Memory Linked Anomaly Metric Learning of Thermography Rail Defects Detection System

Zhang, Xiyuan; Gao, Bin; Shi, Yunhan; Woo, Wai Lok; Li, Haoran

doi:10.1109/jsen.2021.3112698

Cited by 13 publications

(3 citation statements)

References 36 publications

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“…where 𝑑 𝑖 , 𝑒 𝑖 , and 𝑏 are coefficients, kernel vectors, and threshold learned by OC-SVM, respectively. An anomaly detection model [25] is developed to detect the abnormal patterns, which can be defined as 𝑆 𝑡 (𝜃).The main process are shown in Fig. 7.…”

Section: Physically Linked Abnormal Learning Strategymentioning

confidence: 99%

Dynamic Rail Near-Surface Inspection of Multiphysical Coupled Electromagnetic and Thermography Sensing System

Gao

et al. 2023

IEEE Trans. Instrum. Meas.

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The effectiveness of railway fault inspection has remained challenging. Conventional techniques are still functionally limited and unable to meet the increasing demand of railway diagnosis. To mitigate the variety of rail fault detection problems, this paper proposes a dynamic railway inspection system based on multi-physical coupled electromagnetic and thermography sensing. It further shows the development and construction of a new inverted L-type magnet yoke abreast with volumetric coils array. The novel structure can not only significantly enhance the sensitivity and detectability of the region of interest (ROI), but also effectively detect the subsurface defects with the compensation of coils array due to the coupled electromagnetic field. Furthermore, the theoretical analysis of the coupled physical fields has been derived and proved to be consistent with the numerical simulation results. A rail test sample with various defects is carried out to verify the feasibility of the proposed system. Additionally, a metric learning post-processing algorithm has been conducted for distilling eddy current signals and thermograms to improve the accuracy of the detection results. On-site experimental and contrast results with various levels of performance validation have demonstrated that the integrated system is well suited for dynamic rail inspection on near-surface cracks at speed of 1 km/h.

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Section: Physically Linked Abnormal Learning Strategymentioning

confidence: 99%

Dynamic Rail Near-Surface Inspection of Multiphysical Coupled Electromagnetic and Thermography Sensing System

Gao

et al. 2023

IEEE Trans. Instrum. Meas.

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“…A large number of studies for bearing fault diagnosis appeared such as oil analysis, temperature monitoring, acoustic emission, and vibration (vibration acceleration) analysis. [4][5][6][7][8][9][10] In recent years, data-driven diagnosis methods based on machine learning and deep learning have attracted the most attention of researchers and have become a hot topic in the field of bearing fault diagnosis. [11][12][13] At the same time, with the release of some typical public data sets, such as Case Western Reserve University (CWRU) Dataset, Paderborn University Dataset, PRONOSTIA Dataset, and Intelligent Maintenance Systems (IMS) Dataset.…”

Section: Introductionmentioning

confidence: 99%

“…A large number of studies for bearing fault diagnosis appeared such as oil analysis, temperature monitoring, acoustic emission, and vibration (vibration acceleration) analysis. 4–10…”

Section: Introductionmentioning

confidence: 99%

Online anomaly detection and remaining useful life prediction of rotating machinery based on cumulative summation features

Wang

2022

Measurement and Control

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The bearing is the core component of the gearbox transmission system. Once it is damaged during operation, it will cause the shutdown of the mechanical equipment for maintenance. It has important application significance to carry out fault detection and remaining useful life (RUL) prediction. Whereas, some bottlenecks, such as the noise interference of state characteristics, the excessive dependence of supervised learning on prior samples, and the practical RUL online calculation, restrict the industrial application of RUL prediction for rotating machinery equipment. To overcome the above problems, this paper introduces the discrete wavelet transform (DWT) to decrease the noise of the vibration acceleration signal obtained, and then uses the sliding average method to weaken the transient excitation. To make the state characteristics of the monitored bearing trendy, linear, and monotonic, this paper proposes a new set of state interpret indicators: energy and cumulative summation feature (CSF) to reflect the bearing health status. Based on the available bearing health information, the fault boundary threshold is established through the 3[Formula: see text] criteria, which serves as the basis for first predicting time (FPT) detection. Once the FPT point is determined, this paper applies CSF to replace the original vibration acceleration amplitude as the degradation indicator, which has better linearity and monotonicity than amplitude-based indicators, and which is conducive to the implementation of simple structure curve fitting to carry out the overall RUL prediction. Comparing with existing methods, such as relevance vector machine (RVM), deep belief network (DBN), and particle filtering (PF)-based methods, the experimental results demonstrate that the proposed method has the best RUL prediction efficiency and the fastest convergence.

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Detection and classification of surface defects on hot-rolled steel using vision transformers

Vasan,

Sridharan,

Vaithiyanathan

et al. 2024

Heliyon

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Memory Linked Anomaly Metric Learning of Thermography Rail Defects Detection System

Cited by 13 publications

References 36 publications

Dynamic Rail Near-Surface Inspection of Multiphysical Coupled Electromagnetic and Thermography Sensing System

Dynamic Rail Near-Surface Inspection of Multiphysical Coupled Electromagnetic and Thermography Sensing System

Online anomaly detection and remaining useful life prediction of rotating machinery based on cumulative summation features

Detection and classification of surface defects on hot-rolled steel using vision transformers

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