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Unexpected faults in rotating machinery can lead to cascading disruptions of the entire work process, emphasizing the importance of early detection of performance degradation and identification of the current state. To accurately assess the health of a machine, this study introduces an FFT‐based raw vibration data preprocessing and graph representation technique, which analyses changes in frequency bands to detect early degradation trends in vibration data that may appear normal. The approach proposes a methodology that utilizes a graph convolutional autoencoder trained using only normal data to extract health indicators using the differences in the vectors as degradation progresses. This approach has the advantage of using only normal data to detect subtle performance degradation early and effectively represent health indicators accordingly. [ABSTRACT FROM AUTHOR]

Constructing a good health indicator (HI) plays a key role in machinery health monitoring. However, due to the training dataset dependency, most conventional deep-learning-based HIs exhibit inconsistent HI scales and limited cross-domain applicability. This study proposes a deep learning integrated Bayesian health indicator (DBHI) to quantify and evaluate system health states in cross-domain applications accurately. The construction of the DBHI comprises two steps: probability space mapping and probabilistic inference. In the first step, the degradation features are extracted from the data and mapped into a probability space by the proposed adversarial autoencoder model. In the second step, the DBHI is calculated by evaluating the relative risk of the current state based on Bayes' theorem. The effectiveness of the proposed DBHI was validated on two run-to-failure degradation test datasets. In a cross-domain application, the proposed DBHI achieved good performance without retraining compared to other deep learning-based methods that showed invalid results. The proposed DBHI addresses the limitations of conventional deep-learning-based HIs and provides a more reliable and consistent measure of system health, even in a cross-domain application. [ABSTRACT FROM AUTHOR]

Due to the complex and diverse operating conditions of rolling bearings, it is difficult to accurately predict remaining useful life (RUL) of rolling bearings via traditional prediction models. Besides, when a rolling bearing malfunctions, only single channel or single-domain degradation information is considered to bear RUL prediction, and the prediction effect of existing RUL prediction methods is greatly limited. Therefore, in order to address the above issue, a novel method abbreviated as CNN-GRU-MSA with multi-channel feature fusion is proposed for RUL prediction of rolling bearings. Firstly, the statistical features related to the time series are calculated, and the similarity features are constructed based on the obtained statistical features. Then, the sensitive features are selected and fused through specific indicators. Finally, the dual-channel feature fusion is performed to construct a health indicator (HI), which is input into the proposed CNN-GRU-MSA model for training and achieving RUL prediction of rolling bearings. The effectiveness of the proposed method is validated using two benchmark datasets (i.e. IEEE PHM 2012 challenge datasets and XJTU-SY datasets). Experimental results demonstrate the superiority and effectiveness of the proposed method over other similar prediction methods in bearing RUL prediction. [ABSTRACT FROM AUTHOR]

ABSTRACT Rolling bearings are crucial components in a wide variety of machinery. Monitoring their conditions and predicting their remaining useful life (RUL) is vital to prevent unexpected breakdowns, optimize maintenance schedules, and reduce operational costs. This article proposes an approach based on adaptive continuous deep belief networks (ACDBN) and improved kernel extreme learning machine (KELM) to predict the RUL of rolling bearings. In the proposed approach, the ACDBN model is used for extracting hidden fault features and the distance between the initial health state and the real‐time degradation state is used to construct a health indicator (HI). Then, a hybrid kernel extreme learning machine prediction model optimized by the sparrow search algorithm (SSA‐KELM) is proposed to estimate the RUL using the extracted HIs. The SSA is used to find the optimal parameters of the KELM model. The proposed method has been assessed using existing bearing datasets. The obtained results indicate that the proposed method successfully improves RUL prediction accuracy compared to existing approaches in the literature. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries, essential components in new energy vehicles and energy storage stations, play a crucial role in health-status investigation and ensuring safe operation. To address challenges such as limited estimation accuracy and a weak generalization ability in conventional battery state of health (SOH) estimation methods, this study presents an integrated approach for SOH estimation that incorporates multiple health indicators and utilizes the multi-scale convolutional neural network (MSCNN) model. Initially, the aging characteristics of the battery are comprehensively analyzed, and then the health indicators are extracted from the charging data, including the temperature, time, current, voltage, etc., and the statistical transformation is performed. Subsequently, Pearson's method is employed to analyze the correlation between these health indicators and identify those with strong correlations. A regression-prediction model based on the MSCNN model is then developed for estimating battery SOH. Finally, validation using a publicly available lithium-ion battery dataset demonstrates that, under similar operating conditions, the mean absolute error (MAE) for SOH estimation is less than 0.67%, the mean absolute percentage error (MAPE) is less than 0.37%, and the root mean square error (RMSE) is less than 0.74%. The MSCNN has good generalization for datasets with different working conditions. [ABSTRACT FROM AUTHOR]

