Signal Monitoring Research Articles

Vibration energy-based indicators for multi-target condition monitoring in milling operations

The demand for intelligent process monitoring is increasing in aerospace manufacturing to ensure tight tolerances and high surface quality. Real-time monitoring in machining is crucial for machined accuracy and process reliability, reducing production times and costs, and enhancing automation of the manufacturing process. This study presents a robust multi-target condition monitoring method based on the vibration signals. Firstly, three new energy ratio indicators with dimensionless characteristics were defined for tool wear, breakage, and chatter monitoring. Secondly, the vibration energy loss from the tool tip to the tool holder, and spindle housing was measured and compared, and the rules of vibration loss from the tool tip to the spindle housing were revealed. Using force signals as a reference, the monitoring performance of industrially acceptable acceleration and sound signals in multi-target condition monitoring was quantitatively analyzed. Finally, the performance of the proposed vibration energy-based indicators was experimentally illustrated and quantitatively evaluated. It is shown that these indicators can be used to discriminate between tool breakage and chatter, as well as to assess tool wear. The new monitoring method can also minimize the costs of process monitoring by reducing the use of expensive sensors or overusing multiple sensors in a smart manufacturing system.

Identifying sources of interference in civil aviation radio communication

Civil aviation is an important part of public transportation. However, the wireless communication systems used in the approach and tower control phases of traffic control are susceptible to external interference, posing a threat to flight safety. Traditional communication interference solutions are time-consuming and require specialized technicians to troubleshoot. To solve this problem, we propose a real-time method for monitoring abnormal signals and detecting interference sources during aviation radio communications. The method consists of three steps: real-time blind source signal separation using cubic polynomial fitting, abnormal signal monitoring based on discriminative signal residence time, and using Pearson correlation coefficients to identify abnormal interference sources. This comprehensive approach effectively ensures the frequency safety of aviation radio communications. Experiments conducted at different locations in real airport environments demonstrate that this method can efficiently identify the signal bands and their interference sources.

WiLife: Long-term Daily Status Monitoring and Habit Mining of the Elderly Leveraging Ubiquitous Wi-Fi Signals

The global aging demographic underscores the imperative for continuous in-home monitoring of the empty-nest elderly, ensuring their safety and well-being. The widespread deployment of Wi-Fi infrastructure has paved the way to monitor the elderly in a non-intrusive and privacy-preserving manner. Numerous studies have explored the potential of utilizing Wi-Fi signals to address urgent life safety concerns such as fall detection and vital sign monitoring. However, apart from these acute safety issues, the early detection of potential disease symptoms and managing the progression of chronic diseases are also crucial for elderly care, which calls for long-term and continuous monitoring of the elderly’s daily routines. Unfortunately, challenges like continuous activity segmentation and location/orientation dependencies have hindered the implementation of a long-term, around-the-clock activity monitoring system for the elderly. This work introduces ”WiLife”, a cutting-edge Wi-Fi based framework for continuous monitoring of the elderly’s spatio-temporal daily status information. Specifically, WiLife adopts a strategy of partitioning living spaces into functional areas and categorizing daily activities into atomic states . By encapsulating daily life status into a unique series of triple unit format: \(\left\langle{{Time, Area, State}}\right\rangle\) , WiLife is able to offer valuable insights into when, where, and how activities occur. Field implementations spanning 1,080 hours ( \(45days\times 24hours\) ) in real-world home environments highlight WiLife’s exceptional capability in understanding individual living habits and timely detection of irregularities.

Multilayer Fused Correntropy Reprsenstation for Fault Diagnosis of Mechanical Equipment.

Fault diagnosis is vital for improving the reliability and safety of mechanical equipment. Existing fault diagnosis methods require a large number of samples for model training. However, in real-world environments, mechanical equipment usually operates under healthy conditions during most of its service life, resulting in a scarcity of fault samples. To solve this problem, a novel multilayer fusion correntropy representation method combined with a support vector machine is proposed for the fault diagnosis of mechanical equipment. First, the monitoring signal is expanded into multilayer signal components using wavelet packet decomposition. Then, the correlation between the signal components of each layer is expressed by correntropy, and the corresponding correntropy matrix is constructed. After performing the matrix logarithm operator, all correntropy matrices composed of correntropy values are fused into a vector, which is viewed as a feature of the signal. Finally, a support vector machine is established using small samples to realize fault classification. The effectiveness of the proposed method is validated on four public datasets. The results indicate that compared with other methods, the proposed method has advantages in terms of diagnosis accuracy and noise immunity ability.

