Volume 88, Issue 5 - XXXIV. Messtechnisches Symposium des AHMT 2020 / Gastherausgeber: Peter Lehmann

While the resolving power of drift tube ion mobility spectrometers has been studied and modelled in detail over the past decades, no comparable model exists for the signal-to-noise-ratio. In this work, we develop an analytical model for the signal-to-noise-ratio of a drift tube ion mobility spectrometer based on the same experimental parameters used for modelling the resolving power. The resulting holistic model agrees well with experimental results and allows simultaneously optimizing both resolving power and signal-to-noise-ratio. Especially, it reveals several unexpected relationships between experimental parameters. First, even though reduced initial ion packet widths result in fewer injected ions and reduced amplifier widths result in more noise, the resulting shift of the optimum operating point when reducing both simultaneously leads to a constant signal-to-noise-ratio. Second, there is no dependence of the signal-to-noise-ratio at the optimum operating point on the drift length, as again the resulting shift of the optimum operating point causes all effects to compensate each other.

An important preprocessing step for several music signal processing algorithms is the estimation of playing instruments in music recordings. To this aim, time-dependent instrument recognition is realized by a neural network with residual blocks in this approach. Since music signal processing tasks use diverse time-frequency representations as input matrices, the influence of different input representations for instrument recognition is analyzed in this work. Three-dimensional inputs of short-time Fourier transform (STFT) magnitudes and an additional time-frequency representation based on phase information are investigated as well as two-dimensional STFT or constant-Q transform (CQT) magnitudes. As additional phase representations, the product spectrum (PS), based on the modified group delay, and the frequency error (FE) matrix, related to the instantaneous frequency, are used. Training and evaluation processes are executed based on the MusicNet dataset, which enables the estimation of seven instruments. With a higher number of frequency bins in the input representations, an improved instrument recognition of about 2 % in F1-score can be achieved. Compared to the literature, frame-level instrument recognition can be improved for different input representations.

In future aero engines, a planetary gearbox is to be integrated between fan and turbine to increase the efficiency and bypass ratio. This gearbox has to be monitored during operation to detect possible gearbox faults such as gear wear or gear pitting at an early stage. This paper presents a method consisting of vibration measurement, sensor-dependent feature extraction and support-vector machine (SVM)-based classification of pitting for gear condition monitoring. Several gears were loaded with a constant torque on a standardized back-to-back test rig to provoke pitting, and the pitting amount was captured during the tests with a camera. Features are extracted from accelerometers and an acoustic emission sensor, and based on the results of the visually recorded pitting surface, SVM classification is applied to identify the pitting defect. In this contribution, two different SVM classification approaches are investigated. One approach uses a Two-Class SVM, where tests from one gearset are used for SVM training and another approach utilizes a One-Class SVM based on outlier detection. Both methods show that single tooth pitting defects with a relative pitting area of less than 1 % can be effectively identified, whereas the One-Class SVM method showed a higher pitting detection accuracy.

The progress in numerical methods and simulation tools promotes the use of inverse problems in material characterisation problems. A newly developed procedure can be used to identify the behaviour of piezoceramic discs over a wide frequency range using a single specimen via fitting simulated and measured impedances by optimising the underlying material parameters. Since there is no generally accepted damping model for piezoelectric ceramics, several mechanical damping models are examined for the material identification. Three models have been chosen and their ability to replicate the measured impedances is evaluated. On the one hand, the common Rayleigh model is considered as a reference. On the other hand, a Zener model and a model using complex constants are extended to model the transversely isotropic material. As the Rayleigh model is only valid for a limited frequency range, it fails to model the broadband behaviour of the material. The model using complex constants leads to the best fit over a wide frequency range while at the same time only adding three additional parameters for modelling damping. Thus, damping can be assumed approximately frequency-independent in piezoceramics.

The quantitative determination of material parameter distributions in resonant cavities is a relatively new method for the real-time monitoring of chemical processes. For this purpose, electromagnetic resonances of the cavity resonator are used as input data for the reverse calculation (inversion). However, the reverse calculation algorithm is sensitive to disturbances of the input data, which produces measurement errors and tends to diverge, which leads to no measurement result at all. In this work a correction algorithm based on the Monte Carlo method is presented which ensures a convergent behavior of the reverse calculation algorithm.

In order to push the limitations of optical measurement technology further and to measure finer structures, various systems have been published in current research. It has been shown that microspheres applied in the near-field can improve the resolution of an interferometric measuring system and make structures below Abbe’s resolution limit visible. In this study, measurement results obtained with a high-resolution Linnik interferometer show structures with lateral dimensions below the resolution limit. Furthermore, the analysis of the transfer behavior in the spatial frequency domain provides information about the mechanisms in nearfield-assisted interference microscopy by microspheres.

Diffractive lens arrays with overlapping apertures can produce spot arrays with high numerical apertures (NAs). Combined with low-NA objectives, they can measure a large area with high lateral resolution. However, for surface measurements, the axial resolution of such setups is still fundamentally limited by the objectives. In this work, we propose a new design of diffractive optical elements (DOEs) to overcome this problem. The proposed Direct-imaging DOEs can perform 3D high-NA multi-spot surface measurements. Laterally, a non-vanishing contrast up to 1448 lp/mm is measured with a USAF resolution target. Axially, an average height of 917.5 nm with a standard deviation of 49.9 nm is measured with a calibrated step height target of 925.5 nm.