Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine

Li, Qiong; Zhao, Tingting; Zhang, Lingchao; Sun, Weimin; Zhao, Xibin

doi:10.1155/2017/3451358

Cited by 22 publications

(10 citation statements)

References 24 publications

(20 reference statements)

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“…1, the natural logical process to construct an automatic ferrography system is linear, including the following steps: image preprocessing →wear particle segmentation →feature extraction →wear particle recognition →wear condition detection. Various image processing techniques, such as image segmentation [8][9][10][11], feature extraction [12,13], and classification methods [14][15][16][17][18][19], have been applied to wear particle analysis.…”

Section: Introductionmentioning

confidence: 99%

Direct detection of wear conditions by classification of ferrograph images

Wang

Liu

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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Wear particle analysis is one of the main indicators for the detection of wear condition. Traditional computer-aided wear particle analysis employs a unidirectional linear process, i.e. image preprocessing → wear particle segmentation → feature extraction → wear particle recognition → wear condition detection. It has disadvantages such as inaccurate segmentation of wear particles, numerous steps in the "artificial design feature method", accumulation and transmission of errors, and difficulty in overall optimization of the algorithms. In this study, a framework from overall determination to a detailed recognition is proposed to realize intelligent ferrography analysis. Furthermore, a convolutional neural network (CNN) is developed to classify seven types of ferrograph images, which can be used to directly determine wear conditions including initial wear, normal wear, abnormal wear, and severe wear. Compared with the traditional methods, the proposed CNN can automatically extract features layer by layer, yielding accurate end-to-end classification of the ferrograph images. The average accuracy of the CNN model on the test set is 90%. This research provides an effective solution for the automatic identification of wear conditions.

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Section: Introductionmentioning

confidence: 99%

Direct detection of wear conditions by classification of ferrograph images

Wang

Liu

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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“…The overall process can be summarized by the diagram of Fig. 2.Finally, we can get the smallest training error and the lowest weight norm among possible solutions through a simple operation of generalized inverse of the output matrices of the hidden layer (Huang et al 2006b;Huang et al 2012;Lei et al 2018;Li et al 2017).…”

Section: Extreme Learning Machinesmentioning

confidence: 99%

Electrical impedance tomography image reconstruction based on backprojection and extreme learning machines

et al. 2020

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Purpose Electrical impedance tomography (EIT) is an image technique based on the application of an alternating electrical current on electrodes placed on the surface of the domain, which are also responsible for measuring the resulting electrical potentials. EIT main advantages are portability, low cost, and nonuse of ionizing radiation. EIT image reconstruction depends on the resolution of the direct and inverse problems, which is nonlinear and ill-posed. Several reconstruction methods have been used to solve EIT inverse problem, from Newton-based methods to bio-and social-inspired metaheuristics. Method In this work, we propose a new approach: the use of random-weighted neural networks, specifically extreme learning machines (ELM), to approximate sinograms from electrical potential data and, therefore, use the classical backprojection algorithm for image reconstruction. We generated a database of 4000 synthetic 128 × 128 images. We trained 16 ELMs corresponding to a 16-electrode EIT system placed on a circular domain. Results Results were evaluated according to peak-to-noise ratio (PSNR) and Structural Similarity Index (SSIM), as well as visual inspection. Results are fair and similar to image reconstructions obtained by the direct application of the backprojection algorithm, in case this reconstruction problem would be a classic tomographic reconstruction. Conclusion Our work suggested the use of ELMs to create sinograms from the electrical potential data of the studied domain. Sinograms could be easily reconstructed by the backprojection technique for final image generation, where objects and domain are represented. Finally, we were able to reduce the time of EIT reconstruction, mainly due to the high speed of the ELMs, and obtain reasonable and consistent images.

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“…Because of the mechanical transmission system fault, abnormal changes such as lubricating oil quality, temperature, and mechanical vibration can happen. Therefore, its fault diagnosis is mainly carried out using ferrography analysis, temperature detection, and vibration detection [1,2]. The ferrography analysis method relies on the experience of human beings, which may cause serious misjudgment because of the irregular spectrum process.…”

Section: Figure 1 the Faults Of Gearof The Shearer Ranging Armmentioning

confidence: 99%

“…In the modulation, the meshing frequency and its harmonics are carrier frequencies, and the rotation frequency and its multiple frequencies are the modulation frequencies. The vibration signals with modulation are presented in equations (1)…”

Section: Of Gearsmentioning

confidence: 99%

Accurate Fault Location Method of the Mechanical Transmission System of Shearer Ranging Arm

Mao

Zhang

et al. 2020

IEEE Access

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In the process of coal mine production, the fault of the mechanical transmission system of the shearer ranging arm directly affects safety production. Therefore, it is very important to achieve the accurate fault location of the shearer ranging arm. The combination method of optimized continuous complex Morlet wavelet envelope demodulation spectrum analysis and spectrum analysis is proposed. Firstly, the spectrum contrast analysis of vibration signals between the normal state and fault state is performed, which can obtain the fault characteristics frequency and preliminary fault location. Then, the optimized continuous complex Morlet wavelet envelope demodulation algorithm is proposed to extract the side-bands characteristics of vibration signals, which can obtain the rotation frequency of fault location by the contrast analysis of side-bands characteristics in normal and fault state. In the algorithm, the center frequency and bandwidth of Morlet wavelet are optimized by wavelet Shannon entropy, and the optimal scale factor can be accurately determined by spectrum contrast analysis of vibration signals in normal and fault state. Finally, the accurate gear fault location is achieved by comprehensive analysis results of optimized continuous complex Morlet wavelet envelope demodulation spectrum and spectrum contrast. The simulation signals and practical vibration signals are applied to verify the proposed accurate gear fault location method. The experiment results indicate that the center frequency and bandwidth determined by wavelet Shannon entropy make the wavelet waveform best match the fault impact signal, and the optimal scale factor can be accurately determined by spectrum contrast analysis of vibration signals in normal and fault state, and the proposed method can achieve the precise fault location of the mechanical transmission system of shearer ranging arm. It provides the method for the on-site rapid accurate fault location of the mechanical transmission system. It is of great significance to the safe and efficient production of the coal mine.

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Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine

Cited by 22 publications

References 24 publications

Direct detection of wear conditions by classification of ferrograph images

Direct detection of wear conditions by classification of ferrograph images

Electrical impedance tomography image reconstruction based on backprojection and extreme learning machines

Accurate Fault Location Method of the Mechanical Transmission System of Shearer Ranging Arm

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