Feature Extraction In Time-frequency Domain Research Articles

A Time-Frequency Domain Feature Extraction Approach Enhanced by Computer Vision for Wire Arc Additive Manufacturing Monitoring Using Fourier and Wavelet Transform

Wire arc additive manufacturing (WAAM) is a rapidly growing technology that offers several advantages over traditional manufacturing methods, such as high deposition rates and the ability to build large components in a cost-effective manner. However, WAAM is also prone to the generation of defects, so the timely identification of anomalies is important to reduce the waste and get components of high quality. To develop anomaly detection application, the feature extraction process represents a key ingredient which allows machine learning systems to analyze big data. Waveform GMAW welding processes are typically used in WAAM to reduce the heat input supplied to the material and avoid defects such as excessive bending of parts and residual stress. These processes are based on the controlled dip transfer principle, so the waveforms should repeat themselves during deposition. This suggests that the frequency content of the voltage and current welding signals acquired during the process can provide important information about the process state. In this research, an experimental campaign was conducted to collect data for pulsed welding and surface tension transfer (STT) processes during the deposition of mild steel ER70S6, stainless steel 316L, Aluminum 4043, and Inconel 718 alloys. Welding voltage and current signals were acquired during the building processes, and a frequency domain analysis was conducted using the Fast Fourier transform (FFT) and discrete wavelet transform (DWT) with the aim to extract features from signals aiming to better separate the feature space, which means improve anomaly detection performance in detecting defects like arc instability, porosity, geometrical defect due to arc blow and humping. Furthermore, a methodology based on time-frequency analysis enhanced by Gabor filter for texture anomaly detection of scalograms obtained by Morlet Continuous Wavelet Transform is proposed, which showed an improvement of performance in separation between normal and anomalous deposition of several materials under different welding technologies.

Research Progress on Identification and Suppression Methods for Monitoring the Cavitation State of Centrifugal Pumps

Cavitation is a detrimental phenomenon in hydraulic machinery, adversely impacting its performance, inducing vibration and noise, and leading to corrosion damage of overflow components. Centrifugal pump internal cavitation will lead to severe vibration and noise, and not only will the performance of hydraulic machinery be adversely affected but the impact generated by the collapse of the vacuole will also cause damage to the impeller wall structure, seriously affecting the safety of the equipment’s operation. To prevent the generation and development of internal cavitation in centrifugal pumps, to prevent the hydraulic machinery from being in a state of cavitation for a long time, to avoid the failure of the unit, and to realize the predictive maintenance of centrifugal pumps, therefore, it is of great significance to research the methods for monitoring the cavitation of hydraulic machinery and the methods for suppressing the cavitation. This paper comprehensively describes the centrifugal pump cavitation mechanism and associated hazards. It also discusses the current state of centrifugal pump cavitation monitoring methods, including commonly used approaches such as the flow-head method, high-speed photography, pressure pulsation method, acoustic emission method, and vibration method. A comparative analysis of these methods is presented. Additionally, the paper explores signal characterization methods for centrifugal pump cavitation, including time-domain feature extraction, frequency-domain feature extraction, and time–frequency-domain feature extraction. The current research status is elaborated upon. Moreover, the paper presents methods to mitigate cavitation and prevent its occurrence. Finally, it summarizes the ongoing research on identifying and determining the cavitation state in centrifugal pumps and offers insights into future research directions.

Stator-rotor fault diagnosis of induction motor based on time-frequency domain feature extraction

Since the induction motor operates in a complex environment, making the stator and rotor of the motor susceptible to damage, which would have significant impact on the whole system, efficient diagnostic methods are necessary to minimize the risk of failure. However, traditional fault diagnosis methods have limited applicability and accuracy in diagnosing various types of stator and rotor faults. To address this issue, this paper proposes a stator-rotor fault diagnosis model based on time-frequency domain feature extraction and Extreme Learning Machine (ELM) optimized with Golden Jackal Optimization (GJO) to achieve highprecision diagnosis of motor faults. The proposed method first establishes a platform for acquiring induction motor stator-rotor fault data. Next, wavelet threshold denoising is used to pre-process the fault current signal data, followed by feature extraction to perform time-frequency domain eigenvalue analysis. By comparison, the impulse factor is finally adopted as the feature vector of the diagnostic model. Finally, an induction motor fault diagnosis model is constructed by using the GJO to optimize the ELM. The resulting simulations are carried out by comparing with neural networks, and the results show that the proposed GJO-ELM model has the highest diagnostic accuracy of 94.5%. This finding indicates that the proposed method outperforms traditional methods in feature learning and classification of induction motor fault diagnosis, and has certain engineering application value.

