Fusion of Model-Based and Data Driven Based Fault Diagnostic Methods for Railway Vehicle Suspension

Ankrah, Abdulai Ayirebi; Kimotho, James Kuria; Muvengei, Onesmus

doi:10.4236/jilsa.2020.123004

Cited by 6 publications

(6 citation statements)

References 21 publications

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“…Karlsson et al [15] computed frequency response functions among acceleration signals in the carbody, bogie frames and axles, which served as fault indicators, fed to the classification algorithms -the linear Support Vector Machine and 1-Nearest-Neighbour. Ankrah [1] built a supervised machine learning model to predict faulty and healthy state of the suspension system components, based on support vector machine (SVM). They also developed a new SVM model to predict faults on the test data containing acceleration obtained from simulation scenarios.…”

Section: Rail Vehicles' Suspension Fault Detection Methods -State Of ...mentioning

confidence: 99%

“…The model consists of quarter of a body mass 𝑚 𝑏 = 7500 𝑘𝑔 (unloaded) connected with half of a wheelset of mass 𝑚 𝑤 = 1196 𝑘𝑔 by means of a spring and a dashpot with constants 𝑘 = 4.1 • 10 6 𝑁/𝑚 and 𝑐 = 28 • 10 3 • 𝑁𝑠/𝑚 respectively [40]. Frequency response of the body mass acceleration (acceleration gain) is derived analytically basing on the equation of motion (1). The notation is adopted from [27].…”

Section: Preliminary Analysis Of Damper Failurementioning

confidence: 99%

“…1. Quarter rail vehicle model (bogie-less).Frequency response of the body mass acceleration (acceleration gain) is derived analytically basing on the equation of motion(1). The notation is adopted from[27].𝑚 𝑏 𝑧̈+ 𝑐𝑧̇+ 𝑘𝑧 = 𝑐ℎ ̇+ 𝑘ℎ (1) Harmonic excitation ℎ of the wheel mass 𝑚𝑤 and response 𝑧 of body mass 𝑚𝑏 are given by equation (2): ℎ = ℎ ̂𝑒𝑗𝜔𝑡 , 𝑧 = 𝑧ê 𝑗𝜔𝑡 (2) where ℎ hat and 𝑧 hat are complex amplitudes.…”

mentioning

confidence: 99%

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Feasibility study of a rail vehicle damper fault detection by artificial neural Indexed by: networks

Melnik¹,

Koziak²,

Dižo³

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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The aim of the study was to investigate rail vehicle dynamics under primary suspension dampers faults and explore possibility of its detection by means of artificial neural networks. For these purposes two types of analysis were carried out: preliminary analysis of 1 DOF rail vehicle model and a second one - a passenger coach benchmark model was tested in multibody simulation software - MSC.Adams with use of VI-Rail package. Acceleration signals obtained from the latter analysis served as an input data into the artificial neural network (ANN). ANNs of different number of hidden layers were capable of detecting faults for the trained suspension fault cases, however, achieved accuracy was below 63% at the best. These results can be considered satisfactory considering the complexity of dynamic phenomena occurring in the vibration system of a rail vehicle.

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Section: Rail Vehicles' Suspension Fault Detection Methods -State Of ...mentioning

confidence: 99%

Section: Preliminary Analysis Of Damper Failurementioning

confidence: 99%

mentioning

confidence: 99%

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Feasibility study of a rail vehicle damper fault detection by artificial neural Indexed by: networks

Melnik¹,

Koziak²,

Dižo³

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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“…Therefore, data-driven damage recognition has gradually become an effective method in engineering systems. Ankrah et al [22] built a supervised machine learning model to predict faulty and healthy states of suspension system components based on a support vector machine method by using vertical accelerations of the railway vehicle. Janssens et al [23] developed a feature-learning system based on convolutional neural networks (CNNs) for bearing fault detection.…”

Section: Introductionmentioning

confidence: 99%

Ballasted Track Behaviour Induced by Absent Sleeper Support and its Detection Based on a Convolutional Neural Network Using Track Data

Zhang

Tian

et al. 2023

Urban Rail Transit

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With development of the heavy-haul railway, the increased axle load and traction weight bring a significant challenge for the service performance and safety maintenance of the railway track. Conducting defect recognition on concrete sleepers and ballast using big data is vital. This paper focused on the detection of absent sleeper support in a ballasted track with an emphasis on the integration of model-based and data-driven methods. To this end, a mathematical model consisting of the wagon, track and wheel–rail contact subsystems was first established to acquire the necessary raw data for the data-driven method, in which the wagon was regarded as a 47-degree-of-freedom multi-body subsystem, and the track was treated as a multi-layer discrete-elastic support beam subsystem with absent sleeper support. Then, an architectural hierarchy of a three-layer convolutional neural network (TLCNN) was developed, which includes three convolutional layers and two pooling layers, and a method for reconstructing one-dimensional sleeper vertical displacement to a two-dimensional time–space matrix was also proposed. Thirdly, verification was carried out by comparing the simulation and experimental results to illustrate the accuracy and reliability of the mathematical model, and the dynamic behaviour of the track with absent sleeper support was investigated. Lastly, the established TLCNN was used to train the raw data of the sleeper vertical displacement and detect the existence of absent sleeper support. Results show that the integration of model-based and data-driven methods was a reliable and effective approach for the detection of absent sleeper support. The proposed TLCNN can acquire and extract robust characteristics in a noisy environment. To handle more complex recognition tasks and further improve performance, deeper CNN models and larger sample sizes should be preferentially considered in practical applications.

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“…-Ankrah et al [42] investigated the application of KNN, Naïve Bayes, ensemble methods, and linear SVM for fault detection in railway suspensions. -Acceleration signals were used for classification, and the system was classified into three different classes.…”

mentioning

confidence: 99%

Transfer Learning Based Fault Detection for Suspension System Using Vibrational Analysis and Radar Plots

et al. 2023

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The suspension system is of paramount importance in any automobile. Thanks to the suspension system, every journey benefits from pleasant rides, stable driving and precise handling. However, the suspension system is prone to faults that can significantly impact the driving quality of the vehicle. This makes it essential to find and diagnose any faults in the suspension system and rectify them immediately. Numerous techniques have been used to identify and diagnose suspension faults, each with drawbacks. This paper’s proposed suspension fault detection system aims to detect these faults using deep transfer learning techniques instead of the time-consuming and expensive conventional methods. This paper used pre-trained networks such as Alex Net, ResNet-50, Google Net and VGG16 to identify the faults using radar plots of the vibration signals generated by the suspension system in eight cases. The vibration data were acquired using an accelerometer and data acquisition system placed on a test rig for eight different test conditions (seven faulty, one good). The deep learning model with the highest accuracy in identifying and detecting faults among the four models was chosen and adopted to find defects. The results state that VGG16 produced the highest classification accuracy of 96.70%.

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Fusion of Model-Based and Data Driven Based Fault Diagnostic Methods for Railway Vehicle Suspension

Cited by 6 publications

References 21 publications

Feasibility study of a rail vehicle damper fault detection by artificial neural Indexed by: networks

Feasibility study of a rail vehicle damper fault detection by artificial neural Indexed by: networks

Ballasted Track Behaviour Induced by Absent Sleeper Support and its Detection Based on a Convolutional Neural Network Using Track Data

Transfer Learning Based Fault Detection for Suspension System Using Vibrational Analysis and Radar Plots

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