Fault Feature Extraction by Using Adaptive Chirplet Transform

In this paper, we propose a novel method for the detection of road accidents by analyzing audio streams for road surveillance application. In the last decade, due to the increase in a number of people and transportation vehicles, traffic accidents have become one of a major public issue worldwide. The vast number of injuries and death due to road traffic accident reveals the story of a global crisis of road safety. A total of 52,160 road traffic accidents (RTA), 1130 injuries and 85 fatalities were registered during the year 2000, in the state of Qatar. An increase in the number of transportation vehicles around cities has risen the need for more security and safety in public environments. The most obvious reason for a person's death during accidents is the absence or prolong response time of the first aid facility, which is due to the delay in the information of the accident being reached to the police, hospital or ambulance team. In the last couple of years, several surveillance systems have been proposed based on image and video processing for automatically detecting road accidents and car crashes to ensure a quick response by emergency teams. However, in some situations such as adverse weather conditions or cluttered environments, the visual information is not sufficient enough, whereas analyzing the audio tracks can significantly improve the overall reliability of surveillance systems. In this paper we propose a novel method that automatically identifies hazardous situations such as tire skidding and car crashes in presence of background noises, by analyzing the audio streams. Previous studies show that methods for the detection, estimation, and classification of nonstationary signals can be enhanced by utilizing the time-frequency (TF) characteristics of such signals. The TF-based techniques have been proved to outperform classical techniques based on either time or frequency domains, in analyzing real-life nonstationary signals. Time-frequency distributions (TFDs) give additional information about signals that cannot be extracted from either the time domain or frequency domain representations, i.e. the instantaneous frequency of the components of a signal. In order to utilize this extra information provided by TF domain representation, the proposed approach extracts TF image features from quadratic time-frequency distributions (QTFDs), for the detection of audio anomalies. The extended modified-B distribution (EMBD) is utilized to transform a 1-dimensional audio signal into 2-dimensional TF representation which is interpreted as an image. The image descriptors based features are then extracted from the TF representation to classify visually the audio signals into background or abnormal activity patterns in the TF domain. The proposed features are based on Haralick's texture features extracted from the TF representation of audio signals considered and processed as a textured image. These features are used to characterize and hence classify audio signals into M classes. This research study demonstrates that a TF image pattern recognition approach offers significant advantages over standard signal classification methods that utilize either t-domain only or f-domain only features. The proposed method has been experimentally validated on a large open source database of sounds, including several kinds of background noise. The events to be recognized are superimposed on different background sounds of roads and traffic jam. The obtained results are compared with a recent study, utilizing the same large and complex data set of audio signals, and the same experimental setup. The overall classification results confirm the superior performance of the proposed approach with accuracy improvement up to 6%.

Conventional time-frequency Transformers primarily focus on the global features of signals in the time-frequency domain while neglecting the local features in both the time domain and frequency domain. This limitation hinders the ability of the model to effectively capture the shared features among time, frequency, and time-frequency representations. To address this issue, a Multi-Branch Joint Time-Frequency Transformer (MBJTF-Transformer) is proposed for domain generalization (DG) fault diagnosis of rotating machinery. Specifically, a time-branch Transformer is designed to extract temporal features, while a frequency-branch Transformer captures frequency-domain information. In addition, a time-frequency Transformer is employed to learn the shared representations across time, frequency, and time-frequency domains. Finally, a multi-decision fusion strategy of MBJTF-Transformer is adopted to enhance the generalization capability of the model. Experimental results on both the SCARA (Selective Compliance Assembly Robot Arm, SCARA) dataset and the PU (Paderborn University) bearing dataset demonstrate that the proposed MBJTF-Transformer achieves superior DG performance compared to multiple state-of-the-art sequential models.