Enhancing Upper Limb Prosthetic Control in Amputees Using Non-invasive EEG and EMG Signals with Machine Learning Techniques

Amputation of the upper limb significantly hinders the ability of patients to perform activities of daily living. To address this challenge, this paper introduces a novel approach that combines non-invasive methods, specifically Electroencephalography (EEG) and Electromyography (EMG) signals, with advanced machine learning techniques to recognize upper limb movements. The objective is to improve the control and functionality of prosthetic upper limbs through effective pattern recognition. The proposed methodology involves the fusion of EMG and EEG signals, which are processed using time-frequency domain feature extraction techniques. This enables the classification of seven distinct hand and wrist movements. The experiments conducted in this study utilized the Binary Grey Wolf Optimization (BGWO) algorithm to select optimal features for the proposed classification model. The results demonstrate promising outcomes, with an average classification accuracy of 93.6% for three amputees and five individuals with intact limbs. The accuracy achieved in classifying the seven types of hand and wrist movements further validates the effectiveness of the proposed approach. By offering a non-invasive and reliable means of recognizing upper limb movements, this research represents a significant step forward in biotechnical engineering for upper limb amputees. The findings hold considerable potential for enhancing the control and usability of prosthetic devices, ultimately contributing to the overall quality of life for individuals with upper limb amputations.

An end-to-end framework based on acoustic emission for welding penetration prediction

The adverse effects of inadequate welding penetration on the service performance of welded assemblies have raised significant concerns. In this study, we explore the benefits of acoustic emission (AE) sensors for monitoring the laser-arc hybrid weld penetration state during welding processes. The underlying mechanisms of AE signal generation under different penetration states are elaborated, and the feasibility of using time-frequency domain features for monitoring penetration state is validated. Building upon these findings, we propose an end-to-end framework named WAENet. This framework incorporates an automated optimization module for signal processing and time-frequency domain feature extraction, as well as an improved modular convolutional neural network (CNN) for recognition. To enhance the CNN's performance, techniques such as grouped convolution, depth-wise separable convolution, and global average pooling are employed. Furthermore, the training process of the CNN also considers the involvement of hyperparameters related to signal processing and feature extraction, as proposed in this article. WAENet achieves an average accuracy of 99.62 % in identifying the penetration states, which outperforms other models that use alternative feature extraction or classification techniques for comparison. These studies expand the application scope of AE and CNN in the field of intelligent welding.

A Syllable-based Speech Recognition system by using Pitch detection on Time-Frequency domain Feature Extraction

A Syllable-based Speech Recognition system by using Pitch detection on Time-Frequency domain Feature Extraction

Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time–Frequency-Based Features and Deep Learning Models

The milling machine serves an important role in manufacturing because of its versatility in machining. The cutting tool is a critical component of machining because it is responsible for machining accuracy and surface finishing, impacting industrial productivity. Monitoring the cutting tool’s life is essential to avoid machining downtime caused due to tool wear. To prevent the unplanned downtime of the machine and to utilize the maximum life of the cutting tool, the accurate prediction of the remaining useful life (RUL) cutting tool is essential. Different artificial intelligence (AI) techniques estimate the RUL of cutting tools in milling operations with improved prediction accuracy. The IEEE NUAA Ideahouse dataset has been used in this paper for the RUL estimation of the milling cutter. The accuracy of the prediction is based on the quality of feature engineering performed on the unprocessed data. Feature extraction is a crucial phase in RUL prediction. In this work, the authors considers the time–frequency domain (TFD) features such as short-time Fourier-transform (STFT) and different wavelet transforms (WT) along with deep learning (DL) models such as long short-term memory (LSTM), different variants of LSTN, convolutional neural network (CNN), and hybrid models that are a combination of CCN with LSTM variants for RUL estimation. The TFD feature extraction with LSTM variants and hybrid models performs well for the milling cutting tool RUL estimation.

Real-time online unstart prediction of supersonic inlet based on pressure fluctuations

Supersonic inlet unstart prediction is one of the most critical components in supersonic flight technology, because inlet unstart may cause unexpected damage to the engine. However, due to the nonlinear and nonstationary property of pressure signals, effective supersonic inlet unstart prediction based on pressure signals remains a challenging problem. In this paper, change-point detection (CD), wavelet packet transform (WPT), and deep learning (DL) are combined to realize real-time online unstart prediction for supersonic inlet. The combination of the water cycle algorithm (WCA) and penalized contrast change-point detection (PCCD) will accurately divide the signal into two different flow states based on global characteristics, providing a reliable dataset for training the subsequent convolutional neural network (CNN). Then, a novel network named WPT-CNN is applied and it will take pressure signals as input to implement time-frequency domain feature extraction. To improve the accuracy of the classification model, a multi-channel unstart prediction model is proposed, which can simultaneously receive real-time pressure from multiple sensors to obtain detailed information inside the supersonic inlet. Results indicate that WCA-PCCD and multi-channel model can achieve better real-time online unstart prediction in comparison with other change-point detection algorithms and single-channel model.

