Time-frequency Analysis Of Vibration Signals Research Articles

Diagnosis of Loose Core Fault in Saturable Reactor of Thyristor Valve Based on Vibration Signal Time-Frequency Analysis and CNN

The saturable reactor is one of the core components of the thyristor valve, which plays a role in suppressing current changes and balancing voltage distribution. Due to long-term operation under complex working conditions, the vibration of the reactor can cause the core to loosen. To detect faults in time, a fault diagnosis method is proposed for loose cores in saturable reactors of thyristor valves based on time-frequency analysis of vibration signals and convolutional neural networks. The two-dimensional time-frequency spectrum of the vibration signal is obtained using the short-time Fourier transform method. A convolutional neural network model structure is designed to extract time-frequency spectrum features and diagnose different levels of core looseness faults through learning and training. An experiment was carried out to collect core fault information under variable working conditions. The effectiveness of the method proposed in this paper was verified based on experimental data validation. Through comparative experiments, it was found that the proposed method is superior to existing fault diagnosis methods.

A Method Combining Fractal Analysis and Single Channel ICA for Vibration Noise Reduction

Aiming at the problem that real engineering vibration signals are interfered by strong noise, this paper proposes a method combining single channel-independent component analysis (SCICA) and fractal analysis (FD) to reduce the effect of noise on the time-frequency analysis of vibration signals. First, phase space reconstruction is performed on the vibration signal to make the proper input for ICA algorithm. The original is then decomposed into several component signals. The fractal dimension of each component signals is calculated to determine whether the signal should be considered noise. Noisy component signals are then processed by wavelet denoising. Finally, the output signal after noise reduction is reconstructed using the filtered “right” component signals. This paper uses the method to analyze real noisy vibration signal. Experimental results show the effectiveness of the proposed method.

Digital signal processing for self-vibration monitoring in grinding: A new approach based on the time-frequency analysis of vibration signals

The occurrence of chatter can generate parts outside the dimensional and geometric tolerances or even cause irreversible damage, such as changes to the hardness and surface roughness of the ground part. The objective of this study is to propose a new vibration signal processing technique based on the short-time Fourier transform (STFT) and the ratio of power (ROP) statistic for the detection of chatter during the tangential surface grinding of AISI 1045 steel with different grinding wheels. Vibration signals were recorded at 2 MHz in the grinding tests. The Vickers hardness, roughness and metallography of the ground workpiece surfaces were also evaluated. A digital processing technique based on the STFT and ROP was applied to the vibration signals to extract the characteristics of the chatter. The results show that this technique can be used to characterize over time the spectral patterns of a frequency band related to chatter.

Rolling Bearing Fault Diagnosis Algorithm Based on FMCNN-Sparse Representation

The time-frequency analysis of vibration signals is an effective means to analyze the fault characteristics of rolling bearings. The traditional pattern recognition method is difficult to adapt to the complex mapping relationship between the high-dimensional feature space and the state space. The deep learning method has high-dimensional feature adaptive analysis ability, which is suitable for the intelligent analysis of the high-dimensional feature space in fault states. The feedforward deep convolutional neural network (CNN) has achieved some success in mechanical fault diagnosis. However, the rolling bearing fault signal is complex, and there are many interference factors. The CNN relying on the simple feedforward method cannot effectively meet the actual needs in the field of fault diagnosis. Although there are some CNNs with feedback methods, the CNNs of these feedback methods cannot systematically obtain the characteristic information of rolling bearing faults. Therefore, they do not solve the feature extraction problem of rolling bearing faults well. In view of this, this paper provides a specific mathematical definition of the feedback mechanism for constructing the feedback mechanism in the deep CNN, models the feedback mechanism into an optimization problem, determines the basic framework of the feedback mechanism, and an effective feedback mechanism calculation model is proposed. Based on this, a solution algorithm based on the gradient descent method is proposed. Then, an effective supervised feature extraction method based on sparse expression is proposed. It maps the sample features to the feature domain through the effective transform method. In the process, the wavelet packet transform (WPT) transform is used as the basis function to construct a dictionary with structural effects, and mixed penalty terms are introduced to further optimize the performance of structural sparse expression. Finally, the sparse expression is combined with the feedback mechanism CNN (FCNN) to establish a sub-module fault diagnosis network so that a diagnosis can determine the fault severity while assessing the bearing fault location. The example shows that the method proposed in this paper has high accuracy in determining the state of rolling bearings and has great application potential in engineering.