Third order time and frequency analysis has exhibited great potential for correlation analysis of multi-sensor datasets, but is usually presented as separate time domain and frequency domain approaches. A combined framework of both frequency domain and time domain has rarely been used. This paper proposes a non-parametric third order time and frequency domain framework which used two dimensional Fourier transforms to bridge the gap between time domain and frequency domain. A unified framework offers flexibility and efficiency to apply to data. In this paper we study neural spike train data treated as stochastic point processes. In time domain direct analysis, third order cumulant densities of spike trains are applied, which need all first-, secondand third order product densities to be calculated before constructing the third order cumulant density, which brings additional challenges. The novelty in this study is that a new framework is proposed which can offer an alternative approach without calculating lower order quantities and can reveal nonlinear relationship between neural recordings. The results show that the present framework provides a novel non-parametric method to estimate both time and frequency domain measurements which is applicable to neural spike trains.

In many applications, it may be desired to decompose a non‐stationary signal into its individual components. If spectral components of the non‐stationary signal do not overlap in the frequency domain then Fourier transform can be used to decompose the non‐stationary signal. Fourier transform fails to decompose the non‐stationary signal if its spectral components overlap in the frequency domain. In this study, the authors propose Fourier‐Bessel transform and the time–frequency analysis in conjunction with the fractional Fourier transform (FB‐TF) method for the separation of multi‐component non‐stationary signal whose components overlap in both time and/or frequency domains. The efficiency of the proposed method is compared with one of the traditional decomposition methods like EMD. The proposed approach is applied to both simulated and experimental radar data. Results demonstrate the effectiveness of the proposed method for non‐stationary signal separation and for detecting manoeuvring target in heavy sea‐clutter environments. The improvement factor and clutter attenuation are calculated and used to compare the performance of the EMD and the FB‐TF methods in suppressing the sea‐clutter and enhancing target detection. The proposed method can be used as a potential tool for detecting and enhancing the low observable manoeuvring air targets in the sea‐clutter environment.

During the long-term operation of wind turbines, due to environmental factors and equipment aging, the health and reliability of each component will gradually decline, leading to failure. To assess the health status of wind turbines, timely grasp the subsequent changes and development trends, it is necessary to extract degradation characteristics, including time domain, frequency domain, and time-frequency domain characteristics. These degradation characteristics can reflect the operating status of the equipment, help build health indicator curves, and evaluate the health status of high-speed shaft bearings of wind turbines. Selecting reasonable degradation characteristics is an important prerequisite for constructing a health index curve, and using evaluation indicators to construct a comprehensive evaluation function to screen degradation characteristics. The feature fusion method based on a self-organizing feature mapping network is used to fuse multiple selected degradation features and fuse the selected multiple degradation features into a curve that can reflect the bearing degradation process. Finally, a quantitative analysis is performed on the health index curve to scientifically assess the health status of bearings. Bearings are one of the key components of wind turbines. Based on the health index curve constructed in this article, an appropriate prediction model is selected to predict the health index trend of bearings. A timely and effective grasp of the health trends of wind turbine bearings is of great practical significance for formulating scientific and reasonable maintenance measures for wind farms. The work of this article will be divided into the following four parts: (1) Extracting the time domain, frequency domain, and time-frequency domain degradation characteristics of high-speed shaft bearing vibration signals of wind turbines; (2) Comprehensive evaluation using monotonicity, correlation, and robustness constructs function to screen degenerate features; (3) Use self-organizing feature mapping network. The network integrates the selected degradation features and constructs a health index curve; (4) Based on the constructed health index curve, optimize the BiLSTM network hyperparameters through Bayesian and establish a BO-BiLSTM network model to achieve a more accurate and scientific prediction of the health index trend.

Modern radars use complex waveforms to obtain high detection performance and low probabilities of interception and identification. Signals intercepted from multiple radars overlap considerably in both the time and frequency domains and are difficult to separate with primary time parameters. Time–frequency analysis (TFA), as a key signal-processing tool, can provide better insight into the signal than conventional methods. In particular, among the various types of TFA, parameterized time-frequency analysis (PTFA) has shown great potential to investigate the time–frequency features of such non-stationary signals. In this paper, we propose a procedure for PTFA to separate overlapped radar signals; it includes five steps: initiation, parameterized time-frequency analysis, demodulating the signal of interest, adaptive filtering and recovering the signal. The effectiveness of the method was verified with simulated data and an intercepted radar signal received in a microwave laboratory. The results show that the proposed method has good performance and has potential in electronic reconnaissance applications, such as electronic intelligence, electronic warfare support measures, and radar warning.

