Thomas Weispfenning

Model based fault detection of vehicle suspension and hydraulic brake systems

In modern vehicles, mechatronic systems are increasingly used. To improve reliability, safety and economy, an early recognition of small or drifting faults is becoming increasingly important. After a short introduction to methods of model based fault detection and diagnosis, application examples for fault detection of automotive vehicle suspension and hydraulic brake systems are given.

Fault detection and diagnosis of components of the vehicle vertical dynamics

In this paper, model and signal-based methods for supervision ofsuspension elements of a car are presented. A model-based approach usingparameter estimation is used to determine the current parameters of avehicle suspension. Exploiting the analytical redundancy, different faults,like weak dampers or sensor faults, can be distinguished. For faultdetection the use of parity equations is shown. In addition, a signal-basedapproach is presented which makes a detection of tire pressure losspossible. All presented results were drawn from a test rig or from a drivingcar.

Model-Based Identification of a Vehicle Suspension Using Parameter Estimation and Neural Networks

Abstract A two-step scheme for identification of a vehicle suspension is presented which combines parameter estimation and neural networks for approximation. At first, the parameters of the discrete time transfer function are estimated using a RLS-algonthm. These parameters are nonlinear functions of the physical coefficients, but a direct calculation of these is often not possible or leads to large errors due to the nonlinear amplification of noise. Therefore, to approximate the coefficients, a nonlinear mapping using a RBF network is performed. For training of the network and to test generalization abilities, the coefficients of a vehicle suspension were varied. The study shows that an approximation of the physical coefficients by application of the presented scheme is possible. The method was tested by simulated data and measurements from a test rig at the Technical University of Darmstadt.

Fault Detection of Vehicle Suspensions

Abstract In this paper methods for fault detection and diagnosis of vehicle suspensions are presented. With parameter estimation and parity equations symptoms on the current process status can be extracted. By means of parameter estimation detailed symptoms can be generated, allowing an automatic distinction of different faults. For the automatic classification, neural networks were trained. Parity equations are well suited for a detection of sensor faults, although they do not offer the possibility for a distinction of different faults. All the presented results were obtained with measured data drawn from a test rig and a driving car. The proposed methods are suited for an on-line supervision of the car as well as for technical inspection e.g. in workshops.

MODEL AND SIGNAL BASED SUPERVISION OF COMPONENTS OF CARS

Abstract In this paper methods for fault diagnosis of car components are presented. Model based methods like parameter estimation can be used for an estimation of the process coefficients. By dividing the nonlinear characteristic curve into several sections good estimation results can be achieved and a diagnosis of the shock absorber be performed. Signal spectra estimation for detection of tire pressure loss requires only an accelerometer to measure the vertical acceleration of the wheel. The presented results were obtained with measured data drawn from a test rig and a driving car. The proposed methods are suited for an on-line supervision of the car as well as for technical inspection e.g. in workshops.

Model Based Fault Detection of Vehicle Suspension and Hydraulic Brake Systems

Abstract In modern vehicles, mechatronic systems are increasingly used. To improve reliability, safety and economy, an early recognition of small or drifting faults is becoming increasingly important. After a short introduction to methods of model based fault detection and diagnosis, application examples for fault detection of automotive vehicle suspension and hydraulic brake systems are given.