作者：
H. KonradIsermann R.Technical University of Darmstadt
Institute of Automatic Control Laboratory of Control Engineering and Process Automation Landgraf-Georg-Str. 4 D-64283 Darmstadt Germany Phone: +496151 163927 Fax: +49 6151 293445
A new method of fault detection in milling is described. The method uses exclusively drive signals and is based on models for the feed drive and the milling process. Using parameter estimation features are generated w...
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A new method of fault detection in milling is described. The method uses exclusively drive signals and is based on models for the feed drive and the milling process. Using parameter estimation features are generated which are independent of cutting conditions. A subsequent classifier evaluates the process state and provides a reliable diagnosis of the milling process.
作者：
H. KonradTechnical University of Darmstadt
Institute of Automatic Control Laboratory of Control Engineering and Process Automation Landgraf-Georg-Str.4 D-64283 Darmstadt Germany Phone: +49 6151 163927 Fax: +49 6151 293445
In this paper a new method of fault detection in milling is reported. Based on measured cutting forces, model parameters are estimated for each insert of the milling cutter. Using a classifier, the patterns of these e...
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In this paper a new method of fault detection in milling is reported. Based on measured cutting forces, model parameters are estimated for each insert of the milling cutter. Using a classifier, the patterns of these estimated parameters are processed further and the state of the milling process is determined. The method is first tested with simulated data and then verified with measurements on a machining center.
As individual and commercial traffic flow on roads and highways grows enormously, rhe number of accidents increases as well. Therefore, modern vehicle research is focused on improving driving comfort as well as passen...
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As individual and commercial traffic flow on roads and highways grows enormously, rhe number of accidents increases as well. Therefore, modern vehicle research is focused on improving driving comfort as well as passengers' safety. Aiming at that, recent advances in controlengineering and modern computer technology enable the engineer to design special control and supervision systems supporting the driver. Exemplary, this contribution presents two possible solutions. On the one hand, an Adaptive Cruise control system which assists the driver during highway traffic, whereas a vehicle supervision method is applied to detect critical driving situations and sensor faults.
Due to the rising consciousness of safety aspects the supervision of vehicles' tire pressure is a major effort to improve active car safety. Therefore, in this contribution a method for monitoring the tire pressur...
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Due to the rising consciousness of safety aspects the supervision of vehicles' tire pressure is a major effort to improve active car safety. Therefore, in this contribution a method for monitoring the tire pressure is presented using body acceleration signals. Analysing the frequency spectrum of the virtual transfer function between the body acceleration at the front and the rear wheel of one side of the vehicle characteristic features are generated. Thereby, external interferences to the spectrum and their influences to the symptoms are discussed. Then, a neuro-fuzzy classification of the characteristics is applied to quantify the tire pressure.
High-accuracy positioning is applied in a variety of modern computer-controlled machines. The achievable precision is not only determined by the mechanical properties of the systems but strongly depends on the utilize...
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High-accuracy positioning is applied in a variety of modern computer-controlled machines. The achievable precision is not only determined by the mechanical properties of the systems but strongly depends on the utilized control algorithms and the quality of the sensor signals. The objective of this paper is to demonstrate how alternative position sensors influence the performance of a robust digital tracking controller consisting of a disturbance observer in the velocity loop, a feedback controller in the position loop, and a zero phase error tracking controller as feedforward controller. Two different sensor systems for an x-y positioning table are considered. While a digital encoder is attached to the actuating motor, a laser interferometer with a significantly higher resolution directly measures the position of the compliantly coupled table. The latter case represents a noncollocated system. After introducing the hardware setup, both the system identification and the controller design are briefly reviewed. The impact of the measurement device on the control performance and the optimal choice of the controller parameters are investigated in extensive experiments.
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 ...
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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.
Rising demands in automotive development and strict emission standards enforce the application of modern conrrol and supervision strategies to combustion engines. This contribution shows the shaping and adaption of mo...
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Rising demands in automotive development and strict emission standards enforce the application of modern conrrol and supervision strategies to combustion engines. This contribution shows the shaping and adaption of model based fault detection and direct signal analysis methods when applied to a turbocharged diesel engine. First a real lime supervision of fuel mass and injection angle based on dynamic cylinder pressure measurement is described. This is followed by a method for engine misfire detection using only a low resolution crankshaft speed signal. Then fault detection for a diesel engine turbocharger with nonlinear neural networks is proposed. Finally the results of a diagnosis of multiple faults with a neural network are presented. All methods have been implemented and tested experimentally on a dynamical engine test stand at the Technical University of Darmstadt.
作者：
Oliver NellesRolf IsermannTechnical University of Darmstadt
Institute of Automatic Control Laboratory of Control Engineering and Process Automation Landgraf-Georg-Str. 4 D-64283 Darmstadt Germany Phone: +49/6151/16-4524 Fax: +49/6/5//293445
Radial basis function (RBF) networks are often used for identification of nonlinear dynamic systems. The main reason why RBF networks are so successful is that the hidden layer parameters can be fixed in a very reason...
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Radial basis function (RBF) networks are often used for identification of nonlinear dynamic systems. The main reason why RBF networks are so successful is that the hidden layer parameters can be fixed in a very reasonable way and only the weights are optimized by a standard least squares technique. Thus for the case of Gaussian RBFs a good choice of the centers and standard deviations is crucial for good network performance. We show that Ihe most widely used clustering approach has many drawbacks. An alternative technique for center determination is presented, that is not completely unsupervised but exploits error information. It is based on a fusion of linear parameter estimation and the RBF network. First, a linear system is estimated from data. Then only the nonlinear part is approximated by an RBF network.
Predictive control strategies have been proved effective and robust in practice. These algorithms were developed mainly for linear processes. The main idea is to determine the control signal minimising the deviation b...
Predictive control strategies have been proved effective and robust in practice. These algorithms were developed mainly for linear processes. The main idea is to determine the control signal minimising the deviation between a reference signal and the predicted process output in a given prediction horizon. A simplified version is one-step-ahead predictive control, where prediction horizon is restricted to one future point. Extended horizon control proposed by Ydstie (1984) is enlengthened one-step-ahead control which lets more time for the settling process than the dead time. Extended horizon predictive control strategy is derived here for a class of nonlinear systems. Some simulation results show the effect of the tuning parameters for the reference signal tracking performance of the nonlinear system.
In order to apply the best fault-detection and diagnosis scheme, it is required to investigate the process model profoundly and the kinds of faults to be detected. Especially, the process excitation and the effect of ...
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In order to apply the best fault-detection and diagnosis scheme, it is required to investigate the process model profoundly and the kinds of faults to be detected. Especially, the process excitation and the effect of the fault being considered play an important role. This is the starting point for the choice of one of the various model-based fault-detection methods. According to this strategy, two different approaches, an observer-based and a signal-based approach, are selected for the two given faults of the benchmark task. It is shown that the use of adaptive thresholds can significantly improve the performance of the fault-detection scheme with respect to the false alarm rate and the delay in detection.
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