Inferring ECG Waveforms from PPG Signals with a Modified U-Net Neural Network.

There are two widely used methods to measure the cardiac cycle and obtain heart rate measurements: the electrocardiogram (ECG) and the photoplethysmogram (PPG). The sensors used in these methods have gained great popularity in wearable devices, which have extended cardiac monitoring beyond the hospital environment. However, the continuous monitoring of ECG signals via mobile devices is challenging, as it requires users to keep their fingers pressed on the device during data collection, making it unfeasible in the long term. On the other hand, the PPG does not contain this limitation. However, the medical knowledge to diagnose these anomalies from this sign is limited by the need for familiarity, since the ECG is studied and used in the literature as the gold standard. To minimize this problem, this work proposes a method, PPG2ECG, that uses the correlation between the domains of PPG and ECG signals to infer from the PPG signal the waveform of the ECG signal. PPG2ECG consists of mapping between domains by applying a set of convolution filters, learning to transform a PPG input signal into an ECG output signal using a U-net inception neural network architecture. We assessed our proposed method using two evaluation strategies based on personalized and generalized models and achieved mean error values of 0.015 and 0.026, respectively. Our method overcomes the limitations of previous approaches by providing an accurate and feasible method for continuous monitoring of ECG signals through PPG signals. The short distances between the infer-red ECG and the original ECG demonstrate the feasibility and potential of our method to assist in the early identification of heart diseases.

Effect of laser parameters and shielding gas flow on co-axial photodiode-based melt pool monitoring signals in laser powder bed fusion

Melt pool monitoring using co-axial photodiodes is increasingly used for online quality monitoring in PBF-LB AM. However, the fundamental correlations between different processing conditions and the co-axial photodiode signals are not well understood. In this study the impact of laser parameters and the shielding gas flow speed on co-axial photodiode-based melt pool monitoring signals is established. It is shown that laser power has positive linear correlation with the photodiode signal, while the correlation with scanning speed is non-linear in the form of y=ax–b. The focal point position, scanning orientation and shielding gas flow speed have highly non-linear response on the photodiode signal, depending on the combinatorial effects of the parameters. These underlying physical correlations should be carefully assessed and taken into consideration when trying to establish correlations between photodiode-based melt pool monitoring signal and defect formation in the PBF-LB process.

PPG2RespNet: a deep learning model for respirational signal synthesis and monitoring from photoplethysmography (PPG) signal.

Breathing conditions affect a wide range of people, including those with respiratory issues like asthma and sleep apnea. Smartwatches with photoplethysmogram (PPG) sensors can monitor breathing. However, current methods have limitations due to manual parameter tuning and pre-defined features. To address this challenge, we propose the PPG2RespNet deep-learning framework. It draws inspiration from the UNet and UNet + + models. It uses three publicly available PPG datasets (VORTAL, BIDMC, Capnobase) to autonomously and efficiently extract respiratory signals. The datasets contain PPG data from different groups, such as intensive care unit patients, pediatric patients, and healthy subjects. Unlike conventional U-Net architectures, PPG2RespNet introduces layered skip connections, establishing hierarchical and dense connections for robust signal extraction. The bottleneck layer of the model is also modified to enhance the extraction of latent features. To evaluate PPG2RespNet's performance, we assessed its ability to reconstruct respiratory signals and estimate respiration rates. The model outperformed other models in signal-to-signal synthesis, achieving exceptional Pearson correlation coefficients (PCCs) with ground truth respiratory signals: 0.94 for BIDMC, 0.95 for VORTAL, and 0.96 for Capnobase. With mean absolute errors (MAE) of 0.69, 0.58, and 0.11 for the respective datasets, the model exhibited remarkable precision in estimating respiration rates. We used regression and Bland-Altman plots to analyze the predictions of the model in comparison to the ground truth. PPG2RespNet can thus obtain high-quality respiratory signals non-invasively, making it a valuable tool for calculating respiration rates.

