Dieter Filbert

Automated flaw detection in aluminum castings based on the tracking of potential defects in a radioscopic image sequence

Presents a method for inspecting aluminum castings automatically from a sequence of radioscopic images taken at different positions of the casting. The classic image-processing methods for flaw detection of aluminum castings use a bank of filters to generate an error-free reference image. This reference image is compared with the real radioscopic image, and flaws are detected at the pixels where the difference between them is considerable. However, the configuration of each filter depends strongly on the size and shape of the structure of the casting under inspection. A two-step technique is proposed to detect flaws automatically and that uses a single filter. First, the method identifies potential defects in each image of the sequence, and second, it matches and tracks them from image to image. The key idea of the paper is to consider as false alarms those potential defects which cannot be tracked in the sequence. The robustness and reliability of the method have been verified on both real data in which synthetic flaws have been added and real radioscopic image sequences recorded from cast aluminum wheels with known defects. Using this method, the real defects can be detected with high certainty. This approach achieves good discrimination from false alarms.

Advanced fault diagnosis for the mass production of small-power electric motors

High quality is a principal goal in the mass production of electric niotors (i.e. d.c. motors for cars and universal motors for house hold appliances).The processing of vibration and acoustical signals are widely used in quality assurance in the mass production but the coupling of the sensors to the motor as well as noise produced in the environment make it still difficult to get reproducible diagnostic results. High quality in production can be achieved by the powerful modern diagnostic methods which became possible because of the progress in microelectronics (microprocessors and signal processors). This progress made mathematical methods and signal processing applicable. Therefore this paper deals with diagnostic methods that use the measured signals of voltage, current and speed only but achieve a good testing. It gives an overview of new methods for the feature extraction and fault detection on small power electric motors.

A Misfire Detection Method Using Nonlinear Moving Polynomial Filtering

Abstract The OBD II regulations require for 2002 model year cars a misfire identification rate of 100%. Actual misfire detection methods base on speed measuring but problems remain for high speed and low torque. This paper deals with another approach using knock sensors. Their signal contains strong peaks caused by the pressure distribution. A nonlinear polynomial filter is developed to extract and emphasize these peaks and to design a matched filter for the misfire detection purpose. The efficiency of this method is shown using real knock sensor data of a four-cylinder engine.

Automatische Gussfehlererkennung: Stand der Technik (Automated Quality Control of Castings: State of the Art)

In der Automobilindustrie gibt es Leichtmetallgussteile, die als sicherheitsrelevant gelten. Die Qualitätskontrolle von Gussteilen erfolgt mit Hilfe der Röntgendurchleuchtungsprüfung. Ihre Aufgabe ist die Untersuchung auf Gussfehler, die sich im Innern des Teiles befinden und somit von außen micht visuell zu erfassen sind. Seit einigen Jahren werden Röntgenprüfanlagen mit Bildverarbeitung in der Automobilindustrie eingesetzt. In diesem Beitrag wird eine Zusammenfassung der existierenden Detektionsansätze zur automatischen Gussfehlererkennung präsentiert.

Fault Diagnosis on Bearings of Electric Motors by Estimating the Current Spectrum

Abstract The current spectrum of a motor with a faulty bearing contains the rotation frequency and its higher harmonics as well as narrow sidebands produced by frequency and amplitude modulations. A physical model is developed to predict the current spectrum of an universal motor with a faulty bearing. Numerical simulations are compared with measu-rements and the results are used to find significant features for the classification. To classify faulty and faultless motors an algorithm for the extraction of these significant features is introduced. For the feature extraction a model based estimation approach (Prony’s method) is presented.

Unsupervised Classification of Universal Motors Using Modern Clustering Algorithms

Abstract One problem for the classification of universal motors is the definition of the fault classes. Today human experts have the task to make this division, but their results are rather subjective. This paper deals with an alternative way of forming clusters. Different methods for the cluster analysis are presented and the problem of detecting the correct number of clusters is discussed. Statistical criteria are used to test the cluster validity. A practical application is introduced to show the abilitys and limitations of cluster analysis. The required features are extracted from the motor current only. A comparison between the created clusters and the results of the human experts is carried out.

Parameter Estimation and Residual Analysis A Comparison

Abstract A demand of a diagnosis system for quality control of fractional horsepower d.c. motors is the objective and reliable fault detection and isolation. The paper describes the two diagnostic methods i.e. parameter estimation and residual analysis for these special application. Both techniques will be introduced and compared. For residual analysis different procedures are used. Due to a short decision time the speed measurement of the test objects is sometimes undesired. Therefore special attention will be directed to an execution of the methods without speed measurement. At the end evaluations of measurements are described.

Some aspects of knowledge-based fault diagnosis in electronic devices

Abstract The computer-aided fault diagnosis of electronic devices requires the acquisition of different kinds of information — i e, for the diagnosis strategy, measurement tasks and documentation purposes. This paper describes the application of knowledge-based methodologies to support the acquisition process. Major points of consideration will be the application of an inductive learning method for the diagnosis strategy and the implementation of a PROLOG-based consultation system for the generation of measurement instrument settings.

Fault Diagnosis of Electric Low-Power Motors by Analyzing the Current Signal

Abstract To detect and localize faults occurring in the rotor of universal motors analysis of the current signal in the time and frequency domain is presented. The information about the condition of the rotor is contained mainly in the periodical components of the current signal. Because these harmonics are speed dependent and the speed is not constant, digitizing synchronously with speed is carried out. The method of linear prediction is used as a spectral modeling technique in which the current spectra are modeled by all-pole spectra. The coefficients of the linear predictors are the basis for automatic fault detection and classification.

Intelligent measurement methods in technical diagnosis and quality assurance - a comparison

Abstract Faults in technical systems cannot be measured directly. Thus, a variety of intelligent measurement methods has been developed to detect faults. Two widely used procedures-the vibration analysis and the parameter estimation method, and their applications to technical diagnosis - will be described and compared. The paper deals with the application of the vibration analysis to the fault detection in a gear and the application of the parameter estimation to the fault detection in an electric drive system. The paper shows that faults in auxiliary systems canbe detected better by the vibration analysis, whereas faults in the drive system can be detected by the parameter estimation method. A combination of both methods provides better test results than conventional procedures.