Modeling, diagnosis and estimation of actuator faults in vehicle suspensions

Hernández-Alcantara, Diana; Tudón-Martínez, Juan C.; Amézquita-Brooks, Luis; Vivas-Lopez, Carlos A.

doi:10.1016/j.conengprac.2015.12.002

Cited by 34 publications

(16 citation statements)

References 26 publications

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“…These resolutions propelled the creation of a system capable of operating self-diagnosis and alert the driver to possible defects in electronic components or in the emission control systems of the vehicle [ 35 ]. The system was called OBD-II and its first version was launched in 1988, when it became mandatory in all vehicles that would circulate in the state of California from that year forward [ 36 ]. Since 1996, all vehicles manufactured and marketed in the United States are required to have the OBD-II system.…”

Section: Related Workmentioning

confidence: 99%

A Customer Feedback Platform for Vehicle Manufacturing Compliant with Industry 4.0 Vision

Silva

Vieira

Signoretti

et al. 2018

Sensors

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In the last decade, the growth of the automotive market with the aid of technologies has been notable for the economic, automotive and technological sectors. Alongside this growing recognition, the so called Internet of Intelligent Vehicles (IoIV) emerges as an evolution of the Internet of Things (IoT) applied to the automotive sector. Closely related to IoIV, emerges the concept of Industrial Internet of Things (IIoT), which is the current revolution seen in industrial automation. IIoT, in its turn, relates to the concept of Industry 4.0, that is used to represent the current Industrial Revolution. This revolution, however, involves different areas: from manufacturing to healthcare. The Industry 4.0 can create value during the entire product lifecycle, promoting customer feedback, that is, having information about the product history throughout it is life. In this way, the automatic communication between vehicle and factory was facilitated, allowing the accomplishment of different analysis regarding vehicles, such as the identification of a behavioral pattern through historical driver usage, fuel consumption, maintenance indicators, so on. Thus, allowing the prevention of critical issues and undesired behaviors, since the automakers lose contact with the vehicle after the purchase. Therefore, this paper aims to propose a customer feedback platform for vehicle manufacturing in Industry 4.0 context, capable of collecting and analyzing, through an OBD-II (On-Board Diagnostics) scanner, the sensors available by vehicles, with the purpose of assisting in the management, prevention, and mitigation of different vehicular problems. An intercontinental evaluation conducted between Brazil and Italy locations shown the feasibility of platform and the potential to use in order to improve the vehicle manufacturing process.

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Section: Related Workmentioning

confidence: 99%

A Customer Feedback Platform for Vehicle Manufacturing Compliant with Industry 4.0 Vision

Silva

Vieira

Signoretti

et al. 2018

Sensors

View full text Add to dashboard Cite

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“…As nonparametric models usually stand for quite intricate, composite functions, they are out of the scope of this paper because they are too complex to be used for control design purposes, not being able to run in a realtime/embedded ECU (in the context of 1-10 ms). • Parametric models characterize the delivered force by the combination of the behaviours of the spring, dashpots and other mechanical parts, considering each of their mechanisms and operating area [17]: Gamota and Filisko [24] describe the ER fluid behaviour in moderate frequency excitations; Kamath and Wereley [36] and Pellegrini et al [57] present highly complex models derived from mechanical laws; more recently, Hernández-Alcántara et al [30] presented strong experimental results that even including faulty situations of the damper, but the parametric model is simply adapted from an MR model from [29].…”

Section: Er Modelling Workmentioning

confidence: 99%

Development of a simple ER damper model for fault-tolerant control design

Morato

Pham

Sename

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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This work presents a concise control-oriented model for electro-rheological (ER) dampers. This model can serve for faulttolerant control purposes, considering automotive suspension performance enhancement. ER dampers present, basically, a resistance against shearing that varies according to a controlled electric field. The main purpose of this work is to describe the force dynamics delivered by these ER dampers with a simple/reduced-order model that catches its overall behaviour with accuracy. One of the key points in the proposed approach is to describe the dynamics of the controlled portion of the damper force as a first-order system. Synthetically, this study is twofold: (1) the first part is an analytical approach towards the dynamic modelling of the ER damper force, wherein a reduced-order model is obtained, parameters are identified, and validation results are presented; (2) the second analyses the possible faults on these dampers and incorporates their affect to the developed model, which is of paramount importance for diagnosis and reliability of suspension systems. Hence, the proposed model is adequate for the design and synthesis of fault detection and diagnosis/fault-tolerant control (FDD/FTC) schemes, being able to run fast in real time in embedded electronic control units, that usually operate within 1-10 ms. Throughout this study, simulation and experimental validation tests are performed on a real 1/5-scaled vehicle testbed. Model parameters are identified via relatively simple procedures performed in this testbed. Results are shown to illustrate how the model can be used for FDD and FTC of semi-active suspension systems. The overall results assess the ability and the accuracy of the proposed model to characterize the force delivered by ER dampers in both healthy and faulty conditions.

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“…Figure 1 shows the characteristic curve of an intact and defective damper for each axle. Even though there is a great variety of different damper defects and related consequences, simulating defective dampers by changing damper currents is a reasonable approach according to [6,39]. Figure 2 visualizes the overall classification process.…”

Section: Description Of the Datasetmentioning

confidence: 99%

Diagnosing Automotive Damper Defects Using Convolutional Neural Networks and Electronic Stability Control Sensor Signals

Zehelein

Hemmert-Pottmann

Lienkamp

2020

JSAN

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Chassis system components such as dampers have a significant impact on vehicle stability, driving safety, and driving comfort. Therefore, monitoring and diagnosing the defects of these components is necessary. Currently, this task is based on the driver’s perception of component defects in series production vehicles, even though model-based approaches in the literature exist. As we observe an increased availability of data in modern vehicles and advances in the field of deep learning, this paper deals with the analysis of the performance of Convolutional Neural Networks (CNN) for the diagnosis of automotive damper defects. To ensure a broad applicability of the generated diagnosis system, only signals of a classic Electronic Stability Control (ESC) system, such as wheel speeds, longitudinal and lateral vehicle acceleration, and yaw rate, were used. A structured analysis of data pre-processing and CNN configuration parameters were investigated in terms of the defect detection result. The results show that simple Fast Fourier Transformation (FFT) pre-processing and configuration parameters resulting in small networks are sufficient for a high defect detection rate.

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Modeling, diagnosis and estimation of actuator faults in vehicle suspensions

Cited by 34 publications

References 26 publications

A Customer Feedback Platform for Vehicle Manufacturing Compliant with Industry 4.0 Vision

A Customer Feedback Platform for Vehicle Manufacturing Compliant with Industry 4.0 Vision

Development of a simple ER damper model for fault-tolerant control design

Diagnosing Automotive Damper Defects Using Convolutional Neural Networks and Electronic Stability Control Sensor Signals

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