A Method for the Pattern Recognition of Acoustic Emission Signals Using Blind Source Separation and a CNN for Online Corrosion Monitoring in Pipelines with Interference from Flow-Induced Noise.

As a critical component in industrial production, pipelines face the risk of failure due to long-term corrosion. In recent years, acoustic emission (AE) technology has demonstrated significant potential in online pipeline monitoring. However, the interference of flow-induced noise seriously hinders the application of acoustic emission technology in pipeline corrosion monitoring. Therefore, a pattern-recognition model for online pipeline AE monitoring signals based on blind source separation (BSS) and a convolutional neural network (CNN) is proposed. First, the singular spectrum analysis (SSA) was employed to transform the original AE signal into multiple observed signals. An independent component analysis (ICA) was then utilized to separate the source signals from the mixed signals. Subsequently, the Hilbert-Huang transform (HHT) was applied to each source signal to obtain a joint time-frequency domain map and to construct and compress it. Finally, the mapping relationship between the pipeline sources and AE signals was established based on the CNN for the precise identification of corrosion signals. The experimental data indicate that when the average amplitude of flow-induced noise signals is within three times that of corrosion signals, the separation of mixed signals is effective, and the overall recognition accuracy of the model exceeds 90%.

A novel weighted-guided tensor completion missing data imputation method for health monitoring data of planar parallel mechanism

Abstract High-end mechanical equipment plays a crucial role in the manufacturing industry, making the monitoring of its operational status highly significant. Due to various factors such as environmental influences, the absence of monitoring signals in mechanical equipment status is a common issue, leading to a decline in data quality. To ensure data quality, this paper proposes an adaptive weighted low-rank tensor missing data imputation method. Firstly, based on the motion characteristics of a planar parallel mechanism (PPM), a new low-rank tensor model is established using periodicity, sliding windows, and time series. Secondly, the tensor truncation nuclear norm is defined, and a novel rate parameter is introduced to control the truncation degree of all tensor modes, thereby obtaining weights for each dimension. Finally, within the framework of the alternating direction method of multipliers (ADMM), adaptive weights for each dimension are obtained during each iteration, completing the filling of missing data. Two types of missing patterns are studied on a PPM experimental platform, and the results showed that as the missing rate increased, the mean absolute percentage error is less than 0.018, and root mean square error is less than 0.001, and the rate of change is less than 0.08, which was significantly better than other compared algorithms.

Congeneric and Robust Adhesive Epidermal Patch for Anti‐Interference Physiological Signal Recognition

AbstractEpidermal patches utilized for the transduction of biopotentials and biomechanical signals are pivotal in wearable health monitoring. However, the shortcomings, such as inferior conformal ability, deficient adhesion, and motion artifacts, severely impede the bioelectrodes from perceiving stable and superior‐quality physiological signals. Herein, a polymer epidermal patch possessing a spontaneous Janus structure is facilely prepared through itaconic acylhydrazine (IAH) induced gradient polymerization. The solubility discrepancy of the monomers in IAH authorized the Janus structure with distinct adhesion properties on each side. Moreover, the hydrogen bond network constructed by IAH confers the polymer with a high degree of skin compliance, enabling dynamic and stable mechanical properties to withstand complex monitoring environments. By integrating skin‐like softness (Young's modulus ≈0.16 MPa), robust adhesion (35 kPa), and high signal‐to‐noise ratio (32 dB), this epidermal patch displays exceptional elasticity within the physiological activity spectrum, provides swift electrical and mechanical self‐recovery capabilities, and resists interference in dynamic signal monitoring (deformation, compression, humidity, etc.). By demonstrating multifaceted applications for Electrocardiogram recording under diverse disturbances, the epidermal patch profiles a promising noninvasive, enduring wearable bioelectronic interface with immunity to interference.

