Fuzzy Logic-Based Identification of Railway Wheelset Conicity Using Multiple Model Approach

Saba, Erum; Kalwar, Imtiaz Hussain; Unar, Mukhtiar Ali; Memon, Abdul Latif; Pirzada, Nasrullah

doi:10.3390/su131810249

Cited by 6 publications

(7 citation statements)

References 46 publications

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“…Variation in speed

): In previous similar studies [ 18 , 19 , 20 , 21 , 22 , 23 , 24 ] speed was kept constant to detect the anomalies. However, in real-time scenarios, speed is not constant.…”

Section: Methodsmentioning

confidence: 99%

“…The results demonstrate that the proposed wheel flat index is effective for detecting small flats and estimating their severity. In [ 18 ], multiple models and a fuzzy logic-based technique are introduced for detecting conicity in railway wheelsets. This method indirectly identifies the conicity condition by analyzing the lateral acceleration of the wheelset.…”

Section: Introductionmentioning

confidence: 99%

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Wheel Defect Detection Using a Hybrid Deep Learning Approach

Shaikh

Kalwar

Chowdhry

2023

Sensors

Self Cite

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Defective wheels pose a significant challenge in railway transportation, impacting operational performance and safety. Excessive traction and braking forces give rise to deviations from the intended conical tread shape, resulting in amplified vibrations and noise. Moreover, these deviations contribute to the accelerated damage of track components. Detecting wheel defects at an early stage is crucial to ensure safe and comfortable operation, as well as to minimize maintenance costs. However, the presence of various vibrations, such as those induced by the track, traction motors, and other rolling stock subsystems, poses a significant challenge for onboard detection techniques. These vibrations create difficulties in accurately identifying wheel defects in real-time during operational activities, often resulting in false alarms. This research paper aims to address this issue by using a hybrid deep learning-based approach for the accurate detection of various types of wheel defects using accelerometer data. The proposed approach aims to enhance wheel defect detection accuracy while considering onboard techniques’ cost-effectiveness and efficiency. A realistic simulation model of the railway wheelset is developed to generate a comprehensive dataset. To generate vibration data in various scenarios, the model is simulated for 20 s under different conditions, including one non-faulty scenario and six faulty scenarios. The simulations are conducted at different speeds and track conditions to capture a wide range of operating conditions. Within each simulation iteration, a total of 200,000 data points are generated, providing a comprehensive dataset for analysis and evaluation. The generated data are then utilized to train and evaluate a hybrid deep learning model, employing a multi-layer perceptron (MLP) as a feature extractor and multiple machine learning models (support vector machine, random forest, decision tree, and k-nearest neighbors) for performance comparison. The results demonstrate that the MLP-RF (multi-layer perceptron with random forest) model achieved an accuracy of 99%, while the MLP-DT (multi-layer perceptron with decision tree) model achieved an accuracy of 98%. These high accuracy values indicate the effectiveness of the models in accurately classifying and predicting the outcomes. The contributions of this research work include the development of a realistic simulation model, the evaluation of sensor layout effectiveness, and the application of deep learning techniques for improved wheel flat detections.

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“…Variation in speed

): In previous similar studies [ 18 , 19 , 20 , 21 , 22 , 23 , 24 ] speed was kept constant to detect the anomalies. However, in real-time scenarios, speed is not constant.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wheel Defect Detection Using a Hybrid Deep Learning Approach

Shaikh

Kalwar

Chowdhry

2023

Sensors

Self Cite

View full text Add to dashboard Cite

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“…Then knowing the initial displacement y0 and its derivative it is possible to calculate iteratively the solution for each intermediate n th step yn according to (5) (5)…”

Section: Direct Integrationmentioning

confidence: 99%

“…Recent studies prove a strong correlation between measured conicities and measured lateral accelerations [4] so this contactless measurement is strongly correlated to ride dynamics quality. More generally even very recent publications [5][6] still consider the evaluation of equivalent conicity as a fundamental parameter to properly investigate state and stability of railway vehicles Equivalent conicity cannot be directly measured since it's a derived from the solution of equation ( 2) that can be performed only if the function Δr(y)is known from the measurement of both wheel and rail profiles. For this reason, the typical measurement layout described in figure 2 and adopted by Italcertifer is composed by the following elements:  A laser scanner system able to measure accurately both rail profiles.…”

mentioning

confidence: 99%

Estimation of Equivalent Conicity from Contactless Measurements of Wheel and Rail Profiles

Pugi

Bocciolini²,

Piccioli³

et al. 2022

J. Phys.: Conf. Ser.

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Measurement of Equivalent Conicity is quite important index to properly evaluate geometric quality of rails and wheels. The way in which this index is evaluated is evaluated is described by the standard EN 15302 from online contactless measurement of rail profiles performed by a dedicated measurement coach. The numerical treatment of large amount of data derived by the acquisition of a huge number of measured rail profiles should be performed with great accuracy but also with a great numerical efficiency to assure a near to real time evaluation of performed analysis. In this work authors describe an efficient procedure developed using open-source software that has been developed to meet accuracy, efficiency, and portability specifications. Paper investigates how different integration and smoothing should affect efficiency and accuracy of performed operations. Proposed approach is first validated on numerical synthetic inputs and then validated on experimental data, also comparing performances of the proposed software with a commercial one. Results are encouraging, demonstrating the feasibility of the proposed approach for the realization of flexible and efficient tools for Real-Time implementation of the estimation of equivalent conicity for railway applications.

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“…The algorithm proposed in [54] is used for urban ATO operation including uncertainty in the mass, generating optimal energy-time Pareto curves of speed profiles. Fuzzy logic is applied in [81] to detect the railway wheelset conicity as a measure of the deterioration.…”

Section: Introductionmentioning

confidence: 99%

Eco-Driving in Railway Lines Considering the Uncertainty Associated with Climatological Conditions

et al. 2022

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Eco-driving is a keystone in energy reduction in railways and a fundamental tool to contribute to the Sustainable Development Goals in the transport sector. However, its results in real applications are subject to uncertainties such as climatological factors that are not considered in the train driving optimisation. This paper aims to develop an eco-driving model to design efficient driving commands considering the uncertainty of climatological conditions. This uncertainty in temperature, pressure, and wind is modelled by means of fuzzy numbers, and the optimisation problem is solved using a Genetic Algorithm with fuzzy parameters making use of an accurate railway simulator. It has been applied to a realistic Spanish high-speed railway scenario, proving the importance of considering the uncertainty of climatological parameters to adapt driving commands to them. The results obtained show that the energy savings expected without considering climatological factors account for 29.76%, but if they are considered, savings can rise up to 34.7% in summer conditions. With the proposed model, a variation in energy of 5.32% is obtained when summer and winter scenarios are compared while punctuality constraints are fulfiled. In conclusion, the model allows the operator to estimate better energy by obtaining optimised driving adapted to the climate.

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Fuzzy Logic-Based Identification of Railway Wheelset Conicity Using Multiple Model Approach

Cited by 6 publications

References 46 publications

Wheel Defect Detection Using a Hybrid Deep Learning Approach

Wheel Defect Detection Using a Hybrid Deep Learning Approach

Estimation of Equivalent Conicity from Contactless Measurements of Wheel and Rail Profiles

Eco-Driving in Railway Lines Considering the Uncertainty Associated with Climatological Conditions

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