Identification Of Health Status Research Articles

Health status identification method of PV plant sensors based on abnormal data analysis

Health status identification method of PV plant sensors based on abnormal data analysis

Health Status Identification and Material Damping Determination of Plants via Non-contact Measurement and Mechanical Modelling

Health Status Identification and Material Damping Determination of Plants via Non-contact Measurement and Mechanical Modelling

Unsupervised transfer learning for intelligent health status identification of bearing in adaptive input length selection

Input length (IL) is an important element in transfer learning (TL) network for intelligent health status identification of bearing (IHSIB). However, fixed IL are used in most studies. In this paper, a TL network via adaptive IL selection module for IHSIB (AILTLN) is proposed, which includes adaptive IL module, feature extractor module, health status identification module, and domain discriminator module. Firstly, an adaptive IL selection module based on envelope spectrum analysis is proposed. The module varies with bearing structure, motor speed, and sampling frequency. Secondly, group convolution, transposed convolution, and instant normalization are constructed in feature extractor. Thirdly, softmax cross-entropy loss function and maximum mean discrepancy are used for health status identification and domain alignment. The TL results of open bearing dataset and high-speed train bearing experiment show that AILTLN is better than the other existing methods in the TL of IHSIB. The ablation study shows the reuse of low-dimensional features and the adaptive IL help to improve to the accuracy of the proposed method.

Rolling bearing fault diagnosis under time-varying speeds based on time-characteristic order spectrum and multi-scale domain adaptation network

In practical industrial applications, rolling bearing generally operates under variable conditions and its vibration signal significantly fluctuates in amplitude and frequency. This increases the feature distribution differences of the bearing fault samples and makes the health status identification of the bearing more difficult. To this end, a new intelligent fault diagnosis method for bearing under time-varying speed conditions is proposed based on time-characteristic order (TCO) spectrum and multi-scale domain adaptation network (MSDAN). Firstly, by using the good noise robustness and high time–frequency aggregation properties of the synchrosqueezed wave packet transform (SSWPT), the TCO spectrum method based on SSWPT is proposed to eliminate the impact of speed fluctuation, reducing the distribution shift of bearing data under time-varying speeds. Secondly, an MSDAN model based on global-local feature fusion is established to extract the domain-invariant features closely related to the bearing fault state from the TCO spectrum. Finally, the local maximum distribution discrepancy is introduced to capture the discriminative fine-grained features. The feasibility of the proposed method is verified in various transfer tasks on two different bearing datasets with time-varying speeds. Compared with some state-of-art methods, the proposed method can eliminate the sample distribution differences under time-varying speeds, significantly improving its accuracy and generalization performance in cross-domain fault diagnosis of rolling bearing.

MTDL-EPDCLD: A Multi-Task Deep-Learning-Based System for Enhanced Precision Detection and Diagnosis of Corn Leaf Diseases

Corn leaf diseases lead to significant losses in agricultural production, posing challenges to global food security. Accurate and timely detection and diagnosis are crucial for implementing effective control measures. In this research, a multi-task deep learning-based system for enhanced precision detection and diagnosis of corn leaf diseases (MTDL-EPDCLD) is proposed to enhance the detection and diagnosis of corn leaf diseases, along with the development of a mobile application utilizing the Qt framework, which is a cross-platform software development framework. The system comprises Task 1 for rapid and accurate health status identification (RAHSI) and Task 2 for fine-grained disease classification with attention (FDCA). A shallow CNN-4 model with a spatial attention mechanism is developed for Task 1, achieving 98.73% accuracy in identifying healthy and diseased corn leaves. For Task 2, a customized MobileNetV3Large-Attention model is designed. It achieves a val_accuracy of 94.44%, and improvements of 4–8% in precision, recall, and F1 score from other mainstream deep learning models. Moreover, the model attains an area under the curve (AUC) of 0.9993, exhibiting an enhancement of 0.002–0.007 compared to other mainstream models. The MTDL-EPDCLD system provides an accurate and efficient tool for corn leaf disease detection and diagnosis, supporting informed decisions on disease management, increased crop yields, and improved food security. This research offers a promising solution for detecting and diagnosing corn leaf diseases, and its continued development and implementation may substantially impact agricultural practices and outcomes.