Slope synchronous chirplet transform and its application to tacho-less order tracking of rotating machineries

Time-varying filtering for nonstationary signal analysis of rotating machinery: Principle and applications

Abstract: The principal objectives of this study were to examine in-cylinder combustion pressure oscillation characteristics of soybean biodiesel in time domain and time-frequency domain, and their influences on the control and operational parameters, such as injection timing, exhaust gas recirculation (EGR) ratio, engine load and engine speed. In this study, the combustion pressure oscillation characteristics of biodiesel engine for various injection timing, EGR ratio and engine speed were investigated. The corresponding relation of pressure characteristics in the time domain and frequency domain were obtained. The results showed that the pressure oscillation and peak pressure rise acceleration occurred mainly in the diffusion combustion, and the peak pressure rise rate located in the premixed combustion. The in-cylinder pressure level curve can be divided into three stages. The pressure levels of stage 1, stage 2 and stage 3 represent the peak in-cylinder pressure, the maximum amplitude of pressure rise rate and pressure rise acceleration, respectively. As the injection timing retards, the pressure levels of stage 1 and stage 3 decrease gradually. The pressure level curve of stage 3 with 25o before top dead center (BTDC) is the highest and the oscillation is the most significant. It is worth noting that the location of each stage with various operate conditions is not fixed. At 0.41 MPa indicated mean effective pressure (IMEP), with the increase of EGR rate, the pressure levels of stage 1 and stage 2 decrease gradually. The pressure level curve of stage 3 and the maximum amplitude of pressure rise acceleration with 0% EGR rate are the highest. The oscillation with 0% EGR rate is the most significant at 0.41 MPa IMEP. Compared to 0.41 MPa IMEP, the frequency bands of stage 1 and stage 2 at 1.1 MPa IMEP are relatively low due to the soft combustion in the cylinder. As EGR rate increases, the pressure level of stage 1 decreases, and those of stage 2 and stage 3 increase gradually. The oscillation with 30% EGR rate is the most significant. With the increase of engine speed, the pressure levels of stage 1 and stage 2 decrease, and move to the low frequency. The pressure level in the high frequency domain at 1600 r/min is less than that at 1100 r/min, and the combustion process is smooth. Keywords: combustion pressure oscillation, soybean biodiesel, injection timing, exhaust gas recirculation, engine speed DOI: 10.3965/j.ijabe.20160906.1680 Citation: Ma P, Liu H L, Liu S Y. Experimental study on combustion pressure oscillation of soybean bio diesel oil. Int J Agric & Biol Eng, 2016; 9(6): 156－166.

Roller bearings are commonly used components in rotating machinery and are pruned to be failure, which may cause huge economic losses and even casualties. Vibration signals of rolling bearings are signs of rotating machinery. Therefore, they can be used to describe the process of performance degradation. Unfortunately, the signals exhibit non-stationary and non-linear as well as highly non-monotonic behaviors as the bearing condition degrades especially under complex working conditions. In this paper, a method to construct a constitution health index based on vibration signals is presented. Firstly, 40 features in time domain, frequency domain, time-frequency domain and entropy domain are extracted individually. Then, the combined evaluation index is constructed based on the above 40 features in terms of monotonicity, robustness, predictability and correlation. Finally, by comparing the degradation fusion indexes of rolling bearings under different operating conditions with the traditional characteristics, the remaining useful life prediction method based on grey correlation analysis is proposed. The results suggest that the proposed method can effectively predict the RUL of rolling bearings with an acceptable degree of accuracy up to 90%, which outperforms the traditional features.