A hybrid intelligent diagnostic approach for spool jamming faults of hydraulic directional valves

Directional valves are critical functional components in hydraulic systems for diverting the flow of hydraulic oil and controlling oil pressure. Faults of the valves could lead to failure and even serious accidents in hydraulic systems. Fault diagnostic accuracy is directly influenced by the effectiveness of signal processing. Due to the harsh operating environment of hydraulic systems, the condition monitoring signal acquired from the systems contains a large amount of redundant information and environmental noise. This makes the training processes of fault diagnostic approaches time-consuming and inefficient. In this paper, a new hybrid intelligent approach to spool jamming fault diagnostics for hydraulic directional valves is presented. The approach is designed based on the combined strengths of wavelet packet decomposition (WPD), empirical mode decomposition (EMD), variable mode decomposition (VMD), and the multi-layer perceptron (MLP) to improve the efficiency and accuracy of the fault diagnostic process. Five evaluation indices, including processing time (PT), local average accuracy (LAA), stability of accuracy (SA), weighted average of the neuron number (WANN), and robustness of WANN (ROB), are applied to benchmark the approach using ablation experiments. The experimental results show that the hybrid approach has a strong capability to extract fault-related features from the signal and perform fault diagnostics with high accuracy, efficiency, and robustness.

Wavelet denoising analysis on vacuum-process monitoring signals of aerospace vacuum vessel structures

Abstract The monitoring of micro-defects or external actions induced vacuum degradation in aerospace vacuum vessels is an important challenge. A vacuum-process monitoring method based on quasi-distributed fiber Bragg grating (FBG) sensing technology is proposed. Due to the influence of environmental noise and vacuum pump operation noise, the raw signals measured by FBG sensors contain a large amount of noises, which affects the measurement accuracy and data analysis. Therefore, the wavelet threshold (WT) denoising method is proposed to analyze the influence of noises on the monitoring signals measured by two different kinds of FBG sensors. The evaluation of the monitoring signals after denoising indicates that the proposed method can effectively remove the noise and significantly improve signal quality. The highest signal-to-noise ratio of the processed signals can reach 37.61 dB and the mean square error is 3.68 × 10−7, while retaining the key features of the original signal. The proposed WT denoising method demonstrates better performance and feasibility compared with moving average filtering and Kalman filtering methods. The study provides critical supports for improving the performance and reliability of the vacuum vessel monitoring system.

Electrocardiogram Signal Compression Using Deep Convolutional Autoencoder with Constant Error and Flexible Compression Rate

ObjectivesElectrocardiogram (ECG) signals are beneficial for diagnosing cardiac diseases. The cardiac patients' life quality likely increases with continuous or long-period recording and monitoring of ECG signals, leading to better and early diagnosis of disease and heart attacks. However, continuous ECG recording necessitates high data rates and storage, which means high costs. Therefore, ECG compression is a handy concept that facilitates continuous monitoring of ECG signals. Deep neural networks open up new horizons for compression and also for ECG compression by providing high compression rates and quality. Although they bring constant compression ratios with better average quality, the compression quality of individual samples is not guaranteed, which may lead to misdiagnoses. This study aims to investigate the effect of compression quality on the diagnoses and to develop a deep neural network-based compression strategy that guarantees a quality-bound in return for varying compression ratios.Materials and methodsThe effect of the compression quality on the arrhythmia diagnoses is tested by comparing the performance of the deep learning-based ECG classifier on the original ECG recordings and the distorted recordings using a lossy compression algorithm with different compression error levels. Then, a compression error upper limit is calculated in terms of normalized percent root mean square difference (PRDN) error, which also coincides with the findings of the previous studies in the literature. Lastly, to enable deep learning in ECG compression, a single encoder-multi-decoder convolutional autoencoder architecture, and multiple quantization levels are proposed to guarantee a desired upper limit on the error rate.ResultsThe efficiency of the proposed method is demonstrated on a popular benchmark data set for ECG compression methods using a transfer learning approach. The PRDN error is fixed to various values, and the average compression rates are reported. An average of 13.019:1 compression is achieved for a 10% PRDN error rate, assessed as a fair quality threshold for reconstruction error. It has also been shown that the compression model has a runtime that can be run in real-time on wearable devices such as commercial smartwatches.ConclusionThis study proposes a deep learning-based ECG compression algorithm that guarantees a desired upper limit on the compression error. This model may facilitate an eHealth solution for continuous monitoring of ECG signals of individuals, especially cardiac patients.

