Online Health Monitoring Research Articles

Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field data

Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field data

Smart Carbon Fiber-Reinforced Polymer Composites for Damage Sensing and On-Line Structural Health Monitoring Applications.

High electrical conductivity, along with high piezoresistive sensitivity and stretchability, are crucial for designing and developing nanocomposite strain sensors for damage sensing and on-line structural health monitoring of smart carbon fiber-reinforced polymer (CFRP) composites. In this study, the influence of the geometric features and loadings of carbon-based nanomaterials, including reduced graphene oxide (rGO) or carbon nanofibers (CNFs), on the tunable strain-sensing capabilities of epoxy-based nanocomposites was investigated. This work revealed distinct strain-sensing behavior and sensitivities (gauge factor, GF) depending on both factors. The highest GF values were attained with 0.13 wt.% of rGO at various strains. The stability and reproducibility of the most promising self-sensing nanocomposites were also evaluated through ten stretching/relaxing cycles, and a distinct behavior was observed. While the deformation of the conductive network formed by rGO proved to be predominantly elastic and reversible, nanocomposite sensors containing 0.714 wt.% of CNFs showed that new conductive pathways were established between neighboring CNFs. Based on the best results, formulations were selected for the manufacturing of pre-impregnated materials and related smart CFRP composites. Digital image correlation was synchronized with electrical resistance variation to study the strain-sensing capabilities of modified CFRP composites (at 90° orientation). Promising results were achieved through the incorporation of CNFs since they are able to form new conductive pathways and penetrate between micrometer-sized fibers.

Flexible curved multi-functional sensors for air flow and vibration signal measurement of train bogie

Abstract The fluid-solid coupling effects with the aerodynamic noise and structural vibration signal have great part in measurement accuracy and fault diagnosis of the train bogie. It is important to collect aerodynamic noise and structural vibration data for safety evaluation and on-line structural health monitoring (SHM) of the bogie, which brings new challenges to the integration and installation of the sensors in the limited space and complicated operation condition of the train bogie. Flexible curved multifunctional sensors (FCMFS) integrated with the hot film and structural vibration sensor units is designed to the air flow and structural vibration measurement of the train bogie. Bogie vibration and wind tunnel test platforms with FCMFS are built to the air flow velocity and vibration data measurement on the bogie surface under different operation conditions. Piezoelectric Polyvinylidene fluoride(PVDF) sensors have significant output in the wind tunnel and vibration experiments, and the hot film sensor is only sensitive to the air flow signal. The effect of vibration signal to the hot film sensor could be ignored, which is applied to decouple the aerodynamic noise and structural vibration signals for improving the measurement accuracy of the train bogie. The dynamic performance of FCMFS is verified in Rail Transmit Base of East China Jiaotong University, which is applied to SHM and rapid fault diagnosis of train bogie. The air flow distribution and vibration data are collected to safety assessment and fault diagnosis of train bogie, which would promote the intelligent maintenance level of the train bogie.

An optimized single-side inverse finite element method for displacement field reconstruction of wind turbine tower

Wind turbines are subjected to complex dynamic loads during actual operation, which are difficult to predict accurately in advance, which may lead to inaccurate structural strength assessment in the structural design stage, and thus bring safety risks to wind power towers. Inverse Finite Element Method (iFEM) is a reconstruction method of structural displacement distribution, which can estimate the full-field displacement distribution of tower, and used for structural on-line health monitoring. However, conventional iFEM require sensors to be placed on both sides of the structure, which greatly limits its engineering practicality. For this reason, a single-side inverse finite element (iFEM_S) was developed in this study to enhance the engineering applicability of iFEM. Both iFEM and iFEM_S are based on the principle of least squares variation, and the solution equations can be established when the measured strain is known. The core idea of iFEM_S is to find the optimal node displacement s to minimize the sum of mean square errors between the theoretical and measured strains on one side of the structure, thereby generating a set of algebraic equations. Solving these linear equations can achieve the structural displacement field. Additionally, A cyclic optimization algorithm (COA) was developed in this study to improve the robustness of reconstruction results through optimizing strain gauge locations, in which the condition number of pseudo stiffness matrix was used as the objective function, and the condition number is minimized, named iFEM_S_O. Finally, the reconstruction accuracy and robustness and of iFEM, iFEM_S and iFEM_S_O were verified through several numerical examples. The simulated reconstruction results indicated that iFEM_S_O is more accurate and stable than iFEM_S, and iFEM_S is more accurate and stable than iFEM.

