Blade Vibration Signals Research Articles

Non-convex sparse regularization via convex optimization for blade tip timing

Blade Tip Timing (BTT), an emerging technology poised to replace strain gauges, enables contactless measurement of rotor blade vibration. However, the blade vibration signals measured by BTT systems often suffer from significant undersampling. Sparse reconstruction methods are instrumental in addressing the challenge of undersampled signal reconstruction. However, traditional approaches grounded in ℓ1 regularization tend to underestimate the amplitude of the true solution. This underestimation is particularly pronounced in the resonance state of the rotor blade, hindering effective prediction of the blade’s operational state. To overcome this limitation, this paper introduces a novel non-convex regularized BTT model, employing a non-convex penalty term with convex-preserving properties to achieve nearly unbiased reconstruction accuracy. Additionally, we propose a new threshold iteration algorithm designed for the swift solution of this model. The accuracy and robustness of the proposed method in identifying the multimodal vibration parameters of rotor blades are validated through simulations and experiments. Comparatively, the proposed method closely aligns with the Orthogonal Matching Pursuit (OMP) method in recognizing blade multimodal vibration amplitude, showcasing significant improvement over the ℓ1 regularization method. Furthermore, it demonstrates lower sensitivity to changes in BTT probe layout when compared to the OMP and ℓ1 regularization methods.

AI-enabled and multimodal data driven smart health monitoring of wind power systems: A case study

The development of AI has enabled the fault detection of industrial components to be achieved through the combination with deep learning. A detection method combined with deep learning has also emerged for the fault detection of fan blades, such as models based on neural networks using the appearance or sound of the blades. However, the detection model obtained from a single data type often has limitations, such as low accuracy and overfitting. This is also the problem with fan blade detection. In contrast, multimodal data fusion detection models are often more stable. The modality diversity of blade diagnosis is strong, and it can be achieved from multiple modalities such as image, sound, and vibration. To improve the accuracy of fault diagnosis of fan blades, this article proposes a multimodal double-layer detection system (MTDS) based on decision-level and feature-level fusion. The system includes a wind turbine simulation platform and a multimodal detection system. It mainly obtains different modal data of the simulated wind turbine from the image, sound, and vibration signals, including blade images through unmanned aerial vehicle photography, blade vibration signals through electronic vibrators, and blade sound signals through microphones. The highly correlated sound and vibration modal data are fused at the feature level, and a detection model based on the sound and vibration mixed mode is implemented using a sound-vibration-CNN (SV-CNN) proposed in this case. Then, a detection model of the image mode is trained based on the blade image using a Convolution Block Attention Module ResNet (CBAM-ResNet) network. Finally, the detection input of the two modal models is fed into a perceptron to obtain the final prediction result, and the decision-level fusion is implemented to achieve fan blade detection based on multimodal, namely the implementation of MTDS.

Blade vibration study by spectral analysis of tip-timing signals with OMP algorithm

Abstract Blades vibrations must be measured in operations to validate blade design. Tip-timing is one of the classical measurement methods but its main drawback is the generation of sub-sampled and non-uniform sampled signals. Consequently tip-timing signals cannot be processed with conventional methods. Assuming that blade vibration signals yield to line spectra, we introduced a sparse signal model that uses speed variation of the engine. The usual solutions of inverse problems are given with the LASSO method. This paper presents a new approach based on a l 0 -regularization. It is solved with the OMP algorithm adapted to our model. Results from synthetic and real signals are presented to illustrate the efficiency of this method, including a real blade crack test case. The main advantages of the proposed method are to provide accurate estimations with a computational cost drastically reduced with respect to existing methods. Besides, the method does not need to set up regularization parameters while taking into account the speed variation of the engines. Finally, results show that this approach greatly reduces frequency aliasings caused by the low sampling frequency of the measured signals.

Sparse Representation Based Frequency Detection and Uncertainty Reduction in Blade Tip Timing Measurement for Multi-Mode Blade Vibration Monitoring.

