Modern suspension systems for automotive vehicles and their test methods

Konieczny, Łukasz; Burdzik, Rafał

doi:10.21595/vp.2017.19238

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…Luis Carvalho et al suggested a technique based on computing the ratio of signals acquired in frequency domain for both fault and healthy conditions to assess the condition of the tie rod ball joint used in the automobile suspension. 10 From the above literatures one can understand that these type of data-driven methods required domain expertise and specific laboratory setup (simulation platform) to perform fault diagnosis. So nowadays researchers shift their focus toward machine learning based on data-driven approach as an alternative.…”

Section: Introductionmentioning

confidence: 99%

“…13 Most of the research compares model-based methods to assess the condition of the component with a mathematical model. Identifying suitable parameters to design mathematical models for each component is tedious and difficult. Limited numbers of literature were reported that use machine learning techniques in fault diagnosis of a suspension system. Numerous research works on fault diagnosis of suspension system using vibration signal requires vibrating platform to induce forced vibration to the component. 10 Assessment of multiple fault occurrences in the suspension system was not attempted. Considering the aforementioned reasons, one can state that there is a definite need for fault diagnosis in the suspension system. Additionally, the fault diagnosis technique must be nondestructive (without the need for dismantling) and eliminate the need for a vibrating platform.…”

Section: Introductionmentioning

confidence: 99%

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Fault detection of automobile suspension system using decision tree algorithms: A machine learning approach

Balaji

Sugumaran

2023

Proceedings of the Institution of Mechanical Engineers, Part E:

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The study aims to detect multiple faults that are exhibited by suspension system components during prolonged usage. Faults such as strut worn out, strut external damage, strut mount fault, lower arm ball joint fault, lower arm bush worn out and tie rod ball joint fault were considered in this study. A novel approach is proposed in the present study that involves vibration signals and machine learning techniques to identify various suspension system faults. Vibration signals were acquired for different fault conditions (as mentioned above) at three different load conditions by a specially fabricated experimental setup. Statistical features were extracted from the acquired vibration signals from which the most significant features were selected using J48 decision tree algorithm. The selected features were provided as input to the tree-based family of algorithms to determine the best in class classification algorithm for suspension fault diagnosis. The results obtained enumerate that the random forest classifier produces the best classification accuracy for all the load conditions (no load, half load, and full load) with values of 95.88%, 94.88%, and 92.01%, respectively. Finally, the performance of the proposed classification model is compared with other state-of-the-art machine learning classifiers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault detection of automobile suspension system using decision tree algorithms: A machine learning approach

Balaji

Sugumaran

2023

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…There are many studies on the vibration and oscillation of the suspension system using on the vehicle has been done during the last time. Konieczny and Burdzik (2017) introduced suspension system models, including the passive suspension system, the active suspension system, and the semi-active suspension system. This assessment was also commented on by Xue et al (2011) in their paper.…”

Section: Introductionmentioning

confidence: 99%

Study on the sliding mode control method for the active suspension system

Nguyen

2021

Int. J. Appl. Sci. Eng.

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When the vehicle moves, the bump on the road surface is the main factor affecting the vehicle's stability. It produces non-cyclic oscillations and causes a feeling of discomfort and fatigue for passengers. The suspension system of the vehicle is used to regulate and reduce these negative effects. In order to improve vehicle stability and comfort, the active suspension is used to replace the conventional passive suspension system. There are many methods to control the suspension system, each with its advantages and disadvantages. This article focuses on the introduction and establishment of the sliding mode controller for the active suspension system. The results of the study show that when the vehicle is equipped with an active suspension system that is controlled by a sliding mode controller, the values of displacement and acceleration of the sprung mass are significantly reduced. Therefore, the vehicle's stability and comfort are also greatly improved. Besides, the sliding mode controller is very stable to the change of the impact factors. However, this method is more difficult and complex than the use of conventional linear methods.

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“…Dampers have a marked effect on how well tyres follow a road surface. They damp the natural bounce over the road and reduce spring oscillations (Konieczny and Burdzik, 2017). New configurations can be added to the passive suspension system to be a controlled suspension: active; or semi-active.…”

Section: Introductionmentioning

confidence: 99%

Managing the handling–comfort contradiction of a quarter-car system using Kalman filter

Muhammed

Gavrilov

2021

Transactions of the Institute of Measurement and Control

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This article introduces a new approach to manage the “handling–comfort” contradiction of a vehicle suspension system. The idea is based on determining a specific time constant that reflects the percentage of suspension damping. This time constant is defined using the measurement of the sprung mass acceleration and the suspension deflection. A distinction is made between the control unit design for a semi-active suspension system and the control unit design for an active suspension system. The semi-active design is based on two sensors and a Kalman filter (KF), while the active design is based on three sensors and a dual-estimation KF. For active suspension, a third sensor was added to measure the acceleration of the unsprung mass. Simulation is carried out in Simulink and Simscape environments. The results of the proposed approach were compared with the results achieved by the hybrid-hook system. Simulation results showed a better efficiency of the proposed approach in driving safety during a “comfort” situation.

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Modern suspension systems for automotive vehicles and their test methods

Cited by 11 publications

References 11 publications

Fault detection of automobile suspension system using decision tree algorithms: A machine learning approach

Fault detection of automobile suspension system using decision tree algorithms: A machine learning approach

Study on the sliding mode control method for the active suspension system

Managing the handling–comfort contradiction of a quarter-car system using Kalman filter

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