Compressive sensing-based feature extraction for bearing fault diagnosis using a heuristic neural network

Bearing fault diagnosis collects massive amounts of vibration data about a rotating machinery system, whose fault classification largely depends on feature extraction. Features reflecting bearing work states are directly extracted using time-frequency analysis of vibration signals, which leads to high dimensional feature data. To address the problem of feature dimension reduction, a compressive sensing-based feature extraction algorithm is developed to construct a concise fault feature set. Next, a heuristic PSO-BP neural network, whose learning process perfectly combines particle swarm optimization and the Levenberg–Marquardt algorithm, is constructed for fault classification. Numerical simulation experiments are conducted on four datasets sampled under different severity levels and load conditions, which verify that the proposed fault diagnosis method achieves efficient feature extraction and high classification accuracy.

Classification of time-frequency representations using improved morphological pattern spectrum for engine fault diagnosis

Time-frequency representations (TFR) have been intensively employed for analyzing vibration signals in the field of mechanical faults diagnosis. However, in many applications, TFR are simply utilized as a visual aid. It is very attractive to investigate the utility of TFR for automatic classification of vibration signals. A key step for this work is to extract discriminative parameters from TFR as input feature vector for classifiers. This paper contributes to this ongoing investigation by developing an improved morphological pattern spectrum (IMPS) for feature extraction from TFR. The S transform, which combines the separate strengths of the short time Fourier transform and wavelet transforms, is chosen to perform the time-frequency analysis of vibration signals. Then, we present an improved morphological pattern spectrum (IMPS) scheme, which utilizes the first moment replace of the volume measure used in traditional morphological pattern spectrum (MPS) method. The promise of IMPS is illustrated by performing our procedure on vibration signals measured from an engine with five operating states. Experimental results have demonstrated the presented IMPS to be an effective approach for characterizing the TFR of vibration signals in engine fault diagnosis.

Comparison of different techniques for time-frequency analysis of internal combustion engine vibration signals

In this study, we report an analysis of cylinder head vibration signals at a steady engine speed using short-time Fourier transform (STFT). Three popular time-frequency analysis techniques, i.e., STFT, analytic wavelet transform (AWT) and S transform (ST), have been examined. AWT and ST are often applied in engine signal analyses. In particular, an AWT expression in terms of the quality factor Q and an analytical relationship between ST and AWT have been derived. The time-frequency resolution of a Gaussian function windowed STFT was studied via numerical simulation. Based on the simulation, the empirical limits for the lowest distinguishable frequency as well as the time and frequency resolutions were determined. These can provide insights for window width selection, spectrogram interpretation and artifact identification. Gaussian function windowed STFTs were applied to some cylinder head vibration signals. The spectrograms of the same signals from ST and AWT were also determined for comparison. The results indicate that the uniform resolution feature of STFT is not necessarily a disadvantage for time-frequency analysis of vibration signals when the engine is in stationary state because it can more accurately localize the frequency components excited by transient excitations without much loss of time resolution.

Assessment of structural integrity via wavelet based time-frequency analysis of vibration signals

An approach for assessment of structural integrity via wavelet analysis based Structural Health Monitoring (SHM) is presented here. The success of the approach relies on the information available in the form of local (in time) frequency content of the vibration signals. Results from three typical case studies are reviewed and outlined in this paper. The first one is a study based on hysteretic structures. The second example is for a linear time varying Multi-Degree-Of-Freedom (MDOF) system with time varying natural frequencies and mode shapes. The third case considered is an experimental study on the seismic response of concentrically braced frames.

Engine noise characterisation with Wigner-Ville time-frequency analysis

Reduction in the sound emitted by engines requires accurate identification of the excitation and propagation mechanisms inside the engine. Time-frequency analysis of vibration signals picked up on the outside and inside of an engine cylinder enables us to link every event observed in the time-frequency plane to one excitation source. It is then possible to measure the energy of each event and to quantify the part of each excitation mechanism in the vibration energy which is emitted towards the outside of the engine.