Health Status Recognition Method for Rotating Machinery Based on Multi-Scale Hybrid Features and Improved Convolutional Neural Networks.

Rotating machinery is susceptible to harsh environmental interference, and fault signal features are challenging to extract, leading to difficulties in health status recognition. This paper proposes multi-scale hybrid features and improved convolutional neural networks (MSCCNN) health status identification methods for rotating machinery. Firstly, the rotating machinery vibration signal is decomposed into intrinsic modal components (IMF) using empirical wavelet decomposition, and multi-scale hybrid feature sets are constructed by simultaneously extracting time-domain, frequency-domain and time-frequency-domain features based on the original vibration signal and the intrinsic modal components it decomposes. Secondly, using correlation coefficients to select features sensitive to degradation, construct rotating machinery health indicators based on kernel principal component analysis and complete health state classification. Finally, a convolutional neural network model (MSCCNN) incorporating multi-scale convolution and hybrid attention mechanism modules is developed for health state identification of rotating machinery, and an improved custom loss function is applied to improve the superiority and generalization ability of the model. The bearing degradation data set of Xi'an Jiaotong University is used to verify the effectiveness of the model. The recognition accuracy of the model is 98.22%, which is 5.83%, 3.30%, 2.29%, 1.52%, and 4.31% higher than that of SVM, CNN, CNN + CBAM, MSCNN, and MSCCNN + conventional features, respectively. The PHM2012 challenge dataset is used to increase the number of samples to validate the model effectiveness, and the model recognition accuracy is 97.67%, which is 5.63%, 1.88%, 1.36%, 1.49%, and 3.69% higher compared to SVM, CNN, CNN + CBAM, MSCNN, and MSCCNN + conventional features methods, respectively. The MSCCNN model recognition accuracy is 98.67% when validated on the degraded dataset of the reducer platform.

Monitoring Chip Branch Failure in Multichip IGBT Modules Based on Gate Charge

The reliability of the multichip insulated gate bipolar transistor (IGBT) modules has been a concern. Condition monitoring is an effective approach to enhance reliability and improve the quality of customer service. This paper proposes a method for monitoring the chip branch failure due to bond wires fatigue in the multichip IGBT modules. The idea is based on the cumulative effect of the gate charge deviation when the chip branch fails. By integrating the turn-on gate current of multiple switching cycles, the health state of multichip IGBT modules is characterized by the capacitor voltage as a health-sensitive parameter that can be extracted in the proposed monitoring circuit integrated into the gate driver. The capacitor voltage decreases significantly when the chip branch occurs due to bond wires fatigue. It is stable during sampling and has a strong immunity to junction temperature, collector current, and collector-emitter voltage. Besides, the method is non-intrusive and has the potential for real-time and online monitoring. These characteristics make the method reliable, easy to implement, and convenient for health status identification. The confirmatory experiment is carried out to verify the feasibility of the method.

Flexible and printable integrated biosensors for monitoring sweat and skin condition

Wearable and flexible sensors are playing increasing roles in health monitoring (like physiological electrical signals and components of biofluids). Therein, sweat as a carrier of informative biomarkers would attract great attention for health status identification. However, most wearable biosensors have a short lifetime with complex fabrication processes and expensive costs, which would largely limit the application scene to some extent. Here, we developed a state-of-the-art flexible and integrated sensor patch with screen-printing technology for in-situ and real-time monitoring of electrolyte balance and skin state. The screen-printed sensor patch was easily fabricated, highly reproducible, disposable and relatively stable, which was extremely for sweat sensors with low cost. The state of art sensors on the patch of Na+, pH, skin impedance and temperature all showed excellent performance with high linearity (coefficient of determinations (R2) are 0.998, 0.994, 0.998 and 0.997, respectively). Besides, the detection ranges of Na+ and pH sensors are wide enough for sweat analysis of 10–100 mM and 2–8, respectively. The proposed device provides a new strategy for real-time sweat analysis, preventing dehydration and skin state monitoring during exercise.

