Time-domain Signal Research Articles

Novel imbalanced multi-class fault diagnosis method using transfer learning and oversampling strategies-based multi-layer support vector machines (ML-SVMs)

For health monitoring and fault diagnosis of critical mechanical system components, historical data related to equipment failures are often limited and exhibit varying imbalanced multi-class characteristics (e.g., with noisy and time-series data). Moreover, fault diagnosis frameworks based on traditional resampling algorithms (e.g., SMOTE) mostly heavily rely on manual feature extraction, making them difficult to adapt to diverse working conditions or objects. To address these challenges, we propose a novel end-to-end imbalanced multi-class fault diagnosis architecture using transfer learning and oversampling strategies-based multi-layer support vector machines (ML-SVMs). ML-SVMs utilize a VGG-based deep migration feature extraction method to extract features from original time-domain vibration signals, employing natural source domain weights to reduce dependence on human experience and sample size. Then, ML-SVMs introduce ISCOTE (i.e., the first and second layers of ML-SVMs), an improved version of the sample-characteristic over-sampling technique (SCOTE). ISCOTE generates more effective and reasonable samples for each fault class through a scaling factor and iterative optimization mechanism, whether in noisy feature spaces with fuzzy boundaries or in clear boundary feature spaces. Finally, in the third layer of ML-SVMs, multi-class SVMs (e.g., LS-SVMs and standard SVMs) are utilized to train balanced feature samples and derive classification models with strong generalization ability. The effectiveness of ML-SVMs is demonstrated through 16 fault diagnosis instances using CWRU and IMS bearing data, PHM 2010 and TTWD tool wear data. Results indicate that ML-SVMs outperform 8 well-known oversampling-based algorithms in fault diagnosis recognition rates and algorithm robustness. It has offered a feasible architecture for multi-class imbalanced fault scenarios with limited data and multiple adverse features.

Resonant Vibrational Enhancement of Downhill Energy Transfer in the C-Phycocyanin Chromophore Dimer.

Energy transfer between electronically coupled photosynthetic light-harvesting antenna pigments is frequently assisted by protein and chromophore nuclear motion. This energy transfer mechanism usually occurs in the weak or intermediate system-bath coupling regime. Redfield theory is frequently used to describe the energy transfer in this regime. Spectral densities describe vibronic coupling in visible transitions of the chromophores and govern energy transfer in the Redfield mechanism. In this work, we perform finely sampled broadband pump-probe spectroscopy on the phycobilisome antenna complex with sub-10-fs pump and probe pulses. The spectral density obtained by Fourier transforming the pump-probe time-domain signal is used to perform modified Redfield rate calculations to check for vibrational enhancement of energy transfer in a coupled chromophore dimer in the C-phycocyanin protein of the phycobilisome antenna. We find two low-frequency vibrations to be in near-resonance with the interexcitonic energy gap and a few-fold enhancement in the interexcitonic energy transfer rate due to these resonances at room temperature. Our observations and calculations explain the fast downhill energy transfer process in C-phycocyanin. We also observe high-frequency vibrations involving chromophore-protein residue interactions in the excited state of the phycocyanobilin chromophore. We suggest that these vibrations lock the chromophore nuclear configuration of the excited state and prevent the energetic relaxation that blocks energy transfer.

Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques

This paper presents a comprehensive fault diagnosis approach for a delta robot utilizing advanced feature extraction and classification techniques. A four-arm delta robot prototype is designed in SolidWorks for realistic fault analysis. Two case studies investigate faults through control effort and vibration signals, with control effort detecting motor and encoder faults, while vibration signals identify bearing faults. This study compares time-domain signal features and wavelet scattering networks, applied by classification algorithms including wide neural networks (WNNs), efficient linear support vector machine (ELSVM), efficient logistic regression (ELR), and kernel naive Bayes (KNB). Results indicate that a WNN, using wavelet scattering features ranked by one-way anova, is optimal due to its consistency and reliability, while these features enhance computational efficiency by reducing classifier size. Sensitivity analysis demonstrates the classifier’s capacity to detect untrained faults, highlighting the importance of effective feature extraction and classification methods for fault diagnosis in complex robotic systems. This research significantly contributes to fault diagnosis in delta robots and lays the groundwork for future studies on fault tolerance control and predictive maintenance planning. Future work will focus on the physical implementation of the delta robot in laboratory settings, aiming to improve operational efficiency and reliability in industrial applications.

