A New Experimental Approach Using Image Processing-Based Tracking for an Efficient Fault Diagnosis in Pantograph–Catenary Systems

2017 ◽  
Vol 13 (2) ◽  
pp. 635-643 ◽  
Author(s):  
Ebru Karakose ◽  
Muhsin Tunay Gencoglu ◽  
Mehmet Karakose ◽  
Ilhan Aydin ◽  
Erhan Akin
Keyword(s):  
Image Processing ◽  
Fault Diagnosis ◽  
Experimental Approach

scholarly journals Application of Axis Orbit Image Optimization in Fault Diagnosis for Rotor System

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xinyu Pang ◽  
Jie Shao ◽  
Xuanyi Xue ◽  
Wangwang Jiang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Fault Diagnosis ◽  
Mathematical Morphology ◽  
Bp Neural Network ◽  
Feature Vector ◽  
Rotor System ◽  
Identification Accuracy ◽  
Frequency Noise ◽  
Axis Orbit

The shape characteristic of the axis orbit plays an important role in the fault diagnosis of rotating machinery. However, the original signal is typically messy, and this affects the identification accuracy and identification speed. In order to improve the identification effect, an effective fault identification method for a rotor system based on the axis orbit is proposed. The method is a combination of ensemble empirical mode decomposition (EEMD), morphological image processing, Hu invariant moment feature vector, and back propagation (BP) neural network. Experiments of four fault forms are performed in single-span rotor and double-span rotor test rigs. Vibration displacement signals in the X and Y directions of the rotor are processed via EEMD filtering to eliminate the high-frequency noise. The mathematical morphology is used to optimize the axis orbit including the dilation and skeleton operation. After image processing, Hu invariant moments of the skeleton axis orbits are calculated as the feature vector. Finally, the BP neural network is trained to identify the faults of the rotor system. The experimental results indicate that the time of identification of the tested axis orbits via morphological processing corresponds to 13.05 s, and the identification accuracy rate ranges to 95%. Both exceed that without mathematical morphology. The proposed method is reliable and effective for the identification of the axis orbit and aids in online monitoring and automatic identification of rotor system faults.

Crushing Tests of Flowlines Internal Layers: An Experimental Approach via Optical Motion Capture and Image Processing

2010 ◽  
Author(s):  
Andre´ L. C. Fujarra ◽  
Rodolfo T. Gonc¸alves ◽  
Celso P. Pesce ◽  
Marcelo R. Silva ◽  
Carlos A. F. Godinho
Keyword(s):  
Image Processing ◽  
Experimental Approach ◽  
Optical Measurement ◽  
Internal Layers ◽  
New Approach ◽  
Standard Procedures ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Crushing Behavior ◽  
Measurement Approach

New optical measurement approach for crushing tests of flow lines internal layers is detailed, based on an accurate tracking of the deformed carcass section through image processing. The results are compared to those from standard procedures based on discrete measurements using calipers. The new approach shows to be not only appropriate for the standard measurement requirements but also enables a comprehensive understanding of the crushing behavior.

scholarly journals Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7278
Author(s):  
Tito G. Amaral ◽  
Vitor Fernão Pires ◽  
Armando J. Pires
Keyword(s):  
Image Processing ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Power Plants ◽  
Principal Component ◽  
Processing Algorithm ◽  
Maximum Efficiency ◽  
Image Processing Algorithm ◽  
Wide Range ◽  
Pv Modules

Photovoltaic power plants nowadays play an important role in the context of energy generation based on renewable sources. With the purpose of obtaining maximum efficiency, the PV modules of these power plants are installed in trackers. However, the mobile structure of the trackers is subject to faults, which can compromise the desired perpendicular position between the PV modules and the brightest point in the sky. So, the diagnosis of a fault in the trackers is fundamental to ensure the maximum energy production. Approaches based on sensors and statistical methods have been researched but they are expensive and time consuming. To overcome these problems, a new method is proposed for the fault diagnosis in the trackers of the PV systems based on a machine learning approach. In this type of approach the developed method can be classified into two major categories: supervised and unsupervised. In accordance with this, to implement the desired fault diagnosis, an unsupervised method based on a new image processing algorithm to determine the PV slopes is proposed. The fault detection is obtained comparing the slopes of several modules. This algorithm is based on a new image processing approach in which principal component analysis (PCA) is used. Instead of using the PCA to reduce the data dimension, as is usual, it is proposed to use it to determine the slope of an object. The use of the proposed approach presents several benefits, namely, avoiding the use of a wide range of data and specific sensors, fast detection and reliability even with incomplete images due to reflections and other problems. Based on this algorithm, a deviation index is also proposed that will be used to discriminate the panel(s) under fault. Several test cases are used to test and validate the proposed approach. From the obtained results, it is possible to verify that the PCA can successfully be adapted and used in image processing algorithms to determine the slope of the PV modules and so effectively detect a fault in the tracker, even when there are incomplete parts of an object in the image.

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