Online health monitoring of rotating machine elements using statistical spectral distances

Online health monitoring of rotating machine elements using statistical spectral distances

Machine learning based damage identification in SiC/SiC composites from acoustic emissions using autoencoders

Developing the ability to leverage machine learning (ML) to identify damage mechanisms in heterogeneous materials from their acoustic emissions (AE) has wide-reaching ramifications for multi-modal experimentation. It would allow researchers to augment damage triangulation, lifetime prediction, and high-resolution optical studies with complementary mechanism-informed data streams. However, developing this capability hinges on the collection of ground truth acoustic libraries from damage in realistic geometries. Due to time and monetary considerations, there is a dearth of ground truth libraries which can be used to robustly characterize ML mechanism identification frameworks. Addressing this gap, we present a multi-modal acoustic emission and x-ray computed tomography study where AE is gathered, and subsequently labeled, from SiC/SiC unidirectional composites under monotonic tension. This library is used to demonstrate that acoustic signals from early fiber breaks are obscured by matrix cracking. A first-order micromechanical model is used to explain the origin of this obscuring effect, and identify fundamental limitations of unsupervised frameworks. An autoencoder-based anomaly detector approach is used for the first time to overcome these limitations, additionally demonstrating that the frequency distribution of fiber break acoustic signals is narrow. Implications of these findings for enhanced multi-modal testing and online health monitoring are discussed, and strategies for implementation of supervised damage mechanism identification frameworks are proposed.

An online comprehensive health monitoring system for automotive auxiliary converter

Abstract As an essential part of the electronic system in electric vehicles (EVs), the automotive auxiliary converter (AAC) connects the batteries and burdens the auxiliary power supply. This paper mainly proposes an online comprehensive health monitoring system for AAC. Firstly, based on the control diagram of the LLC resonant converter, three critical components are emerging, including capacitor, optocoupler, and semiconductor. These components can partly reflect the operation condition of the output loop, feedback loop, and control link, respectively. Then the characteristics parameters of these three components, equivalent series resistance (ESR) of the capacitor, current transfer ratio (CTR) of the optocoupler, and on-resistance of the semiconductor jointly construct the three-dimensional health monitoring vector. Finally, the online monitoring accuracy is verified and the proposed system can thus effectively promote the operation and maintenance of AAC. Furthermore, the online health monitoring system enables a flexible expansion, which can be further customized into different kinds of power converters, showing high applicability and practicality.

On fused filament fabrication of PLA nanofiber reinforced PCL matrix-based smart porous scaffolds

In the past, studies were reported on polycaprolactone (PCL) and polylactic acid (PLA) nanofibers (NF)-based scaffolds for tissue engineering applications. But hitherto, little has been reported on the sensing capability of PLA-NF reinforced PCL composite (PPNC)-based smart porous scaffolds for online health monitoring (OHM) of joint strains/ tendon tears/ contusions/ rhabdomyolysis, etc. In this study, smart porous scaffolds (SPS) were fabricated by fused filament fabrication (FFF) using PPNC for OHM (of joint strains/ tendon tears/ contusions/ rhabdomyolysis, etc.) with tuneable sensing and mechanical properties. The results suggest that FFF parameters, zigzag fill pattern, 0.20 mm layer thickness, and 80 % infill percentage are the best-predicted settings for maximum yield stress (σymax) of SPS. Further, a vector network analyzer was used to ascertain the sensing capability of SPS. The resonance frequency (Rf) for PCL (virgin) and PPNC with minimum yield stress (σymin) and σymax were observed as 2.3021, 2.6008, and 2.4315 GHz respectively. The simulated return loss (S11), specific absorption ratio (SAR), and Rf for PCL (−10.7593 dB, 0.977 W/kg, and 2.6450 GHz), PPNC with σymin (−10.5737 dB, 1.437 W/kg and 2.3400 GHz) and PPNC with σymax (−10.9429 dB, 1.155 W/kg and 2.5050 GHz) indicate that experimental data is in good co-relation with the observed values. The results are supported by a scanning electron microscope and differential scanning calorimetry.

Parameter identification based on iterative signal space in blade tip timing

Blade tip timing (BTT), as an effective non-contact measurement technology, is widely used for health monitoring of rotating blades. Due to the installed number of probes is limited by the engine structure, the requirement of BTT sampling frequency is difficult to meet the Nyquist sampling theorem, which leads the BTT signal is severely under-sampled. Therefore, it is difficult to extract vibration parameters, such as frequency, amplitude. Many algorithms was proposed to solve the undersampling problem. In this paper, a new BTT signal processing algorithm is proposed based iterative signal space, which expands individual data into subspace level data geometrically. Compared to some previous methods, the iterative signal space algorithm can identify the blade vibration parameters more accurately, which is verified in both numerical simulation and experiment. Furthermore, different degrees of noise is used to show the feasibility and robustness. The results are very significant for online blade fault diagnosis and health monitoring of turbines.

