Condition Monitoring of the Dampers in the Railway Vehicle Suspension Based on the Vibrations Response Analysis of the Bogie

Dumitriu, Mădălina

doi:10.3390/s22093290

Cited by 14 publications

(7 citation statements)

References 53 publications

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“…Fault detection by analysis of distance in a diagnostic space made up of selected statistical parameters of acceleration signals is presented in [26]. Dumitriu [4] investigated the possibility of developing a new method for fault detection of a damper in the primary suspension of the railway vehicle, based on the analysis of the vertical vibration's response of the bogie, regarding the RMS accelerations measured/simulated in four reference bogie points. Also Dumitriu in the previous work [5] proposed a method to detect the failure of the damper in the primary suspension of the rail vehicle, based on the analysis of cross-correlation of the vertical accelerations measured on the bogie frame against the two axles.…”

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

confidence: 99%

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%

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 upper and lower limits of the diagnostic parameter were determined, and based on this, the vehicle user would receive information about the upcoming wear and tear and total failure of the bearing. The study in [ 20 ] describes a method for detecting shock absorber damage in the primary suspension of a rail vehicle based on the analysis of vertical vibrations of the bogie. The experimental data obtained were used to determine the normal operating area and damage area of the shock absorber in the primary suspension of the bogie.…”

Section: Related Work and Other Methodsmentioning

confidence: 99%

Diagnosis of the Pneumatic Wheel Condition Based on Vibration Analysis of the Sprung Mass in the Vehicle Self-Diagnostics System

Prażnowski

Mamala

Deptuła

et al. 2023

Sensors

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This paper presents a method for the multi-criteria classification of data in terms of identifying pneumatic wheel imbalance on the basis of vehicle body vibrations in normal operation conditions. The paper uses an expert system based on search graphs that apply source features of objects and distances from points in the space of classified objects (the metric used). Rules generated for data obtained from tests performed under stationary and road conditions using a chassis dynamometer were used to develop the expert system. The recorded linear acceleration signals of the vehicle body were analyzed in the frequency domain for which the power spectral density was determined. The power field values for selected harmonics of the spectrum consistent with the angular velocity of the wheel were adopted for further analysis. In the developed expert system, the Kamada–Kawai model was used to arrange the nodes of the decision tree graph. Based on the developed database containing learning and testing data for each vehicle speed and wheel balance condition, the probability of the wheel imbalance condition was determined. As a result of the analysis, it was determined that the highest probability of identifying wheel imbalance equal to almost 100% was obtained in the vehicle speed range of 50 km/h to 70 km/h. This is known as the pre-resonance range in relation to the eigenfrequency of the wheel vibrations. As the vehicle speed increases, the accuracy of the data classification for identifying wheel imbalance in relation to the learning data decreases to 50% for the speed of 90 km/h.

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“…Modal analysis is typically used to determine infrastructures' mechanical characteristics [21,22]. In the literature, three main modal analysis methods can be found.…”

Section: Of 22mentioning

confidence: 99%

Operational and Analytical Modal Analysis of a Bridge Using Low-Cost Wireless Arduino-Based Accelerometers

Komarizadehasl

Huguenet²,

Lozano

et al. 2022

Sensors

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Arduino-based accelerometers are receiving wide attention from researchers to make long-term Structural Health Monitoring (SHM) feasible for structures with a low SHM budget. The current low-cost solutions found in the literature share some of the following drawbacks: (1) high noise density, (2) lack of wireless synchronization, (3) lack of automatic data acquisition and data management, and (4) lack of dedicated field tests aiming to compare mode shapes from Operational Modal Analysis (OMA) with those of a digital model. To solve these problems, a recently built short-span footbridge in Barcelona is instrumented using four Low-cost Adaptable Reliable Accelerometers (LARA). In this study, the automatization of the data acquisition and management of these low-cost solutions is studied for the first time in the literature. In addition, a digital model of the bridge under study is generated in SAP2000 using the available drawings and reported characteristics of its materials. The OMA of the bridge is calculated using Frequency Domain Decomposition (FDD) and Covariance Stochastic Subspace Identification (SSI-cov) methods. Using the Modal Assurance Criterion (MAC), the mode shapes of OMA are compared with those of the digital model. Finally, the acquired eigenfrequencies of the bridge obtained with a high-precision commercial sensor (HI-INC) showed a good agreement with those obtained with LARA.

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Condition Monitoring of the Dampers in the Railway Vehicle Suspension Based on the Vibrations Response Analysis of the Bogie

Cited by 14 publications

References 53 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

Diagnosis of the Pneumatic Wheel Condition Based on Vibration Analysis of the Sprung Mass in the Vehicle Self-Diagnostics System

Operational and Analytical Modal Analysis of a Bridge Using Low-Cost Wireless Arduino-Based Accelerometers

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