Triclosan elicited biochemical and transcriptomic alterations in Labeo rohita larvae

In the current study, Triclosan (TCS, a commonly used antimicrobial agent) induced alterations in biochemical parameters and gene expression were recorded in the larvae of Labeo rohita after 96 h exposure and 10 days recovery period to find out health status biomarkers. 96 h exposure to 0.06, 0.067 and 0.097 mg/L TCS significantly declined the levels of glucose, triglycerides, urea and uric acid and activity of alkaline phosphatase (ALP), glutamic oxaloacetic transaminase (GOT) and glutamic pyruvic transaminase (GPT). There was a non-significant decline in the levels of cholesterol and total protein but albumin and total bilirubin showed no change. After 10 days of recovery period, trend was opposite for glucose, urea and ALP only. Decline in the expression of trypsin and pancreatic amylase and elevation in creatine kinase during exposure to TCS showed a reverse trend after recovery period. However, concentration dependent elevation of chymotrypsin persisted till the end of recovery period. Principal Component Analysis (PCA) showed association of total protein, ALP, GOT, creatine kinase and pancreatic amylase with PC1 after exposure as well as recovery period. Therefore, these can be considered as important biomolecules for identification of health status of TCS stressed fish.

Women’s Mental Wellness: Coping and Responding to Covid -19

The COVID 19 pandemic greatly creates impact to the women in the education sector, specifically in their mental health. Quantitative in nature, this research assessed how the 200 women – teachers and students - reacted and adjusted with the varying mental adversities amidst the crisis brought about by the pandemic. To better understand the respondents' coping mechanisms and responses, a comprehensive survey, as a research instrument, was conducted. The study revealed that feeling unsafe, less calm, worried, apprehension, being immersed in information confusion, and being in a high emotional state are the most dominant mental adversities experienced by the research participants. To cope with such mental adversities, the respondents exercised health compliance, engaged in open communication, and immersed in entertainment activities, and active campaign against COVID-19. This research highlights the importance of women's mental health status identification amidst a pandemic crisis for academic institutions and the government to introduce intervention programs addressing women's mental adversities during crises.

Health status identification of catenary based on VMD and FA-ELM

Catenary works as a key part in the electric railway traction power supply system, which is exposed outdoors for a long time and the failure rate is very high. Once a failure occurs, it will directly affect the driving safety. Based on the above, a model of identifying the health status for the catenary based on firefly algorithm optimized extreme learning machine combined with variational mode decomposition is proposed in this paper. Variational mode decomposition is used to decompose the original detection curve of catenary into a series of intrinsic mode function components, and the intrinsic mode function components filtered by the correlation coefficient method after decomposing each detection curve are input into the firefly algorithm optimized extreme learning machine model to realize health status identification. Compared with some other models, the results show that the proposed model has better health status identification effect.

Identification of diseases in newborns using advanced acoustic features of cry signals

Our challenge in the current study is to extend research on the cries of newborns for the early diagnosis of different pathologies. This paper proposes a recognition system for healthy and pathological cries using a probabilistic neural network classifier. Two different kinds of features have been used to characterize newborn cry signals: 1) acoustic features such as fundamental frequency glide (F0glide) and resonance frequencies dysregulation (RFsdys); 2) conventional features such as mel-frequency cestrum coefficients.This paper describes the automatic estimation of the proposed characteristics and the performance evaluation of these features in identifying pathological cries. The adopted methods for F0glides and RFsdys estimation are based on the derived function of the F0 contour and the jump "J" of the RFs between two subsequent tunings, respectively. The database used contains 3250 cry samples of full-term and preterm newborns, and includes healthy and pathologic cries.The obtained results indicate the important association between the quantified features and some studied pathologies, and also an improvement in the identification of pathologic cries. The best result obtained is 88.71% for the correct identification of health status of preterm newborns, and 82% for the correct identification of full-term infants with a specific disease. We conclude that using the proposed characteristics improves the diagnosis of pathologies in newborns. Moreover, the method applied in the estimation of these characteristics allows us to extend this study to other uninvestigated pathologies.

Health Degradation Monitoring of Rolling Element Bearing by Growing Self- Organizing Mapping and Clustered Support Vector Machine

Health degradation monitoring of rolling element bearing (REB) is of great significance to ensure safety and availability of mechanical equipment. This paper presents a new online health degradation monitoring method of REB based on growing self-organizing mapping (GSOM) and clustered support vector machine (CSVM). In the proposed method, multidimensional health degradation features of the REB are extracted to reflect health degradation process, including time-domain statistical features, frequency spectrum features, intrinsic mode function energy features, wavelet packet frequency band energy features. Multiple GSOMs are utilized to adaptively fuse each kind of the extracted features for the health indices. CSVM is constructed to achieve accurate health status identification of the REB. A health degradation experimental case of the REB is analyzed to demonstrate the effectiveness of the proposed method. The results show that the proposed method has obvious superior performance compared to other existing methods.

