Marcel Kreidl

Condition Indicators for Gearbox Condition Monitoring Systems

Condition monitoring systems for manual transmissions based on vibration diagnostics are widely applied in industry. The systems deal with various condition indicators, most of which are focused on a specific type of gearbox fault. Frequently used condition indicators (CIs) are described in this paper. The ability of a selected condition indicator to describe the degree of gearing wear was tested using vibration signals acquired during durability testing of manual transmission with helical gears.

Signal-to-noise ratio enhancement based on wavelet filtering in ultrasonic testing.

In ultrasonic non-destructive testing of materials with a coarse-grained structure the scattering from the grains causes backscattering noise, which masks flaw echoes in the measured signal. Several filtering methods have been proposed for improving the signal-to-noise ratio. In this paper we present a comparative study of methods based on the wavelet transform. Experiments with stationary, discrete and wavelet packet de-noising are evaluated by means of signal-to-noise ratio enhancement. Measured and simulated ultrasonic signals are used to verify the proposed de-noising methods. For comparison, we use signal-to-noise ratio enhancement related to fault echo amplitudes and filtering efficiency specific for ultrasonic signals. The best results in our setup were achieved with the wavelet packet de-noising method.

Classification of ultrasonic signals

In ultrasonic non-destructive testing it is very difficult to detect flaws in materials with coarse-grain structure. The ultrasonic signals measured on these materials contain echoes which are very similar to fault echoes. These echoes arise from grains which are contained in the material. For the detection of flaws various methods for suppressing echoes from grains have to be used. In this work we used the method for filtering ultrasonic signals based on discrete wavelet transform. For the classification of ultrasonic signals in A-scan we used a pattern recognition method called support vector machines. In this study we classify signals with fault echoes, echo from weld and back-wall echo. Ultrasonic signals were measured on materials used for constructing aeroplane engines. The experimental results indicate the performance of the proposed approach.

Signal-to-Noise Ratio Improvement based on the Discrete Wavelet Transform in Ultrasonic Defectoscopy

In ultrasonic testing it is very important to recognize the fault echoes buried in a noisy signal. The fault echo characterizes a flaw in the material. An important requirement on ultrasonic signal filtering is zero-time shift, because the position of ultrasonic echoes is essential. This requirement is accomplished using the discrete wavelet transform (DWT), which is used for reducing the signal-to-noise ratio. This paper evaluates the quality of filtering using the discrete wavelet transform. Additional computer simulations of the proposed algorithms are presented.

Localization of the Best Measuring Point for Gearwheel Behaviour Testing Using Group of Adaptive Models Evolution

Localization of the Best Measuring Point for Gearwheel Behaviour Testing Using Group of Adaptive Models Evolution This paper deals with signal processing for vibration analysis of the gear transmission. The main goal was localization of the best measuring point on the gearbox housing, which could provide the highest sensitivity to gearwheels vibration. Vibration measured at some selected points on the gearbox housing was analysed using methods of spectral analysis. The important frequencies in the vibration spectra and a value of their importance were estimated using Group of Adaptive Models Evolution. This work was done within the framework of experimental research concerned with testing and lifetime estimation of new designs of gearwheels. The lifetime of an examined gearwheel, which is mainly related to rise of pitting faults, was assessed by monitoring of a gearwheel vibration level.

Signal Separation in Ultrasonic Non-Destructive Testing

In ultrasonic non-destructive testing the signals characterizing the material structure are commonly evaluated. The sensitivity and resolution of ultrasonic systems is limited by the backscattering and electronic noise level commonly contained in the acquired ultrasonic signals. For this reason, it is very important to use appropriate advanced signal processing methods for noise reduction and signal separation. This paper compares algorithms used for efficient noise reduction in ultrasonic signals in A-scan. Algorithms based on the discrete wavelet transform and the Wiener filter are considered. Part of this paper analyses and applies blind source separation, which has never been used in practical ultrasonic non-destructive testing. All proposed methods are evaluated on both simulated and acquired ultrasonic signals.

Application of the self-organizing map to manual automotive transmission

In recent years, research in the gearbox diagnostics has been done on the computation of threshold values for existing condition features, enabling the use of simple classification methods. This paper describes the application of advanced classification methods in gearbox diagnostics. Time domain signal evaluation is transformed to a vector classification problem. A vector composed of three amplitude features (the root mean square, skewness and kurtosis) of the synchronously averaged vibration signal, is computed for each tested gearbox. The classification is based on the self-organizing feature map algorithm (Kohonen neural network). A database containing vibration signals from four manual automotive transmissions has been used to test the performance of the proposed system. The results obtained using this approach, demonstrate the ability to discriminate among various types of fault.

System for 3D Visualization of Flaws for Eddy Current Inspection

This paper presents a novel method for 3D visualization of flaws detected during Eddy Current (EC) inspection. The EC data was acquired using an automated scanning system equipped with precise eddy current probe positioning. The method was tested on a single frequency instrument with an absolute probe. The EC inspection procedure is implemented statically by registering the operating point of the instrument at each equidistant point on a tested object.The paper describes a data processing method based on the Fourier transform enabling 3D visualization of flaws. This three-dimensional image of the result of a scan enables the position of flaws to be determined, and the size and bevel (angle to the surface) of each detected flaw to be estimated. This research investigated flaws rising from the surface of the tested object, and flaw depth was not evaluated in this work. This method of visualization is simple to implement and is currently targeted for application in EC scanning devices.

EMAT APPLIED IN THE AVIATION INDUSTRY

The goal of this paper is to show the result of the real measurement with EMAT system in the aviation industry. Common nondestructive evaluation (NDE) techniques used in the field are the eddy current and the ultrasound inspection. EMAT (Electro-Magnetic Acoustic Transducers) system is a combination of these two methods and exhibit an advantage in noncontact principle of the eddy current and detection depth of the ultrasound NDE. In the cooperation with the main Czech aircraft producer Aero Vodochody Czech Republic, the main Czech aircraft service company LOM Praha s.p. and Czech Airlines Company Department of Quality control stressed parts of aircrafts were analyzed. The samples from these companies cover significant part of currently used civil and military aircrafts. EMAT signals acquired on these samples were loaded with high level of noise. The advanced signal processing method was applied when it was necessary to enhance signal-to-noise ratio and to improve the flaw detection.

Flaw Shape Characterization in 3D Volume Visualization in Ultrasonic Testing

In general the ultrasonic testing is used for flaw detection and localization in tested materials. The flaws in materials are commonly characterized in measured ultrasonic signals by faults echoes. In the case of materials with coarse-grained structure the ultrasonic signal contains undesirable echoes caused by the scattering from these grains considered as noise. Another source of noise is caused by the electronic circuitry which is called electronic noise. These sources of noise make the flaw detection more difficult. The efficient methods for noise reduction have to be used. This paper evaluates different filtering methods used for noise reduction. Methods based on Wiener filtering, stationary wavelet transform, neural networks in 2D space are compared with conventional mathematical filtering methods based on median filter and mean value. The goal of this paper is to determine efficient method for noise reduction in B-scan for the exact flaw shape characterization. Filtered B-scans are used for the construction of 3D-volume visualization of flaws in the tested materials. Final 3D-volume visualization represents the flaw shape and is useful in the case of characterization of the flaw visualization