Malekjafarian et al., 2023 - Google Patents
A machine-learning-based approach for railway track monitoring using acceleration measured on an in-service trainMalekjafarian et al., 2023
View HTML- Document ID
- 4396585153120836481
- Author
- Malekjafarian A
- Sarrabezolles C
- Khan M
- Golpayegani F
- Publication year
- Publication venue
- Sensors
External Links
Snippet
In this paper, a novel railway track monitoring approach is proposed that employs acceleration responses measured on an in-service train to detect the loss of stiffness in the track sub-layers. An Artificial Neural Network (ANN) algorithm is developed that works with …
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
- G06Q10/0639—Performance analysis
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
-
- 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
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
- G06F17/5018—Computer-aided design using simulation using finite difference methods or finite element methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/22—Health care, e.g. hospitals; Social work
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
- G06Q10/105—Human resources
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
- G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Mosleh et al. | Railway vehicle wheel flat detection with multiple records using spectral kurtosis analysis | |
Pintão et al. | Development and validation of a weigh-in-motion methodology for railway tracks | |
Pourzeynali et al. | Comprehensive study of moving load identification on bridge structures using the explicit form of Newmark-β method: Numerical and experimental studies | |
Yang et al. | Feasibility study of tractor-test vehicle technique for practical structural condition assessment of beam-like bridge deck | |
Malekjafarian et al. | Railway track loss-of-stiffness detection using bogie filtered displacement data measured on a passing train | |
Dižo et al. | Evaluation of ride comfort in a railway passenger car depending on a change of suspension parameters | |
Milosevic et al. | Condition monitoring of railway crossing geometry via measured and simulated track responses | |
Carnevale et al. | A feasibility study of the drive-by method for damage detection in railway bridges | |
Gou et al. | Experimental study on dynamic effects of a long-span railway continuous beam bridge | |
Malekjafarian et al. | A machine-learning-based approach for railway track monitoring using acceleration measured on an in-service train | |
Guedes et al. | Detection of wheel polygonization based on wayside monitoring and artificial intelligence | |
Malekjafarian et al. | Indirect monitoring of frequencies of a multiple span bridge using data collected from an instrumented train: a field case study | |
Vale | Wheel flats in the dynamic behavior of ballasted and slab railway tracks | |
Ren et al. | Railway bridge condition monitoring using numerically calculated responses from batches of trains | |
Celiński et al. | Research on the applicability of vibration signals for real-time train and track condition monitoring | |
Gonçalves et al. | Wheel out-of-roundness detection using an envelope spectrum analysis | |
Zakharenko et al. | Train classification using a weigh-in-motion system and associated algorithms to determine fatigue loads | |
Souza et al. | Drive-by Methodologies Applied to Railway Infrastructure Subsystems: A Literature Review—Part I: Bridges and Viaducts | |
Song et al. | Evaluating the effect of wheel polygons on dynamic track performance in high-speed railway systems using co-simulation analysis | |
Saramago et al. | Experimental validation of a double-deck track-bridge system under railway traffic | |
Bernardini et al. | Damage identification in warren truss bridges by two different time–frequency algorithms | |
Azim et al. | Development of a novel damage detection framework for truss railway bridges using operational acceleration and strain response | |
de Souza et al. | Feasibility of applying Mel-frequency cepstral coefficients in a drive-by damage detection methodology for high-speed railway bridges | |
Traquinho et al. | Damage Identification for Railway Tracks Using Onboard Monitoring Systems in In-Service Vehicles and Data Science | |
Hashlamon et al. | Numerical parametric study on the effectiveness of the contact-point response of a stationary vehicle for bridge health monitoring |