Time-Frequency (TF) analyses have been widely used to analyze some nonstationary signals like speech and biological signals. Cohen's class TF distribution has a high resolution in time and frequency and becomes a powerful tool for nonstationary signal analysis. In order to apply the TF analysis using a novel kernel to biological signals, the authors investigated a higher resolution kernel for the ultrasound Doppler signal. The authors would like to apply the TF analysis to blood flow signal analysis. They measured the ultrasound Doppler signal which was obtained in olive oil with a steel ball moving to and fro in the continuous ultrasound. The movement of the steel ball was controlled by various functions. The authors used three kinds of kernel: (1) the Wigner distribution, (2) the Choi-Williams distribution (ED), and (3) the authors' new kernel (named eight figure-kernel), that has high resolution. The results show that the demodulation accuracy with the authors' new kernel is higher and the negative components are smaller than the ED, which makes the cross-term small.

Machine-learning-based models for predicting seismic demands of SCBFs based on scalar and vector-valued intensity measures

Aiming to monitor wear condition of milling cutters in time and provide tool change decisions to ensure manufacturing safety and product quality, a tool wear monitoring model based on Bagging-Gradient Boosting Decision Tree (Bagging-GBDT) is proposed. In order to avoid incomplete tool state information contained in a single domain feature parameter, a multi-domain combination method is used to extract candidate characteristic parameter sets from time domain, frequency domain, and time–frequency domain. Then top 21 significant features are screened by eXtreme Gradient Boosting selection method. Synthetic Minority Oversampling Technique technology is integrated during feature selection to overly sample feature vectors, so that wear condition categories can be well balanced. Bagging idea is then introduced for parallel calculation of the gradient boosting decision tree and to improve its generalization ability. A Bagging-GBDT milling cutter wear condition prediction model is constructed and verified by public ball-end milling data set. Experiments show that random features and training samples selection can effectively improve prediction performance and generalization ability of prediction model. Our Bagging-GBDT model gains F1 score of 0.99350, which is 0.2% and 13.2% higher than the random forest algorithm and basic GBDT model, respectively.

Helical idler gear transmission systems can adapt to high-speed, heavy-load working environments and are thus widely used in aerospace, shipbuilding, and other heavy industry sectors. Root crack is one of the common fault types. Prior studies generally only considered cracks at a single working flank, lacking comparative analysis between the crack at the working flank and the non-working flank. This paper examines the dynamic response of helical idler gears with root cracks at different working flanks, comparing dynamic response differences between working and non-working flank cracks. First, a comprehensive dynamics model of the helical idler gear system is established. Second, the influence of root crack location (the working flank or the non-working flank) on time-varying meshing stiffness is considered based on potential energy method, and a flexible model is established by finite element method for the faulty gear. Finally, solution results of the rigid-flexible coupling dynamics model are analyzed. The dynamic response signal characteristics of root cracks at the working flank and the non-working flank are analyzed in time domain, frequency domain and time frequency domain, respectively. Corresponding experiments are designed based on the FZG experimental platform, and the experimental results are in good agreement with the simulation results, which verified the accuracy of the model.

Navigation aids play a crucial role in guiding ship navigation and marking safe water areas. Therefore, ensuring the accurate and efficient recognition of a navigation aid’s state is critical for maritime safety. To address the issue of sparse features in navigation aid data, this paper proposes an approach that involves three distinct processes: the extension of rank entropy space, the fusion of multi-domain features, and the extraction of hidden features (EFE). Based on these processes, this paper introduces a new LSTM model termed EFE-LSTM. Specifically, in the feature extension module, we introduce a rank entropy operator for space extension. This method effectively captures uncertainty in data distribution and the interrelationships among features. The feature fusion module introduces new features in the time domain, frequency domain, and time–frequency domain, capturing the dynamic features of signals across multiple dimensions. Finally, in the feature extraction module, we employ the BiLSTM model to capture the hidden abstract features of navigational signals, enabling the model to more effectively differentiate between various navigation aids states. Extensive experimental results on four real-world navigation aid datasets indicate that the proposed model outperforms other benchmark algorithms, achieving the highest accuracy among all state recognition models at 92.32%.