The accurate monitoring of blade vibration under operating conditions is essential in turbo-machinery testing. Blade tip timing (BTT) is a promising non-contact technique for the measurement of blade vibrations. However, the BTT sampling data are inherently under-sampled and contaminated with several measurement uncertainties. How to recover frequency spectra of blade vibrations though processing these under-sampled biased signals is a bottleneck problem. A novel method of BTT signal processing for alleviating measurement uncertainties in recovery of multi-mode blade vibration frequency spectrum is proposed in this paper. The method can be divided into four phases. First, a single measurement vector model is built by exploiting that the blade vibration signals are sparse in frequency spectra. Secondly, the uniqueness of the nonnegative sparse solution is studied to achieve the vibration frequency spectrum. Thirdly, typical sources of BTT measurement uncertainties are quantitatively analyzed. Finally, an improved vibration frequency spectra recovery method is proposed to get a guaranteed level of sparse solution when measurement results are biased. Simulations and experiments are performed to prove the feasibility of the proposed method. The most outstanding advantage is that this method can prevent the recovered multi-mode vibration spectra from being affected by BTT measurement uncertainties without increasing the probe number.

Sparse reconstruction of blade tip-timing signals for multi-mode blade vibration monitoring

Severe blade vibrations may reduce the useful life of the high-speed blade. Nowadays, non-contact measurement using blade tip-timing (BTT) technology is becoming promising in blade vibration monitoring. However, blade tip-timing signals are typically under-sampled. How to extract characteristic features of unknown multi-mode blade vibrations by analyzing these under-sampled signals becomes a big challenge. In this paper, a novel BTT analysis method for reconstructing unknown multi-mode blade vibration signals is proposed. The method consists of two key steps. First, a sparse representation (SR) mathematical model for sparse blade tip-timing signals is built. Second, a multi-mode blade vibration reconstruction algorithm is proposed to solve this SR problem. Experiments are carried out to validate the feasibility of the proposed method. The main advantage of this method is its ability to reconstruct unknown multi-mode blade vibration signals with high accuracy. The minimal requirements of probe number are also presented to provide guidelines for BTT system design.

A non-uniformly under-sampled blade tip-timing signal reconstruction method for blade vibration monitoring.

High-speed blades are often prone to fatigue due to severe blade vibrations. In particular, synchronous vibrations can cause irreversible damages to the blade. Blade tip-timing methods (BTT) have become a promising way to monitor blade vibrations. However, synchronous vibrations are unsuitably monitored by uniform BTT sampling. Therefore, non-equally mounted probes have been used, which will result in the non-uniformity of the sampling signal. Since under-sampling is an intrinsic drawback of BTT methods, how to analyze non-uniformly under-sampled BTT signals is a big challenge. In this paper, a novel reconstruction method for non-uniformly under-sampled BTT data is presented. The method is based on the periodically non-uniform sampling theorem. Firstly, a mathematical model of a non-uniform BTT sampling process is built. It can be treated as the sum of certain uniform sample streams. For each stream, an interpolating function is required to prevent aliasing in the reconstructed signal. Secondly, simultaneous equations of all interpolating functions in each sub-band are built and corresponding solutions are ultimately derived to remove unwanted replicas of the original signal caused by the sampling, which may overlay the original signal. In the end, numerical simulations and experiments are carried out to validate the feasibility of the proposed method. The results demonstrate the accuracy of the reconstructed signal depends on the sampling frequency, the blade vibration frequency, the blade vibration bandwidth, the probe static offset and the number of samples. In practice, both types of blade vibration signals can be particularly reconstructed by non-uniform BTT data acquired from only two probes.

