Slimani et al., 2024 - Google Patents
Machine Learning Approach for Classification of Faults in Cable via Compressed Sensing Time-Domain ReflectometrySlimani et al., 2024
- Document ID
- 17003812761347386247
- Author
- Slimani H
- Gargouri Y
- Mboula F
- Ravot N
- Publication year
- Publication venue
- 2024 Prognostics and System Health Management Conference (PHM)
External Links
Snippet
In this paper, a new method for cable diagnostics is proposed based on compressed acquisition of Orthogonal Multi-tone Time Domain Reflectometry (OMTDR). In this study, the Random Demodulator (RD) is used to sample the reflected signals, and we propose a …
- 238000002310 reflectometry 0 title abstract description 19
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/28—Testing of electronic circuits, e.g. by signal tracer
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means
- G01N27/72—Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means by investigating magnetic variables
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means
- G01N27/02—Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means by investigating the impedance of the material
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0031—General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R29/00—Arrangements for measuring or indicating electric quantities not covered by groups G01R19/00 - G01R27/00
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R33/00—Arrangements or instruments for measuring magnetic variables
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/141—Discrete Fourier transforms
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Sun et al. | Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning | |
Oliveira et al. | Ultrasound-based identification of damage in wind turbine blades using novelty detection | |
Wang et al. | GIS partial discharge pattern recognition via lightweight convolutional neural network in the ubiquitous power internet of things context | |
CN117171696B (en) | Sensor production monitoring method and system based on Internet of things | |
CN116223962B (en) | Method, device, equipment and medium for predicting electromagnetic compatibility of wire harness | |
CN109632973A (en) | A kind of ultrasound echo signal extracting method based on Based on Multiscale Matching tracking | |
US11630135B2 (en) | Method and apparatus for non-intrusive program tracing with bandwidth reduction for embedded computing systems | |
CN115165885A (en) | Identification system and method based on machine vision identification and spectral measurement | |
CN114970607A (en) | Transformer partial discharge detection method based on deep neural network acoustic emission signal separation | |
CN117708542A (en) | Equipment fault diagnosis method and system based on deep neural network | |
CN109682892A (en) | A kind of signal based on time frequency analysis removes drying method | |
Al-Greer et al. | Capacity estimation of lithium-ion batteries based on adaptive empirical wavelet transform and long short-term memory neural network | |
Slimani et al. | Machine Learning Approach for Classification of Faults in Cable via Compressed Sensing Time-Domain Reflectometry | |
CN109507292B (en) | A kind of signal extraction method | |
Yuan et al. | A novel fault diagnosis method for second-order bandpass filter circuit based on TQWT-CNN | |
CN118509333A (en) | Radio frequency test network construction and evaluation method and system based on python | |
Aydin et al. | A new fault diagnosis approach for induction motor using negative selection algorithm and its real-time implementation on FPGA | |
CN114088195B (en) | Analysis method, acquisition device, electronic equipment and medium for drilling well site noise | |
Slimani et al. | Detection, Localization and Characterization of Fault in Cable via Machine Learning Approach Based on Compressed Sensing Time-Domain Reflectometry | |
Klock et al. | A new automated energy meter fraud detection system based on artificial intelligence | |
CN116625682A (en) | Asynchronous motor bearing fault diagnosis method and device and storage medium | |
TWI781451B (en) | Apparatus, system and method for obtaining a performance metric of a device under test based on one or more nearfield measurement results, method for determining performance metrics of a device under test based on an over the air test in the nearfield of the device under test with one or more antennas in an automatic test equipment, and related computer program | |
Chao et al. | A Novel Approach to Transformer Fault Diagnosis Based on Transfer Learning | |
Wei et al. | Evaluation method of spindle performance degradation based on VMD and random forests | |
Qian et al. | Ultrasonic signal processing method for transformer oil based on improved EMD |