Robust safety monitoring and signal detection using alternatives to the standard poisson distribution

ABSTRACT Proper and timely characterization of the safety profile of a pharmaceutical product under development is imperative for assessing the overall benefit-risk relationship of the product and for making key development decisions. For ongoing clinical development, a comprehensive and robust safety monitoring and safety signal detection program which is based upon quantitative statistical reasoning is critical. Methods presented here can be applied to safety signal detection and periodic safety monitoring. Various statistical properties, distributions, and models, all utilizing a Bayesian framework are considered and further examined in order to identify robust methods applicable to a broad set of scenarios and situations. Methods developed for incidence counts (including those with under-dispersed distributions) with variable time-at-risk and with underlying constant or non-constant hazard rates, are proposed and compared to traditional methods designed to assess adverse event incidence rates or binomial incidence proportions (which assume an underlying constant hazard rate and subsequent Poisson distribution for modeling event counts).

GA-BP-Based Low-Noise FBG Hydroacoustic Monitoring System with Reference Sensor.

To address the issue of harsh marine background noise impacting the monitoring signal of fiber-optic hydrophones, we propose a low-noise fiber Bragg grating (FBG) hydroacoustic monitoring system with a reference sensor based on genetic algorithm backpropagation (GA-BP). Through theoretical analysis, we deduce the noise suppression steps of the GA-BP algorithm based on the reference sensor and construct train and test sets based on the data from the reference sensor and monitoring sensor at different times, optimizing the GA-BP algorithm to find the best fitting results and thereby obtaining the low-noise monitoring signal. Experimental results from the anechoic tank show that the proposed method can suppress background noise interference on effective signals and that the suppression effect improves as the background noise increases. The sound pressure sensitivity ranges from -173.76 dB to -171.33 dB at frequencies of 8 kHz to 12 kHz, with a response flatness of less than 2.43 dB. The noise suppression effect is obvious under the condition of poor signal-to-noise ratio (SNR), which can reach more than 18.3 dB. The advantages of the proposed algorithm in array signal processing are further demonstrated by the directivity experiment, which proves that the algorithm has a great potential for engineering applications in harsh marine environment.

A Double-Layer Polyurethane Electrospun Membrane with Directional Sweat Transport Ability for Use as a Soft Strain Sensor.

Wearable electronics for long-term monitoring of physiological signals should be capable of removing sweat generated during daily motion, which significantly impacts signal stability, human comfort, and safety of the electronics. In this study, we developed a double-layer polyurethane (PU) membrane with sweat-directional transport ability that can be applied for monitoring strain signals. The PU membrane was composed of a hydrophilic, conductive layer and a relatively hydrophobic layer. The double-layer PU composite membrane exhibited varied pore size and opposite hydrophilicity on its two sides, enabling the spontaneous pumping of sweat from the hydrophobic side to the hydrophilic side, i.e., the directional transport of sweat. The membrane can be used as a strain sensor to monitor motion strain over a broad working range of 0% to 250% with high sensitivity (GF = 4.11). The sensor can also detect simple human movements even under sweating conditions. We believe that the strategy demonstrated here will provide new insights into the design of next-generation strain sensors.

Textile omnidirectional antenna for wearable WiFi-7 applications using higher order modes

In WiFi 7 (IEEE 802.11be), Multiple Input Multiple Output (MIMO) technology plays a crucial role in enhancing throughput. This combination enables numerous applications for wearable WiFi devices. For instance, WiFi can be utilized for tracking and fall detection purposes. Additionally, wearable devices used in gaming, vital signal monitoring, and tracking can benefit from the implementation of wearable MIMO antennas. Despite the demand for wearable WiFi antennas, specifically in the new 6 GHz band, a significant gap exists in the investigation of antennas that are completely made of textile, are low profile, and provide vertical polarization and omnidirectional patterns. Addressing this gap, in this paper, a wearable planar antenna is introduced, which is specifically designed to offer an omnidirectional pattern and linear polarization. To ensure its practicality and ease of use, the antenna utilizes lightweight and wearable textile material. The design details and performance of the proposed antenna are presented. The antenna covers all three bands of WiFi, namely, 2.4 GHz, 5 GHz, and 6 GHz bands. The 10-dB return loss bandwidth is 2.4 GHz–2.5 GHz, and 4.6 GHz–7.2 GHz. The maximum Specific Absorption Rate (SAR) for an input power of 500 mW is calculated at 1.053 W/Kg for a 2 mm air gap between the antenna and tissue surface. The size of the antenna is given by a circular area of π × (50 mm)2, and a thickness of 6 mm.