Fault Detection in Induction Motor Using Time Domain and Spectral Imaging-Based Transfer Learning Approach on Vibration Data.

The induction motor plays a vital role in industrial drive systems due to its robustness and easy maintenance but at the same time, it suffers electrical faults, mainly rotor faults such as broken rotor bars. Early shortcoming identification is needed to lessen support expenses and hinder high costs by using failure detection frameworks that give features extraction and pattern grouping of the issue to distinguish the failure in an induction motor using classification models. In this paper, the open-source dataset of the rotor with the broken bars in a three-phase induction motor available on the IEEE data port is used for fault classification. The study aims at fault identification under various loading conditions on the rotor of an induction motor by performing time, frequency, and time-frequency domain feature extraction. The extracted features are provided to the models to classify between the healthy and faulty rotors. The extracted features from the time and frequency domain give an accuracy of up to 87.52% and 88.58%, respectively, using the Random-Forest (RF) model. Whereas, in time-frequency, the Short Time Fourier Transform (STFT) based spectrograms provide reasonably high accuracy, around 97.67%, using a Convolutional Neural Network (CNN) based fine-tuned transfer learning framework for diagnosing induction motor rotor bar severity under various loading conditions.

A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology

Wireless communications are increasingly vulnerable to malicious jamming attacks due to the inherent openness and broadcast nature. In this regard, a novel anti-jamming scheme based on knowledge graph is proposed in this paper, where the knowledge graph which contains the prior knowledge of the commonly-used jamming patterns is utilized to make the fast anti-jamming decision with low complexity. Specifically, for the known jamming patterns, the anti-jamming strategy is directly extracted based on the search from the knowledge graph by using the sensing information. On the other hand, for the unknown jamming patterns, a time-frequency domain feature extraction method is first proposed to extract the jamming feature and match the jamming patterns in the knowledge graph. As such, multiple anti-jamming strategies can be obtained by the knowledge graph search. Finally, the optimal strategy can be achieved by the two communication decision criteria-based strategy fusion. The simulation results showed that the time-frequency domain feature extraction method can well extract the time-frequency domain feature of the jamming pattern, and the proposed algorithm has high decision accuracy and robustness.

Modeling Brain Volume Using Deep Learning-Based Physical Activity Features in Patients With Dementia.

There is a proven correlation between the severity of dementia and reduced brain volumes. Several studies have attempted to use activity data to estimate brain volume as a means of detecting reduction early; however, raw activity data are not directly interpretable and are unstructured, making them challenging to utilize. Furthermore, in the previous research, brain volume estimates were limited to total brain volume and the investigators were unable to detect reductions in specific regions of the brain that are typically used to characterize disease progression. We aimed to evaluate volume prediction of 116 brain regions through activity data obtained combining time-frequency domain- and unsupervised deep learning-based feature extraction methods. We developed a feature extraction model based on unsupervised deep learning using activity data from the National Health and Nutrition Examination Survey (NHANES) dataset (n = 14,482). Then, we applied the model and the time-frequency domain feature extraction method to the activity data of the Biobank Innovations for chronic Cerebrovascular disease With ALZheimer’s disease Study (BICWALZS) datasets (n = 177) to extract activity features. Brain volumes were calculated from the brain magnetic resonance imaging of the BICWALZS dataset and anatomically subdivided into 116 regions. Finally, we fitted linear regression models to estimate each regional volume of the 116 brain areas based on the extracted activity features. Regression models were statistically significant for each region, with an average correlation coefficient of 0.990 ± 0.006. In all brain regions, the correlation was > 0.964. Particularly, regions of the temporal lobe that exhibit characteristic atrophy in the early stages of Alzheimer’s disease showed the highest correlation (0.995). Through a combined deep learning-time-frequency domain feature extraction method, we could extract activity features based solely on the activity dataset, without including clinical variables. The findings of this study indicate the possibility of using activity data for the detection of neurological disorders such as Alzheimer’s disease.

Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction

In a wind turbine, the rolling bearing is the critical component. However, it has a high failure rate. Therefore, the failure analysis and fault diagnosis of wind power rolling bearings are very important to ensure the high reliability and safety of wind power equipment. In this study, the failure form and the corresponding reason for the failure are discussed firstly. Then, the natural frequency and the characteristic frequency are analyzed. The Empirical Mode Decomposition (EMD) algorithm is used to extract the characteristics of the vibration signal of the rolling bearing. Moreover, the eigenmode function is obtained and then filtered by the kurtosis criterion. Consequently, the relationship between the actual fault frequency spectrum and the theoretical fault frequency can be obtained. Then the fault analysis is performed. To enhance the accuracy of fault diagnosis, based on the previous feature extraction and the time-frequency domain feature extraction of the data after EMD decomposition processing, four different classifiers are added to diagnose and classify the fault status of rolling bearings and compare them with four different classifiers.

An Automated method for the analysis of bearing vibration based on spectrogram pattern matching

As a mean for non-intrusive inspection of bearing systems, the scope of predicting their condition from the acoustic vibrations liberated during their operation, utilizing signal processing methods, has been of extensive research, over decades. Vibration being highly non-stationary, time domain as well as spectral features cannot characterize its behavior. Even though spectrogram is a time-frequency domain feature extraction technique, its interpretation is tedious and perhaps, subjective. In the proposed method, the spectrogram images of the normal vibration data is compared with that of the contextual vibration, using Structural Similarity Index Metric (SSIM). It is hypothesized that the pattern similarity between the contextual spectrogram and the baseline is low when the bearing is faulty. The SSIM between the spectrogram image of normal bearing vibration data and the baseline is different from those between the baseline and vibration data corresponding to Inner Race Failure (IRF), Roller Element Defect (RED) and Outer Race Failure (ORF). Via the proposed method of spectrogram pattern matching based on SSIM, the subjectivity in the comparative interpretation of spectrogram is eliminated fully. The SSIM corresponding to the vibrations acquired from the normal and faulty bearings differ with a P value of 4.43693x 10-16. The technique can distinguish defective bearings with, 95.74% sensitivity, 96% accuracy and 100% specificity, without dismantling or open intervention

Research on Fault Diagnosis of Air Conditioner Based on Deep Learning

The essence of intelligent fault diagnosis is to classify the feature of faults by machine learning. It is difficult and key to extract fault characteristics of signals efficiently. The general feature extraction methods include time frequency domain feature extraction, Empirical Mode Decomposition (EMD), Wavelet Transform and Variational Mode Decomposition (VMD). However, these methods require a certain prior experience and require reasonable analysis and processing of the signals. In this paper, in order to effectively extract the fault characteristics of the air conditioner's vibration signal, the stacked automatic encoder (SAE) is used to extract the feature of air conditioner’s vibration signal, and the Softmax function is used to identify the air conditioner's working condition. The SAE performs unsupervised learning on the signal, and Softmax function performs supervised learning on the signal. The number of hidden layers and the number of hidden layer's nodes are determined through experiments. The effects of learning rate, learning rate decay, regularization, dropout, and batch size on the correct rate of the model in supervised learning and unsupervised learning are analyzed. Thereby realizing the fault diagnosis of the air conditioner. The recognition correct rate of deep learning model reached 99.92\%. The deep learning fault diagnosis method proposed in this paper is compared with EMD and SVM, VMD and SVM two kind of fault diagnosis methods.

A Method for the Investigation of Bearing Vibration Based on Spectrogram Image Comparison

The possibility of analyzing the condition of bearing systems from the vibrations generated during their operation, by means of signal processing methods, has been of extensive research, over last few years. As, vibration signal is highly non-stationary, time as well as frequency domain features cannot designate its behavior well. Though, Spectrogram is a time-frequency domain feature extraction method, its analysis is tedious and maybe, subjective. In the proposed method, the spectrogram images of the normal vibration data is compared with that of the contextual vibration, using Peak Signal-To-Noise Ratio (PSNR). It has postulated that, the pattern similarity between the contextual spectrogram and baseline is little when the bearing is faulty. The PSNR between the spectrogram image of normal bearing vibration data and the baseline is different from those between the baseline and vibration data corresponding to Inner Race Failure (IRF), Roller Element Defect (RED) and Outer Race Failure (ORF).The PSNR analogous to the vibrations picked up from normal and faulty bearings vary with a P value of 4.58445 × 10−20. The method can discriminate faulty bearings with, 96.77% sensitivity and 100% specificity.