Multi-channel Fused Vision Transformer Network for Bearing Fault Diagnosis under Different Working Conditions

Abstract Many of the current fault diagnosis methods rely on time-domain signals. While the richest information are contained in these signals, their complexity poses challenges to network learning and limits the ability to fully characterize them. To address these issues, a novel Multi-channel Fused Vision Transformer Network (MFVTN) is proposed in this paper. Firstly, the Overlapping Patch Embedding (OPE) module is introduced to overlap the time-domain map with edge information, preserving the global continuous features of the time-domain map and adding positional encoding for sorting. This integration helps the Vision Transformer (ViT) merge detailed features and construct the global mapping. Secondly, multiple dimensional time domain signal features are extracted and fused in parallel, enabling multi-domain fault diagnosis of bearings. In order to enhance the network ability to extract domain-invariant features, an adversarial training strategy combined with Wasserstein distance is utilized. The results demonstrate that the diagnostic accuracy of the proposed MFVTN can reach 98.2%.

A Lightweight and Efficient Multimodal Feature Fusion Network for Bearing Fault Diagnosis in Industrial Applications

To address the issues of single-structured feature input channels, insufficient feature learning capabilities in noisy environments, and large model parameter sizes in intelligent diagnostic models for mechanical equipment, a lightweight and efficient multimodal feature fusion convolutional neural network (LEMFN) method is proposed. Compared with existing models, LEMFN captures rich fault features at multiple scales by combining time-domain and frequency-domain signals, thereby enhancing the model’s robustness to noise and improving data adaptability under varying operating conditions. Additionally, the convolutional block attention module (CBAM) and random overlapping sampling technology (ROST) are introduced, and through a feature fusion strategy, the accurate diagnosis of mechanical equipment faults is achieved. Experimental results demonstrate that the proposed method not only possesses high diagnostic accuracy and rapid convergence but also exhibits strong robustness in noisy environments. Finally, a graphical user interface (GUI)-based mechanical equipment fault detection system was developed to promote the practical application of intelligent fault diagnosis in mechanical equipment.

A new frequency domain denoising method for coal mine transient electromagnetic measuring data

Abstract Due to limitation of space in mine tunnel, only a 2 m × 2 m rectangle coil can be employed to collect the electromagnetic signals for the mine transient electromagnetic analysis. Hence, a small emission magnetic moment and weak detection signal strength will increase the electromagnetic analysis difficulty. Moreover, the transient electromagnetic response signal in mines exhibits exponential decay over time, with late-stage amplitudes being notably small, which makes these signals susceptible to noise interference. As a result, it is necessary to study effective noise removal methods to enhance the signal-to-noise ratio of the late-stage electromagnetic data. Addressing this challenge task, this study proposes a new method for frequency domain denoising of transient electromagnetic data. This new method utilizes the Fourier Transform to convert the original transient electromagnetic data from the time domain to the frequency domain, and then, the noise is identified and removed through the frequency characters. Lastly, the processed frequency domain information is transformed to the time domain by the Fourier Inverse Transform to restore the time domain signals. The effect of this frequency domain denoising method is demonstrated through numerical simulations and underground coal mine field data to show significant noise reduction performance on the abnormality detection in the underground coal mine.

