEMANTIC SEGMENTATION METHODS FOR DEFORESTATION DETECTION IN THE AMAZON

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1497-1505
Author(s):  
R. B. Andrade ◽  
G. A. O. P. Costa ◽  
G. L. A. Mota ◽  
M. X. Ortega ◽  
R. Q. Feitosa ◽  
...  
Keyword(s):  
Global Climate ◽  
Biodiversity Loss ◽  
Semantic Segmentation ◽  
Training Sample ◽  
Training Data ◽  
Detection Methods ◽  
Amazon Forest ◽  
Convolutional Network ◽  
Detection Approach ◽  
Segmentation Methods

Abstract. Deforestation is a wide-reaching problem, responsible for serious environmental issues, such as biodiversity loss and global climate change. Containing approximately ten percent of all biomass on the planet and home to one tenth of the known species, the Amazon biome has faced important deforestation pressure in the last decades. Devising efficient deforestation detection methods is, therefore, key to combat illegal deforestation and to aid in the conception of public policies directed to promote sustainable development in the Amazon. In this work, we implement and evaluate a deforestation detection approach which is based on a Fully Convolutional, Deep Learning (DL) model: the DeepLabv3+. We compare the results obtained with the devised approach to those obtained with previously proposed DL-based methods (Early Fusion and Siamese Convolutional Network) using Landsat OLI-8 images acquired at different dates, covering a region of the Amazon forest. In order to evaluate the sensitivity of the methods to the amount of training data, we also evaluate them using varying training sample set sizes. The results show that all tested variants of the proposed method significantly outperform the other DL-based methods in terms of overall accuracy and F1-score. The gains in performance were even more substantial when limited amounts of samples were used in training the evaluated methods.

scholarly journals Left Ventricular Systolic Motion Pattern Differs Among Patients With Left Bundle Branch Block Patterns

2021 ◽  
Author(s):  
Yan Chen ◽  
Yanjuan Zhang ◽  
Di Xu ◽  
Chun Chen ◽  
Changqing Miao ◽  
...  
Keyword(s):  
Left Bundle Branch Block ◽  
Basal Layer ◽  
Right Ventricular Outflow Tract ◽  
Left Ventricular ◽  
Control Group ◽  
Motion Pattern ◽  
Bundle Branch Block ◽  
Motion Patterns ◽  
Basal Rotation ◽  
Peak Value

Abstract Purpose:The study aimed to investigate left ventricular (LV) motion pattern in patients with LBBB patterns including patients with pacemaker rhythm (PM), type B Wolff-Parkinson-White syndrome (B-WPW), premature ventricular complexes originating from the right ventricular outflow tract (RVOT-PVC), and complete left bundle branch block (CLBBB).Methods: Two-dimensional speckle tracking was used to evaluate peak value and time to peak value of the LV twist, LV apex rotation, and LV base rotation in patients with PM, B-WPW, RVOT-PVC, and CLBBB with normal LV ejection fraction, and in age-matched control subjects.Results: The LV motion patterns were altered in all patients compared to the control groups. Patients with PM and CLBBB had a similar LV motion pattern with a reduced peak value of LV apex rotation and LV twist. Patients with B-WPW demonstrated the opposite trend in the reduction of LV rotation peak value, which was more dominant in the basal layer. The most impairment in the LV twist/rotation peak value was identified in patients with RVOT-PVC. Compared to the control group, the apical-basal rotation delay was prolonged in patients with CLBBB, followed by those with B-WPW, RVAP, and RVOT-PVC.Conclusion: The LV motion patterns were different among patients with different patterns of LBBB. CLBBB and PM demonstrated a reduction in LV twist/rotation that was pronounced in the apical layer, B-WPW showed a reduction in the basal layer, and RVOT-PVC in both layers. CLBBB had the most pronounced LV apical-basal rotation dyssynchrony.

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