Remote Diagnosis Method of Substation Equipment Fault Based on Image Recognition Technology
DOI:
https://doi.org/10.13052/dgaej2156-3306.3623Keywords:
Image recognition, substation, fault diagnosis, fault area.Abstract
In order to better guarantee the operation effect of substation equipment,
a remote fault diagnosis method of substation equipment based on image
recognition technology is proposed. Combined with image recognition tech-
nology, the running image of substation equipment is tracked and collected,
the information characteristics of substation equipment are deeply excavated,
and the fault area of substation equipment is accurately judged. Remote
positioning has been carried out to realize the accurate detection of substa-
tion equipment fault. Finally, through the experiment, the remote diagnosis
method of substation equipment fault based on image recognition technology
is in the actual application process With higher accuracy, it can effectively
ensure the safety of substation equipment operation.
Downloads
References
Jingwen X, Yunxuan Z, Ziqi Y. Retraction: Early bearing fault diagnosis
based on improved SFLA and ELM network[J]. Transactions of the
Canadian Society for Mechanical Engineering, 2018, 42(2):187–193.
Wang M, Zhang Z, Li K, et al. Research on key technologies of fault
diagnosis and early warning for high-end equipment based on intelligent
manufacturing and Internet of Things[J]. The International Journal of
Advanced Manufacturing Technology, 2020, 107(3):1039–1048.
Zhi Z, Ming W, Zongjie C, et al. SAR Image Recognition with
Monogenic Scale Selection-Based Weighted Multi-task Joint Sparse
Representation[J]. Remote Sensing, 2018, 10(4):504.
Lin T, Chen Z, Zheng C, et al. Fault diagnosis of lithium-ion battery pack
based on hybrid system and dual extended Kalman filter algorithm[J].
IEEE Transactions on Transportation Electrification, 2020(99):1.
Cheng L, Yu T. Dissolved Gas Analysis Principle-Based Intelligent
Approaches to Fault Diagnosis and Decision Making for Large Oil-
Immersed Power Transformers: A Survey[J]. Energies, 2018, 11(4):913.
Ma Y, Wei W, Zhou C. Research on Body Mass Estimation Method
of Koi Broodstock Base on Feeding State Image Recognition Technol-
ogy[J]. Journal of Physics: Conference Series, 2020, 1631(1):12138.
Maity S, Chakrabarti A, Bhattacharjee D. Robust Human Action Recog-
nition Using AREI Features and Trajectory Analysis from Silhouette
Image Sequence[J]. Iete Journal of Research, 2018, 65(2):1–14.
Jin S, Fan D, Malekian R, et al. An image recognition method for gear
fault diagnosis in the manufacturing line of short filament fibres[J].
Insight: Non-Destructive Testing and Condition Monitoring, 2018,
(5):270–275.
Wu Y, Fu Z, Fei J. Fault diagnosis for industrial robots based on a
combined approach of manifold learning, treelet transform and Naive
Bayes[J]. Review of entific Instruments, 2020, 91(1):015116.
Biswasa R, Gonz ́alez-Castroa V, Fidalgoa E, Enrique A. Perceptual
image hashing based on frequency dominant neighborhood struc-
ture applied to Tor domains recognition[J]. Neurocomputing, 2020,
(8):24–38.
Darong H, Lanyan K, Xiaoyan C, et al. Fault diagnosis for the motor
drive system of urban transit based on improved Hidden Markov
Model[J]. Microelectronics Reliability, 2018, 82(MAR.):179–189.
Zheng L, Yan P, Chen F. Power Grid Fault Diagnosis Method Based on
Stacked Sparse Denoising Auto-Encoder and GRU Network[J]. Journal
of Physics: Conference Series, 2020, 1631(1):12114.
Zhang Y, Fan Z, Gao X, et al. A Fault Diagnosis Method of Train
Wheelset Rolling Bearing Combined with Improved LMD and FK[J].
Journal of Sensors, 2019, 2019(18):1–11.
Moshen K, Gang C, Yusong P, et al. Research of Planetary Gear Fault
Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS[J].
Sensors, 2018, 18(3):782.
Yuan C, Peng L, Yuzhuo Z. Parallel processing algorithm for rail-
way signal fault diagnosis data based on cloud computing[J]. Future
Generation Computer Systems, 2018, 88(11):279–283.