Research on coal rock parameter calibration based on discrete element method

To ensure that the discrete element model of the coal wall accurately reflects the actual cutting process of coal and rock during virtual prototype simulation of mining equipment, this study aims to expedite the development of intelligent mining machinery. Using coal samples from the 4602 working face of Yangcun Coal Mine, operated by Yanzhou Coal Mining Group, we conducted coal rock packing experiments and uniaxial compression tests to obtain the packing angle and compressive strength of the coal samples. Based on the experimental results, we designed Plackett–Burman experiments (PB experiments), steepest ascent experiments, and Box-Behnken experiments to study the influence of particle contact mechanics parameters and bonding mechanics parameters on the packing angle and compressive strength, using the packing angle and compressive strength as response variables. Our objective is to minimize the relative error between the simulated packing angle and the measured packing angle. We solved and optimized the parameter calibration model and conducted simulation calibration experiments based on the optimization results. A comparative analysis with the actual test results revealed that the maximum relative errors between the simulated and measured values for the packing angle and compressive strength were only 2.9% and 5.0%, respectively. Additionally, a discrete element model of a typical working face coal wall was established based on the parameters obtained from this calibration method. A bidirectional coupling model of the cutting process between the coal and rock was created using EDEM-RecurDyn to simulate the rigid-flexible coupling of the coal cutter. An experimental coal wall model was constructed based on similarity theory, and both simulation and physical experiments were conducted. The evaluation metrics for comparison were the time-domain and frequency-domain characteristics of the drum’s vibration signals. The maximum relative error for the time-domain signal characteristics between the two experimental setups was only 4.19%, while the maximum relative error for the frequency-domain signal characteristics was 3.75%. This validates the feasibility of the proposed calibration method for the discrete element coal wall model and the accuracy of the calibration results. Furthermore, it demonstrates that the constructed discrete element coal wall accurately represents the actual coal and rock properties. The virtual simulation based on this model effectively replicates the interaction process between mining machinery and coal, providing a safe, efficient, and low-cost technological approach for performance analysis of mining equipment and intelligent control of supporting devices.

915 nm pumping kilowatt fiber oscillator with high optical-to-optical efficiency

We demonstrate an all-fiber oscillator with high optical-to-optical efficiency using laser diodes working at 915nm as the pump sources to reduce the demand for thermal management. When the output power of the bidirectional pumped oscillator is 1.16kW, the optical-to-optical efficiency is as high as 75.4%. In this working state, the output characteristics of the oscillator are observed. The Raman suppression ratio is 41.23dB and the beam quality factor Mx2 = 1.14, My2 = 1.29. Analyzing the time trajectory of the highest power output, there are no significant characteristic peaks in the time domain signal and the frequency domain signal, which indicates that the oscillator has good stability.Improving the output characteristics of the 915nm pumping lasers has a positive significance for the application of non-strict ambient temperature control.

Electromagnetic radiation of granite under dynamic compression

To elucidate the characteristics and mechanism of electromagnetic radiation in granite under impact loading, based on the quasi-static compression tests, this paper conducts dynamic compression experiments on granite using Hopkinson pressure bar and one-stage light-gas gun as loading methods. Combined with experimental and theoretical analyses, the relationship between mechanical and electromagnetic responses under impact loads of different intensities, and the time-domain signals of electromagnetic radiation generated by a single crack under different strain rates are studied. The intensity and frequency of electromagnetic radiation increase with the increasing compressive strain rate. According to the thermal activation theory, it reveals the microscopic mechanism of the transition from intergranular microcracks to transgranular microcracks in terms of strain sensitivity. It also serves as the physical basis for the increase in electromagnetic radiation intensity amplitude and frequency with increasing compressive strain rate. Transgranular microcracks are the primary cause of electromagnetic radiation generated by fractures.

An Improved Vital Signal Extraction Method Based on Laser Doppler Effect.

The mixed signal of respiratory waveform and heartbeat waveform detected by the Laser-Doppler system is processed with an intermediate-frequency (IF) interference filtering method, an enhanced extraction method and a waveform-fixing method. To filter the IF interference signals and the noise scatters in the time-frequency graph, the filtering method based on coefficient of variation (CoV) values and the enhanced curve extraction method based on noise-scatter theory are utilized in vital signal analysis. To decouple the respiratory signal and the heartbeat signal in time domain, the waveform-fixing method based on second-order difference theory is utilized in signal decoupling. This method as an algorithm is applied in the computer simulation and laboratory environments. The results show that the above methods can extract the mixed waveforms and identify the respiratory rates and heart rates in real experimental data. The IF interference signal can be filtered adaptively, and the accuracy of the analyzed rates can be improved to about 95%.