Rigid-flexible coupling dynamics simulation of planetary gear transmission based on MFBD

This paper deals with the problem of getting dynamic characteristics in complex mechanical multi-body system. Based on the MFBD (Multi-Flexible-Body Dynamics) technology, the rigid-flexible coupling dynamic simulation method is proposed, and then the method is applied to the planetary gear transmission. The results show that the dynamic stress distribution of planetary gear can be obtained to determine the dangerous location, and the dynamic response characteristics are more obvious. Then the simulation model of planetary gear transmission with broken tooth is established, the fault feature extraction in time-frequency domain is carried out using the acceleration signal. In addition, industrial data is also used to validate the effectiveness of the proposed method.

On time-frequency domain feature extraction of wave signals for structural health monitoring

Wave propagation signals are commonly used as information carrier in structural health monitoring. To facilitate decision making, wave signals are often decomposed into multiple components to reveal its frequency or time-frequency content. In this research we investigate the use of time-frequency decomposition techniques for feature extraction. The method based on the adaptive harmonic wavelet transform (AHWT) possesses high computational efficiency and at the same time inherently avoids certain issues in some other time-frequency feature extraction methods, e.g., the empirical mode decomposition (EMD). EMD entertains the adaptivity with respect to signal features, but that adaptive nature affects the comparability of the resulting intrinsic mode functions (IMFs). In contrast, the AHWT based method has similar feature extraction capabilities in the time-frequency domain while using a deterministic basis. Therefore, wavelet features can become cross-comparable when common wavelet basis is used. Our case study shows that the AHWT based approach can identify the critical features in Lamb wave signals to realize effective and robust decision making in structural damage detection.

Remaining life prediction of rolling bearing based on PCA and improved logistic regression model

Rolling bearing reliability assessment and remaining useful life (RUL) prediction are crucially important for improving the reliability of mechanical equipment, reducing the probability of sudden failure, and saving on maintenance costs. Novel prediction method is proposed based on PCA and Improved Logistic Regression Model (ILRM) to solve the problem that the model is difficult to establish and the remaining life of rolling bearing is difficult to estimate. Time domain, frequency domain, and time-frequency domain feature extraction methods are employed in this study to extract the original features from the vibration signals. Next, the relative feature value is used to reduce the influence of random vibration and individual differences between bearings. PCA is run based on the original extracted features and high dimensional and superfluous information to merge the original features and reduce the dimension, where typically sensitive features are extracted. The ILRM is then used to build a model that reflects the deterioration trend and eliminates the impact of fluctuations, ultimately yielding information regarding the rolling bearing’s reliability and the remaining life. The proposed method is shown to accurately predict the lifespan of rolling bearings, thus exhibiting practical value in the engineering field.

Wavelet based Fault Classification for Rolling Element Bearing in Induction Machine

Induction motors plays the most important role in any industry. Induction motor faults results in motor failure causing breakdown and great loss of production due to shutdown of industry and also increases the running cost of machine with reduction in efficiency. This needs for early detection of fault with diagnosis of its root cause. In this research paper a wavelet based fault classification method has been developed for rolling element bearing in induction motor using vibration signal. Wavelet based vibration analysis is one of the most successful techniques used for condition monitoring of rotating machines. This paper describes a new condition monitoring method for induction motors based on wavelet transform. A robust bearing fault detection scheme has been developed by time-frequency domain feature extraction from vibration signals of healthy and defective machine. General Terms Induction motor, fault detection, ball bearing, wavelet transform, condition monitoring.

A Novel Local Time-Frequency Domain Feature Extraction Method for Tool Condition Monitoring Using S-Transform and Genetic Algorithm

This paper investigates an online effective method for tool condition monitoring. Acoustic emission signal of a system which is acquired by a sensor mounted to the spindle of the milling machining center is used as the fault indicator because it is easily to be installed, inexpensive and practical for use in industrial environment. Time-frequency analysis is selected for signal processing step based on its ability to reveal time and frequency variant characteristics of faulty signal. S-transform is used as a powerful time-frequency method for this purpose. Because of the high dimension of the time-frequency results, it is desirable to use a local region of interest in time-frequency domain instead of using the entire information, for fast and accurate monitoring and detection when any abnormal/fault operating condition might occur. Such a strategy also helps to reduce the computation cost which is necessary for online applications and improves the interpretation resolution for law quality signals. An optimization method based on genetic algorithm is used for finding the most discriminative local area as the region of interest in time-frequency domain. For feature generation step, a correlation coefficient between each signal and the healthy signal is assigned to the signal using a 2-D correlation analysis. Curve fitting approach is then used to determine a function to approximate the fault value based on the correlation coefficients. Experimental results based on a milling machine under different operating conditions show that this method has a high accuracy for fault detection. It is also concluded that the accuracy of the local feature extraction is higher than the conventional ways.