Fault Detection of Vehicle Suspensions

Weispfenning, Thomas; Isermann, Rolf

doi:10.1016/s1474-6670(17)42498-0

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…Hence, in the present study, the advantage and robustness of the MODLEM classification algorithm for fault diagnosis of rotary machinery are illustrated by benchmarking with the J48 algorithm with accuracy and confusion matrix of fault diagnosis of various suspension component faults. As only a few studies were conducted in condition monitoring of vehicle suspension systems [13,14,15], this paper illustrates the robustness of the MODLEM algorithm over the J48 algorithm for fault diagnosis of automobile suspension systems.…”

Section: Introductionmentioning

confidence: 94%

Robust algorithm to learn rules for classification: A fault diagnosis case study

Balaji,

Sugumaran

2023

FME Transactions

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Machine learning algorithms are used for building classifier models. The rule-based decision tree classifiers are popular ones. However, the performance of the decision tree classifier varies with hyperparameter tuning. The optimum hyperparameter values are obtained using either optimization algorithms or trial and error methods. The present study utilizes the MODLEM algorithm to overcome the drawbacks accounted for by decision tree algorithms. Eliminating hyperparameter tuning and producing results closer to standard decision tree algorithms makes MODLEM a robust classification algorithm. The robustness of the MODLEM algorithm is illustrated with the fault diagnosis case study. The case study is faults diagnosis of an automobile suspension system using vibration signals acquired at various fault conditions.

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Section: Introductionmentioning

confidence: 94%

Robust algorithm to learn rules for classification: A fault diagnosis case study

Balaji,

Sugumaran

2023

FME Transactions

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“…Placing the accelerometer over the lower arm will increase the chances of acquiring vibration signals from different suspension components. 13,14 Next, the output of the accelerometer was fed to NI9234 DAQ (data acquisition tool) with NI DAQ-9174 USB Chassis which is connected to a PC with NI LabVIEW software. The signals were recorded at the rate of 25 kHz for 10,000 steps with 10.26 mV/g sensitivity.…”

Section: Experimental Studiesmentioning

confidence: 99%

“…From the literature study, following research gap has been identified: Most of the research works carried out used vibration analysis to monitor the condition and identify faults in a specific suspension component (Damper). No specific method or research was carried out to identify faults in suspension components like lower arm, tie rod, and knuckle. Diagnosis of faults for every component makes condition monitoring a time-consuming process and requires domain expertise. 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.…”

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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Diagnose und Sensor-Fehlertoleranz aktiver Fahrwerke

Fischer

Fahrdynamik-Regelung

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Fault Detection of Vehicle Suspensions

Cited by 6 publications

References 4 publications

Robust algorithm to learn rules for classification: A fault diagnosis case study

Robust algorithm to learn rules for classification: A fault diagnosis case study

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

Diagnose und Sensor-Fehlertoleranz aktiver Fahrwerke

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