Differential Evolution-based Time Domain Decomposition Method for Multi-impact Vibration Signals of Reciprocating Machinery

Abstract To solve the problem of extracting the impact component from the complex time-domain vibration signal of reciprocating machinery vibration signals, a differential evolution-based time domain decomposition method is proposed to achieve adaptive extraction of impact components. The method establishes new decomposition window containing three adjustment parameters to adapt to multiple forms of impact components. Furthermore, with the optimization objectives of minimizing reconstruction loss, amplitude moment loss, and similarity loss, a decomposition parameter optimization algorithm based on differential evolution is established to achieve the optimization process of decomposition parameters. The results of processing simulated and actual vibration signals of diesel engines show that the new method can adaptively and accurately identify the impact component and impact time center in the vibration component, with a signal reconstruction loss of less than 2.5% and a decomposition time of only 54.1s.

End-to-End Intelligent Fault Diagnosis of Transmission Bearings in Electric Vehicles Based on CNN

Environmental noise and transmission components can cause significant interference in vibration signals, rendering the extraction of bearing fault features challenging in service scenarios. Traditional fault diagnosis methods rely heavily on professional domain knowledge, prior models, and signal preprocessing methods. The accuracy of fault diagnosis depends on the quality of the fault-sensitive features extracted by vibration signal preprocessing methods. An improved convolutional neural network (CNN) end-to-end intelligent fault diagnosis model based on raw vibration data (RVDCNN) is proposed. The time-domain vibration signal of the transmission bearing is converted into a continuous two-dimensional numerical matrix, and a two-dimensional CNN model is constructed through network structure optimization. The original time-domain vibration signal numerical matrix of the bearing is trained and tested to extract and learn abstract fault features of different fault types, and then the fault classification of the bearing is achieved. To verify the generalizability of the RVDCNN intelligent fault diagnosis model, it is applied to the recognition of rolling bearings in the two-speed mechanical automatic transmission of electric vehicles, achieving recognition accuracy of 99.11% for seven types of bearings.

Research on faults diagnosis of dynamic balance in scroll compressor based on CWT-MViTV2

Abstract The dynamic balancing of rotor systems is a critical factor affecting the vibration of scroll compressors. Traditionally, the detection and correction of rotor dynamic balancing systems have relied on time-domain and frequency-domain analysis of vibration signals, as well as validation using rotor dynamic balancing test platforms. This paper proposes a novel fault diagnosis method for the dynamic balancing of scroll compressor rotor systems. This method acquires the time-domain vibration signals of the scroll compressor through online detection experiments. These signals are then transformed into time-frequency maps using continuous wavelet transform (CWT). By integrating the MViTV2 neural network, this approach enables effective identification of dynamic balancing fault types without requiring disassembly or shutdown of the machine. The fault types, corresponding to the offset of the balancing weight’s centroid, will provide direct data support for subsequent validation work. This study compares various neural network models combined with time-frequency maps and demonstrates that the proposed model achieves the highest accuracy of 99.749% compared to other models, and the method’s generalizability is validated in the public dataset. Furthermore, the proposed model maintains a high accuracy of 94.872% in high-noise environments. After improvement, the accuracy of the model has been increased to 95.641%, the training time and the diagnosis time has been reduced to 0.240 iter/s and 0.0277 s.