Facial Imagery BMI Algorithm correlates with Normal and Overweight Measured BMI

Efficient procedures to determine health status are in high demand in today's generation. Body Mass Index (BMI) is a global method for determining an individual's health status. This research exploration is to identify if a facial image (photograph) can identify participant's BMI correctly. Sample of 1,210 young adults with a facial image and objective height and weight were used for analysis. Facial BMI (fBMI) was measured through the Local Binary Pattern (LBP)/Active Shape Model (ASM) was used to detect 76 points on each face and measured BMI (mBMI) was calculated by weight in kilograms divided by height in meters squared. Correlation analysis of fBMI to mBMI showed significant correlation between BMIs in the normal and overweight categories (p<.0001). Further analysis indicated the measure to be less efficacious in underweight and obese participants. Matched pairs data for each individual, there was a range of 14.73–49.74 for mBMI and a narrowed spread for fBMI (range = 16.29–28.85) indicating that fBMI detected participant BMI 0.4212 less than mBMI (p<.0004). BMI categories used included: less than 18.5 BMI (0), 18.5–24.9 (1), 25.0–29.9 (2), and equal to or above 30 (3). mBMI had representation of categories across 0–3 BMI, while fBMI representation included just 0–2. This shows less sensitivity of the algorithm to obese individuals. Contingency table analysis indicted 109 participants in the 3rd category of mBMI were placed into a lower category for fBMI. Agreement test for symmetry shows significant disagreement 95% CI [0.0618,0.1446] (p<.0001). Facial imagery identification of health status is a useful measure in human research however; more sensitive measures to identify underweight and obese individuals are warranted.Support or Funding InformationApproval to use the data set was granted by the University of Tennessee Institutional Review Board prior to study implementation. This material is based upon work that is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, under award number 2014‐67001‐21851.

Thinking eHealth

This paper focuses on a mathematical background of an individual health status monitoring system to empower young people to manage their health. The proposed health status monitoring system uses symptoms observed with mobile sensing devices and prior information about health and environment (provided it exists) to define individual physical and psychological status. It assumes that a health status identification process is influenced by many parameters and conditions. It has a flexible logical inference system providing positive psychological influence on young people since full acceptance of recommendations on their behavioral changes towards healthy lifestyles is reached and a correct interpretation is guaranteed. The model and algorithms of the individual health status monitoring system are developed based on the composition inference rule in Zadeh's fuzzy logic. The model allows us to include in the algorithms of logical inference the possibility of masking (by means of a certain health condition) the symptoms of other health situations as well as prior information (if it exists) regarding health and environment. The algorithms are generated by optimizing the truth of a single natural “axiom”, which connects an individual health status (represented by classes of health situations) with symptoms and matrices of influence of health situations on symptoms and masking of symptoms. The new algorithms are fairly different from traditional algorithms, in which the result is produced in the course of numerous single processing rules. Therefore, the use of a composition inference rule makes a health status identification process faster and the obtained results more precise and efficient comparing to traditional algorithms.

利用红外热像的面色识别法辨识中医体质的初步探讨

通过对体质辨识方法进行主客观(宏微观)相结合的现代诠释，形成基于体质辨识的中医健康状态识别、评估系统，并基于红外热像与可见光融合等模式识别技术，将人脸面色识别技术与中医体质辨识法有机结合，使更符合人类视觉感知的自然色彩效果和良好的颜色恒常性，以便提取体质辨识信息，与其他辨识法有机结合构建中医体质多层次辨识系统。 According to the modern interpretation of constitution identification methods in which the subjective and objective (macro and micro) analysis are combined, Traditional Chinese Medicine health status identification and assessment system are formed to be based on the constitution identification. Face recognition technology and Traditional Chinese Medicine constitution identification method are combined with the infrared thermography, visible light image fusion and pattern recognition technology, which makes it more consistent with the natural colour effect of human visual perception and good colour constancy, so as to extract physical identification information and combine with other identification methods to set up a TCM constitution multi-level identification system.