Flexible Microfluidic Strain Sensor Made with Poly(3,4-ethylenedioxythiophene):polystyrenesulfonate-MXene-Au Nanocomposites for Monitoring Physiological Signals.

Flexible strain sensors have been widely used in wearable electronics. However, the fabrication of flexible strain sensors with a large strain detection range, high sensitivity, and negligible hysteresis remains a formidable challenge, even after enormous advancements in the field. Herein, a flexible microfluidic strain sensor was fabricated by filling poly(3,4-ethylenedioxythiophene):polystyrenesulfonate-MXene-gold (PEDOT:PSS-MXene-Au) nanocomposites into microchannels in an elastic matrix. Owing to the unique properties of the nanofiller and Ecoflex elastomer microchannel, the microfluidic strain sensor detected a strain of 0%-500% with low hysteresis (2.4%), high sensitivity (guage factor = 25.4), short response times (∼86 ms), and good durability. Moreover, the flexible microfluidic sensor was used to detect various physiological signals and human activities, control a mechanical hand, and capture hand motions in real time. As demonstrated by its good performance, the proposed flexible microfluidic sensor holds great potential in applications such as wearable electronics, physiological signal monitoring and human-machine interactions.

Lightweight, ultra-compressed, and environmentally friendly wood/TPU aerogel sensor based on optimized performance of dynamic 3D pore structure

Although bio-based sensing materials have a wide range of applications in the field of pressure detection, they still need to improve their sensitivity, detection limit and hysteresis. This paper studied the relationship between the 3D pore structure and sensing performance under dynamics. Using Balsa wood as the substrate, CWA/TPU aerogel and its sensor were prepared with lightweight, compressibility, highly sensitivity, wide-detection, and low-hysteresis. Meanwhile, the brittleness problem of the carbonized aerogel was solved by uniformly attaching TPU to the aerogel interface. In this paper, the 3D structure of CWA/TPU aerogel during compression was reconstructed by Micro-XCT technology, and the results show that the sensitivity of the bio-based carbonized material is directly proportional to the porosity and inversely proportional to the aspect ratio. This CWA/TPU aerogel pressure sensor has a high sensitivity of 76.18 kPa−1 in a wide detection limit of 0.6 Pa–100 kPa, 90 % supercompression strain, ±7.4 % low hysteresis and outstanding stability over 10,000 cycles. And the sensor can detect different ranges of pressure strains and has great potential for future applications in physiological signal monitoring, action recognition, and sports training.

Flexible and wearable multilayer structure fiber sensor for motion and human physiological signal monitoring

AbstractThe development of flexible piezoresistive sensors has increasingly attracted widespread attention due to their various potential applications in wearable devices, human–machine interfaces, and health monitoring systems. Developing high‐performance sensors that can accurately monitor movement and human health monitoring is very necessary. This study uses silver nanoparticles, MXene, and polypyrrole to prepare conductive fabrics based on various fabrics through layer‐by‐layer encapsulation. Finally, conductive fabrics with different layers were encapsulated to prepare a multilayer fiber sensor. Next, the series piezoresistive performance of sensors made of different fabric materials and sensors with different layers were tested. The experimental results show that the sensor prepared has the best performance when the selected fabric material is mulberry silk and the number of layers of the fabric is five. After testing, the sensitivity of the sensor is 4.9029 at low strain and 0.7901 at high strain. Moreover, it exhibits good stability during 1600 compression‐release cycles and temperatures range from −20 to 60°C. The designed sensor performs well in motion and human physiological signal monitoring. This study provides new ideas and references for the design and research of multilayer fiber sensors.