Encrypted-OFDM: A secured wireless waveform

Wireless communication has been a broadcast system since its inception, which violates security and privacy issues at the physical layer between the intended transmit and receive pairs. Consequently, it is essential to secure the wireless signal such that only the intended receiver can realize the signal properties. In this paper, we propose Encrypted-OFDM, a new waveform, where the signal structure is altered to encrypt the waveform with a shared secret key. We achieve the signal level security by modifying the OFDM signal in time-domain, thus erasing the OFDM properties and obfuscating the signal properties to an eavesdropper. We present a two-stage encryption algorithm to increase the robustness of the transmitted waveform and achieve a high level of secrecy, even when low entropy keys are used. We also introduce a novel channel estimation algorithm by removing the pilots, so that only the intended receiver can estimate the channel correctly. Furthermore, we perform both secrecy and error analysis for the transmitted and received Encrypted-OFDM waveform. Extensive simulation and over-the-air experiments show that the performance of Encrypted-OFDM is comparable to legacy OFDM, and the SNR penalty due to the secured waveform varies between ≈ 1–4 dB. In all these scenarios, Encrypted-OFDM remains unrecognized at the eavesdropper.

Measurement of surface rectangular defect depth based on laser-excited transmitted surface waves

ABSTRACT In order to study the effect of laser-excited surface waves on the transmission of surface rectangular defects, the transmission information of surface waves is used to achieve the quantitative calculation of defect depth. In this paper, the finite element method is employed to analyse the transmission process of laser-excited surface waves on rectangular defects. Subsequently, the influence of the front and back edges of rectangular defects on the transmission of surface waves is examined, and the formation mechanisms of feature points in the time-domain signals at detection points are elucidated. Ultimately, the calculation formula for defect depth is derived on the basis of the propagation path of each component after transmission. The experimental results are consistent with the simulation results, and the relative error of the surface rectangular defect depth calculation is within 9%. The validation of the proposed method on homogeneous isotropic metallic materials demonstrates the effectiveness and applicability of the proposed method. These results will provide a reliable method for characterising the depth of rectangular defects on surfaces by laser ultrasonic technology.

Defect classification and quantification method based on AC magnetic flux leakage time domain signal characteristics

Pipelines play a crucial role in industries such as petroleum and nuclear power, where non-destructive testing is essential. Magnetic flux leakage testing methods are widely used for pipeline inspection due to their ability to detect both internal and external defects. However, accurately classifying and evaluating the size of such defects poses challenges due to the complex coupling relationship between ferromagnetic materials and defect magnetic fields. This paper proposes a method for defect classification and quantification based on the time domain characteristics of AC magnetic flux leakage signals. Firstly, the paper explores the shielding effects caused by transient high magnetic permeability within the material on signals from internal defects. It analyzes the differences in signals between internal and external defects. Then, based on the nonlinear attributes of defect signals, the paper proposes a defect classification method based on derivatives analysis of the windowed time-domain signal. Moreover, the study finds that rising time and zero-crossing time can be used to assess the depth of internal and external defects separately, which can decouple the width and depth. Finally, experimental validation confirms the effectiveness of defects classification and quantification. This paper provides a feasible method for evaluating the defect of ferromagnetic materials.

An End-to-End Underwater Acoustic Target Recognition Model Based on One-Dimensional Convolution and Transformer

Underwater acoustic target recognition (UATR) is crucial for defense and ocean environment monitoring. Although traditional methods and deep learning approaches based on time–frequency domain features have achieved high recognition rates in certain tasks, they rely on manually designed feature extraction processes, leading to information loss and limited adaptability to environmental changes. To overcome these limitations, we proposed a novel end-to-end underwater acoustic target recognition model, 1DCTN. This model directly used raw time-domain signals as input, leveraging one-dimensional convolutional neural networks (1D CNNs) to extract local features and combining them with Transformers to capture global dependencies. Our model simplified the recognition process by eliminating the need for complex feature engineering and effectively addressed the limitations of LSTM in handling long-term dependencies. Experimental results on the publicly available ShipsEar dataset demonstrated that 1DCTN achieves a remarkable accuracy of 96.84%, setting a new benchmark for end-to-end models on this dataset. Additionally, 1DCTN stood out among lightweight models, achieving the highest recognition rate, making it a promising direction for future research in underwater acoustic recognition.