COVID-19 Online Health Monitoring System to Support Municipal Health Centers.

An online health-monitoring system for COVID-19-infected patients who are staying in hotels and homes was developed using geographical information systems. This system provides display functions for sending health observation forms to infected residents, scoring for medical risk assessment, and centralized management. More than 1,146,000 health observation records were registered in November 2022, and the system contributed to maintaining the functionality of the municipal health center in Sapporo, Japan.

A Review of Robotic Arm Joint Motors and Online Health Monitoring Techniques

A Review of Robotic Arm Joint Motors and Online Health Monitoring Techniques

Digital Twin-Based Online Health Monitoring of Power Electronics Systems with Self-Evolving Compensators and Improved Parameter Identification Capability

Digital Twin-Based Online Health Monitoring of Power Electronics Systems with Self-Evolving Compensators and Improved Parameter Identification Capability

Self-Evolving Digital Twin-Based Online Health Monitoring of Multiphase Boost Converters

Self-Evolving Digital Twin-Based Online Health Monitoring of Multiphase Boost Converters

Output-only modal identification with recursive dynamic mode decomposition for time-varying systems

The time-varying modal identification is a prominent analytical means but challenging task to grasp the dynamic characteristics of time-varying systems, which is important guarantee for real-time control and on-line health monitoring of many engineering structures. In this paper, a data-driven approach for time-varying modal identification without requiring parametric modeling about underlying system is presented. Firstly, the fundamental theory for Dynamic Mode Decomposition (DMD) is reviewed combining the Koopman operator. Subsequently, the DMD incorporated into operational modal identification is examined from the perspective of linear system, and the inner connection between modal parameters of vibration system and eigenvalues and eigenmodes of DMD is established. On the basis of that, a weighted recursive strategy integrated with DMD is employed to investigate the capability of time-varying modal identification, and the main identification procedures are elaborated. Finally, the effectiveness and practicability of the proposed method in extracting modal properties of time-varying systems are investigated by a numerical and experiment example. The results demonstrate that, compared to other methods, the proposed approach has high computational efficiency while ensuring the identification accuracy, exhibiting a good engineering application prospect.

The Health and Functional Age Trend Self-assessment of the Older Workers and Retirees in Ukraine from Online Data Human Health Passport in COVID-19 Pandemic

For a general health assessment of the workers in organized industries, and unorganized groups of the unemployed and retirees, systematic online health monitoring has been used. Health monitoring includes a scale assessment of functional tests. The actual state of population health is still not included in the registers of National Statistics of Ukraine for example workers, pensioners and unemployment. The purpose of the study was systemic online health monitoring, to assess the impact of professional, demographic, and socio-economic factors in the Covid-19 pandemic condition and the limited capacity of the health care system in Ukraine. Methods: The study used the intrinsic capacity assessment scales, the five-question scale to detect muscle dysfunction in older persons, as well as some questions regarding residual performance, psychophysiological methods, and characteristics of the functional age. The questionnaire Human Health Passport 1.1 includes 70 questions; 21 of them were assessed by 1 point for positive answer and zero for negative case. The screening was carried out online on social media to Internet users aged 31 to 90. In the study 377 respondents of which 83.83% were employees. The Human Health Passport screening showed reliability (Cronbach alfa = 0.737), and correlation relationships of the main estimated indicators are significant. So, 54% of respondents do need health care under a physician’s supervision. The 39% of respondents need should convey the proper individual schedule of workout and labour hours, as well as physical activity and breathing exercises because only 7% of respondents are healthy. Correlation analysis of the scale results showed a significant relationship between age, with static balancing, falls during the last 6 months, and the ability to 5 times sit to stand test within 14 seconds. The optimal management of Human Health Passport screening helps to reduce the harmful influence on the environment, and transport expenses for doctor visits and saves the doctor's time for patient examination and prevention of the exposure and the spread of COVID-19. The screening showed the existing reserves of training for health promotion and, the overstrain of the functional systems of the body in the working population - 76%—by the purpose. _________________________________________________________________________________________ Keywords: remote health screening; retirees; biological age; ageing; elderly workers

Health monitoring of carbon fiber-reinforced polymer composites in γ-radiation environment using embedded fiber Bragg grating sensors

Fiber-reinforced polymer (FRP) composites are considered suitable candidates for structural materials of spacecrafts due to their excellent properties of high strength, light weight, and corrosion resistance. An online health monitoring method for FRP composites must be applied to space structures. However, the application of existing health monitoring methods to space structures is limited due to the harsh space environment. Here, carbon fiber-reinforced polymer (CFRP) composites embedded with fiber Bragg grating (FBG) sensors were prepared to explore the feasibility of strain monitoring using embedded FBG sensors in γ-radiation environment. The analysis of the influence of radiation on the strain monitoring demonstrated that the embedded FBG can be successfully applied to the health monitoring of FRP composites in radiation environment.

