Damage detection for wind turbine rotor blades using airborne sound

When operating a wind turbine, damage of rotor blades is a serious problem. Undetected damages are likely to increase overtime, and therefore, the safety risks and economical burdens also increase. A monitoring system, which detects reliably defects in early stages, gives scope for action and is therefore a key element to avoid damage increase and to optimize the efficiency of wind turbines. One promising approach for damage detection is acoustic emission methods. Although most acoustic emission approaches use ultrasonic sound waves of the structure that require about 12 to 40 sensors to monitor one rotor blade, we propose to use the airborne sound in lower frequencies from about 500 Hz to 35 Hz with three optical microphones and present a signal model-based damage detection algorithm. The real-time algorithm uses six audio features from a spectrogram representation to detect damages and to estimate its significance. In a fatigue test of a 34-m blade, the algorithm detected the damage event and damage increasing without false detection. It was also tested with recordings inside an operating blade of a 3.4-MW wind turbine. In the recorded time period of about 1 year, the algorithm indicated no false detection. © 2020 The Authors. Structural Control and Health Monitoring published by John Wiley & Sons Lt

Estimation of unknown system states based on an adaptive neural network and Kalman filter

In the field of industry 4.0 the number of sensors increases steadily. The sensor data is often used for system observation and estimation of the system parameters. Typically, Kalman filtering is used for determination of the internal system parameters. Their accuracy and robustness depends on the system knowledge, which is described by differential equations. We propose a self-configurable filter (FNN-EKF) which estimates the internal system behavior without knowledge of the differential equations and the noise power. Our filter is based on Kalman filtering with a constantly adapting neural network for state estimation. Applications are denoising sensor data or time series. Several bouncing ball simulations are realized to compare the estimation performance of the Extended Kalman Filter to the presented FNN-EKF

Analysis of Affine Motion-Compensated Prediction in Video Coding

Motion-compensated prediction is used in video coding standards like High Efficiency Video Coding (HEVC) as one key element of data compression. Commonly, a purely translational motion model is employed. In order to also cover non-translational motion types like rotation or scaling (zoom), e. g. contained in aerial video sequences such as captured from unmanned aerial vehicles (UAV), an affine motion model can be applied. In this work, a model for affine motion-compensated prediction in video coding is derived. Using the rate-distortion theory and the displacement estimation error caused by inaccurate affine motion parameter estimation, the minimum required bit rate for encoding the prediction error is determined. In this model, the affine transformation parameters are assumed to be affected by statistically independent estimation errors, which all follow a zero-mean Gaussian distributed probability density function (pdf). The joint pdf of the estimation errors is derived and transformed into the pdfof the location-dependent displacement estimation error in the image. The latter is related to the minimum required bit rate for encoding the prediction error. Similar to the derivations of the fully affine motion model, a four-parameter simplified affine model is investigated. Both models are of particular interest since they are considered for the upcoming video coding standard Versatile Video Coding (VVC) succeeding HEVC. Both models provide valuable information about the minimum bit rate for encoding the prediction error as a function of affine estimation accuracies. © 1992-2012 IEEE

Facial image processing

[No abstract available