Blade Crack Detection of Centrifugal Fan Using Adaptive Stochastic Resonance

Centrifugal fans are widely used in various industries as a kind of turbo machinery. Among the components of the centrifugal fan, the impeller is a key part because it is used to transform kinetic energy into pressure energy. Crack in impeller’s blades is one of the serious hidden dangers. It is important to detect the cracks in the blades as early as possible. Based on blade vibration signals, this research applies an adaptive stochastic resonance (ASR) method to diagnose crack fault in centrifugal fan. The ASR method, which can utilize the optimization ability of the grid search method and adaptively realize the optimal stochastic resonance system matching input signals, may weaken the noise and highlight weak characteristic and thus can diagnose the fault accurately. A centrifugal fan test rig is established and experiments with three cases of blades are conducted. In comparison with the ensemble empirical mode decomposition (EEMD) analysis and the traditional Fourier transform method, the experiment verified the effectiveness of the current method in blade crack detection.

Non-contact crack detection of high-speed blades based on principal component analysis and Euclidian angles using optical-fiber sensors

High-speed rotating blades are key mechanical components in turbo-machinery. High cycle fatigues often induce blade cracks and ultimately the loss of blades. Effort has being made to measure on-line blade vibrations and promptly detect potential cracks. Traditional contacting methods are difficult to satisfy the needs of on-line testing, enduring extreme conditions and non-invasion. Nowadays non-contact vibration measurement at blade tips by optical sensing has become a promising approach. In this paper a blade tip-timing (BTT) method using four optical-fiber sensors is presented and all-blade vibration displacements are derived in detail. Blade vibration signals collected by the BTT method are typical sub-sampled signals, so a signal reconstruction method is proposed based on the Shannon sampling theorem. Next nine candidate vibration parameters are calculated to form the crack feature space and principal component analysis (PCA) is used to extract principal components from it. Then Euclidian angles of principal components are defined to detect cracks. In the end, an experimental set-up is built and a small crack is cut on a blade artificially. Collected data are analyzed to testify the proposed method and the experimental results demonstrate its effectiveness.

Vibration Measurement of Rotating Blades Using a Root Embedded PZT Sensor

Finite element and experimental studies are carried out to test the suitability of a piezoelectric (PZT) sensor in measuring vibrations of blades modeled as beams. The rotating system contains twelve blades mounted to the shaft through a rotor. The PZT sensor is secured in the root between the rotor and blade. First, finite element results are obtained using the finite element package ANSYS. A modal analysis is performed on the system to identify modes and mode shapes. Transient, harmonic and steady‐state responses are then computed to test the ability of the PZT sensor in generating signals for blade vibrations. For the experimental part, the blade vibration signals are produced using the PZT sensor and a strain‐gage, and the outputs are compared with each other. From both the finite element and experimental results, it is concluded that the root‐embedded PZT sensor can be effectively used for blade vibration measurements in a wide range of cases.

On-line fan blade damage detection using neural networks

This paper presents a methodology for monitoring the on-line condition of axial-flow fan blades with the use of neural networks. In developing this methodology, the first stage was to utilise neural networks trained on features extracted from on-line blade vibration signals measured on an experimental test structure. Results from a stationary experimental modal analysis of the structure were used for identifying global blade mode shapes and their corresponding frequencies. These in turn were used to assist in identifying vibration-related features suitable for neural network training. The features were extracted from on-line blade vibration and strain signals which were measured using a number of sensors.The second stage in the development of the methodology entails utilising neural networks trained on numerical Frequency Response Function (FRF) features obtained from a Finite Element Model (FEM) of the test structure. Frequency domain features obtained from on-line experimental measurements were used to normalise the numerical FRF features prior to neural network training. Following training, the networks were tested using experimental frequency domain features. This approach makes it unnecessary to damage the structure in order to train the neural networks.The paper shows that it is possible to classify damage for several fan blades by using neural networks with on-line vibration measurements from sensors not necessarily installed on the damaged blades themselves. The significance of this is that it proves the possibility to perform on-line fan blade damage classification using less than one sensor per blade. Even more significant is the demonstration that an on-line damage detection system for a fan can be developed without having to damage the actual structure.