Low-Cost Purpose-Built Ultra-Low-Field NMR Spectrometer.

Low-field NMR has emerged as a new analytical technique for the investigation of molecular structure and dynamics. Here, we introduce a highly integrated ultralow-frequency NMR spectrometer designed for the purpose of ultralow-field NMR polarimetry of hyperpolarized contrast media. The device measures 10 cm × 10 cm × 2.0 cm and weighs only 370 g. The spectrometer's aluminum enclosure contains all components, including an RF amplifier. The device has four ports for connecting to a high-impedance RF transmit-receive coil, a trigger input, a USB port for connectivity to a PC computer, and an auxiliary RS-485/24VDC port for system integration with other devices. The NMR spectrometer is configured for a pulse-wait-acquire-recover pulse sequence, and key sequence parameters are readily controlled by a graphical user interface (GUI) of a Windows-based PC computer. The GUI also displays the time-domain and Fourier-transformed NMR signal and allows autosaving of NMR data as a CSV file. Alternatively, the RS485 communication line allows for operating the device with sequence parameter control and data processing directly on the spectrometer board in a fully automated and integrated manner. The NMR spectrometer, equipped with a 250 ksamples/s 17-bit analog-to-digital signal converter, can perform acquisition in the 1-125 kHz frequency range. The utility of the device is demonstrated for NMR polarimetry of hyperpolarized 129Xe gas and [1-13C]pyruvate contrast media (which was compared to the 13C polarimetry using a more established technology of benchtop 13C NMR spectroscopy, and yielded similar results), allowing reproducible quantification of polarization values and relaxation dynamics. The cost of the device components is only ∼$200, offering a low-cost integrated NMR spectrometer that can be deployed as a plug-and-play device for a wide range of applications in hyperpolarized contrast media production─and beyond.

Cross-domain transfer fault diagnosis by class-imbalanced deep subdomain adaptive network

In variable working conditions and class imbalances, fault diagnosis in gearbox systems becomes difficult due to the difficulty in learning the classification boundaries of the minority class. Moreover, the diversity in fault mode distributions under various working conditions poses significant challenges for domain adaptation. To this end, this work proposed a Class-imbalanced Deep Subdomain Adaptive Network (CIDSAN) for complex cross-domain fault diagnosis scenarios involving feature and label shifts. We design a convolutional neural network embedded with SimAM (SimAM-CNN) to extract global feature information from time-domain vibration signals. Subsequently, a novel cost-sensitive classifier is presented for class re-weighting, along with a Joint Supervised Classifier that refines intra-class and inter-class distances by re-weighting class weights in the latent space using cost-sensitive classifiers while leveraging LDAM regularization to adjust classification boundaries. Diagnostic case studies on two datasets under various working conditions validate the effectiveness and superiority of the proposed approach. The code https://github.com/Zjy0121/CIDSAN.

Sound quality characteristics of reciprocating hermetic refrigeration compressors

In recent years, sound quality (SQ) has become an increasingly important factor in the home appliance industry, enhancing the customer experience and comfort. Manufacturers that prioritize SQ in their designs can gain a competitive advantage in the market and increase customer satisfaction. The compressor is one of the main sources of noise in a refrigerator. While manufacturers typically provide refrigerator sound quality and noise labels, research that specifically evaluates the compressor sound quality metrics at a component level is not common. Therefore, this study aims to isolate the compressor from the refrigerator to investigate SQ metrics for hermetic refrigeration compressors. To achieve this, compressors with different capacities are operated and recorded at different working speeds for different thermal conditions. Objective parameters are calculated and tested using time domain signals and analyzed with spectral analysis methods. In addition to the objective parameters, subjective results are obtained through jury tests consisting of 75 people of different genders and age ranges. By considering objective parameters of sound quality as input and sound quality evaluation values as output, a regression model is established. It is found that Loudness, Sharpness, and Degree of Modulation play an important role in compressor sound quality evaluations consider.