ABS-PLA-Al composite for digital twinning of heritage buildings

In the past two decades, significant studies have been reported on the use of 3D-printed thermal sensors for online health monitoring of heritage buildings. But hitherto little has been reported on the 4D capability of such thermal sensors for the digital twinning of heritage buildings. In this study, 80% acrylonitrile butadiene styrene (ABS) and 20% polylactic acid (PLA) matrix (by wt %), was reinforced with 5% Al powder (≈45-50 µm) based on melt flow index (MFI). The substrate (as a passive sensor) of ABS-PLA-5 %Al was 3D printed with fused filament fabrication (FFF) for the construction of a micro-strip patch antenna (MPA) as an internet of things (IoT) based solution for online health monitoring and digital twinning of heritage buildings. The dielectric properties (dielectric constant (ɛr) and loss tangent (tanδ)) were explored by using a ring resonator test to ascertain the dimensions and design of the passive sensor. For 4D capabilities, the prepared sensor was exposed to two stimuli (temperature and moisture) for exploring its use as a digital twinning component.

The evaluation of Nonlinear Output Frequency Response Functions based on tailored data-driven modelling for rotor condition monitoring

Poorly assembled or faulty rotors often behave nonlinearly. The accurate extraction of weak nonlinear features is a vital issue. This paper proposes to extract the partial dynamic properties of concern for nonlinear feature based rotor condition monitoring. The realization of this concept relies on a novel tailored data-driven NARX (Nonlinear Auto Regressive with eXogenous input) modelling approach. The tailored NARX model represents part of the system’s dynamic properties of concern. The details are first to use the harmonic product spectrum to determine the rotational speed of the inspected rotor system. After that, the least mean squares method is used to extract the harmonic components from the measured vibration signal. The low-order harmonics of concern are then reconstructed for the tailored NARX modelling. Then the Nonlinear Output Frequency Response Functions are evaluated from the tailored NARX model as features for rotor condition monitoring. Finally, the proposed method is validated by simulation and experiment cases. The results show that the obtained features based on the proposed tailored NARX modelling approach are more robust and more accurate than conventional methods when assessing rotor faults. The proposed approach provides a reference for the inspection of the assembly quality of components such as bearings and the on-line health monitoring of rotating machinery.

Application Research of Optical Fiber Sensing Technology in Power Optical Fiber Communication Systems

The distributed optical fiber sensing system uses optical fiber as the sensing element and signal transmission medium at the same time, and adopts advanced Optical Time-domain Reflectometry (OTDR) technology to detect the changes of temperature and strain at different positions along the optical fiber, thus achieving a truly distributed measurement. Using the Micro-structured Optical Time-domain Reflectometry (M-OTDR) optical fiber sensing access network technology, combined with optical link components such as fault locators, can judge the operating status of optical fiber Composite phase cable (IOPPC) lines, and detect faults (such as breakpoints) in optical fiber communication location and diagnosis. The position of the lightning strike is determined by detecting the change of polarization state caused by the transmitted light in the optical fiber inside the IOPPC before and after a lightning strike, and the positioning error is less than 1%. By observing the Brillouin frequency shift chart, the ice state can be accurately identified and the positioning function can be realized. By monitoring the vibration frequency of the IOPPC cable, the line dancing phenomenon can be detected in time to prevent line faults caused by dancing. The online safety and health monitoring of the operating state of transmission lines are realized.

Elastic wave propagation in thick-walled hollow cylinders for damage localization through inner surface sensing

Thick-walled hollow cylinders (TWHCs) are widely used in engineering structures and transportation systems, exemplified by train axles. The real-time and online health monitoring of such structures is crucial to ensure their structural integrity and operational safety. While elastic-wave-based structural health monitoring (SHM) shows promise, the development of feasible methods strongly relies on a good understanding and exploitation of the wave propagation properties and their interaction with structural defects. TWHCs usually bear multiple wave modes, which is a less investigated and explored topic as compared with thin-walled structures. This work examines this issue and proposes a dedicated damage localization strategy by using the selected waves captured on the inner surface of a TWHC. It is shown that, alongside the quasi-surface-waves on the outer surface, longitudinal waves converted from the thickness-through shear bulk waves are generated to propagate along the inner surface. Their propagation characteristics are exploited for damage localization based on hyperbolic loci methods through inner surface sensing. Numerical studies are conducted to validate the method and assess different transducer configurations, alongside experimental verifications on a benchmark TWHC containing a notch-type defect. Studies provide guidance on damage detection in TWHCs and sensor network design.