Research on a damage identification method of harmonic magnetic field detection in steel pipes with cladding

2020 ◽  
Vol 62 (9) ◽  
pp. 533-539
Author(s):  
Xinhua Wang ◽  
Yaping Gu ◽  
Yingchun Chen ◽  
Zia Ullah ◽  
Yizhen Zhao

This paper presents a harmonic magnetic field detection technology for damage identification of an in-service coated steel pipeline. Based on the principles of electromagnetic theory and magnetic field detection combined with magnetic focusing technology, an array consisting of a focusing detection probe and a harmonic magnetic field detection system were designed. However, the acquired detection signal includes an excitation signal in addition to the defect information. In order to make the defect information more obvious, the excitation signal needs to be removed to extract the defect feature. Local mean decomposition (LMD) is a new time-frequency analysis method that adaptively decomposes a signal into a set of product function (PF) combinations. The envelope of the PF is the instantaneous amplitude and the instantaneous frequency can be calculated by demodulating the derivative of the phase with a uniform amplitude-modulated signal. This method completely bypasses the Hilbert transform. Therefore, it does not involve the problem of negative frequencies without physical meaning. The effectiveness of LMD is demonstrated by a successful example of damage detection. Combining the data characteristics obtained by the experiment, the data processing algorithm suitable for the test data is written by improving the LMD. The algorithm is easy to use and has high engineering practicability.

Author(s):  
Hua Fan ◽  
Jingxuan Yang ◽  
Jia Zhang ◽  
Ke Zhang ◽  
Dezhi Xing ◽  
...  

Author(s):  
Zhi Zeng ◽  
Yongfu Zhou

Background: Detection technology is a product development technique that serves as a basis for quality assurance. As electric energy meters (EEMs) are measurement instruments whose use is mandatory in several nations, their accuracy, which directly depends on their reliability and proper functioning, is paramount. In this study, to eliminate electromagnetic interference, a device is developed for testing a set of EEMs under a constant magnetic field interference. The detection device can simultaneously test 6 electric meters; moreover, in the future, it will be able to measure the influence of magnetic field strength on the measurement accuracy of EEMs, thereby improving the production efficiency of electric meter manufacturers. Methods: In this study, we first design a 3D model of the detection device for a single meter component; then, we establish a network, which includes a control system, and perform the planning of the path of a block that generates a constant magnetic field. Finally, we control the three-axis motion and rotation of the block using a PLC to implement detection for the five sides of the EEM. Results & Discussion: The proposed device can accurately determine whether an EEM can adequately function, within the error range prescribed by a national standard, under electromagnetic interference; this can enable reliable, automatic testing and fault detection for EEMs. Experiments show that our device can decrease the labor cost for EEM manufacturers.


Measurement ◽  
2021 ◽  
pp. 109534
Author(s):  
Yizhen Zhao ◽  
Xinhua Wang ◽  
Tao Sun ◽  
Yingchun Chen ◽  
Lin Yang ◽  
...  

Nano Energy ◽  
2021 ◽  
pp. 105964
Author(s):  
Sugato Hajra ◽  
Venkateswaran Vivekananthan ◽  
Manisha Sahu ◽  
Gaurav Khandelwal ◽  
Nirmal Prashanth Maria Joseph Raj ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4456
Author(s):  
Sungjae Ha ◽  
Dongwoo Lee ◽  
Hoijun Kim ◽  
Soonchul Kwon ◽  
EungJo Kim ◽  
...  

The efficiency of the metal detection method using deep learning with data obtained from multiple magnetic impedance (MI) sensors was investigated. The MI sensor is a passive sensor that detects metal objects and magnetic field changes. However, when detecting a metal object, the amount of change in the magnetic field caused by the metal is small and unstable with noise. Consequently, there is a limit to the detectable distance. To effectively detect and analyze this distance, a method using deep learning was applied. The detection performances of a convolutional neural network (CNN) and a recurrent neural network (RNN) were compared from the data extracted from a self-impedance sensor. The RNN model showed better performance than the CNN model. However, in the shallow stage, the CNN model was superior compared to the RNN model. The performance of a deep-learning-based (DLB) metal detection network using multiple MI sensors was compared and analyzed. The network was detected using long short-term memory and CNN. The performance was compared according to the number of layers and the size of the metal sheet. The results are expected to contribute to sensor-based DLB detection technology.


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