Copyright of African Journal of Reproductive Health is the property of Women's Health & Action Research Centre and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The health indicator (HI) plays a crucial role in the condition monitoring of the rolling bearing. However, most existing HIs exhibit significant fluctuations when the speed changes. To address the issue, this paper proposes a new HI namely reweighted fault impact strength (RFIS)-HI. First, sub-signals are obtained through a frequency division strategy, and corresponding resampled signals are derived using order tracking. Second, the average impact peak in the time domain is acquired to measure the impact of the signal. According to fault characteristic order (FCO), the ratio of FCOs summation to noise amplitude in the frequency domain is obtained to measure periodicity. Then, the FISgram is constructed for selecting the optimal frequency band. To better quantify the degradation degree of the bearing, different weights are assigned and optimized for constructing RFIS. Finally, the influence of rotational speed on RFIS is eliminated by utilizing prior knowledge. Taking the first 10% of the dataset as baseline data, RFIS-HI is constructed through relative similarity. In this paper, a bearing dataset under time-varying speed conditions and an XJTU-SY dataset are used for verification. Results show that the proposed HI can achieve better trendability, scale similarity, and stability. [ABSTRACT FROM AUTHOR]

In data-driven prognosis methods, the accuracy of predicting the remaining useful life (RUL) of mechanical systems is predominantly contingent upon the efficacy of system health indicators (HI), typically amalgamated from statistical features derived from collected signals. Nevertheless, the majority of extant HI are beset by two principal shortcomings: (1) during traditional data denoising processes, degradation information from raw data is prone to loss owing to the lack of incorporation of the true physical properties of the data; and (2) the performance evaluation of constructed HI is imbalanced due to the influence of network structures on single models, often resulting in strong performance in only one or two indicators. To overcome such shortcomings, a mechanical health indicator construction method based on physical properties was proposed, termed 1D-WGAN-GP Joint attention LSTM-DenseNet. Firstly, artificial sample data is generated by analyzing the physical properties of the original dataset, which is then used to train the 1D-WGAN-GP model to achieve data denoising. Subsequently, the fusion of the attention LSTM (A-LSTM) network and DenseNet network is utilized to extract crucial feature vectors of HI under varying health conditions from the denoised data. Finally, the extracted feature vectors are used to construct system HI using the Euclidean distance method, and these indicators are used for predicting the system's RUL. The results indicate that the proposed method outperformed traditional methods in terms of denoising effectiveness. Further, through ablation experiment analysis, the HI constructed by the proposed method demonstrated obvious complementarity in terms of monotonicity, correlation, robustness, and comprehensive evaluation. In RUL prediction applications, the proposed method also exhibited good performance, thereby validating its effectiveness. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurately estimating the state of health (SOH) of lithium-ion batteries is of great significance in ensuring the safe operation of the battery system. Addressing the issue where traditional SOH estimation methods fail under variable working conditions, an online SOH estimation method for lithium-ion batteries based on equivalent circuit model (ECM) and sparse Gaussian process regression (SGPR) is proposed. During the constant current charging process, the parameters of the ECM of lithium-ion battery are dynamically identified by two online filters, based on which, a condition-insensitive health indicator is constructed. In combination with the SGPR, the indirect SOH estimation is achieved. This method uses the unified signal processing method and feature mapping model under various working conditions, and features strong robustness with low redundancy. The experimental results show that the average absolute error of the method proposed under various working conditions does not exceed 0.94%, and the root mean square error stays below 1.12%. When benchmarked against existing methods, this method has significant advantages in comprehensive performance.

Simple Summary: In the process of calf rearing, it is inevitable to encounter issues of illness and death among calves. Often, due to the inability to detect sicknesses such as diarrhoea in a timely fashion, these sicknesses lead to the calves' demise. This research starts from the practical application needs, and proposes the development of a monitoring system using deep learning technology to monitor the daily standing and lying behaviour of calves to predict their condition and adaptation to the environment. By analysing the standing and lying time of calves, the system can provide early warnings about calves' condition and health status. This research helps to promptly grasp calves' condition and growth status, thereby improving their welfare and management, enhancing the health condition of reared calves, ensuring the quality and safety of meat and milk, and reducing production costs. This research method also offers a new idea for the construction of smart ranches, as the construction of precision and smart ranches is not only a demand of consumers but also an inevitable direction for the development of the breeding industry. Standing and lying are the fundamental behaviours of quadrupedal animals, and the ratio of their durations is a significant indicator of calf health. In this study, we proposed a computer vision method for non-invasively monitoring of calves' behaviours. Cameras were deployed at four viewpoints to monitor six calves on six consecutive days. YOLOv8n was trained to detect standing and lying calves. Daily behavioural budget was then summarised and analysed based on automatic inference on untrained data. The results show a mean average precision of 0.995 and an average inference speed of 333 frames per second. The maximum error in the estimated daily standing and lying time for a total of 8 calf-days is less than 14 min. Calves with diarrhoea had about 2 h more daily lying time (p < 0.002), 2.65 more daily lying bouts (p < 0.049), and 4.3 min less daily lying bout duration (p = 0.5) compared to healthy calves. The proposed method can help in understanding calves' health status based on automatically measured standing and lying time, thereby improving their welfare and management on the farm. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

HUMS (Health and Usage Monitoring Systems) provide enhanced safety and availability by alerting maintenance personnel when components (shaft/gears/bearings) have degraded or become damaged. Proactive maintenance ensures that a degraded component will not fail in flight. Often, the damage is from high cycle fatigue or other relatively slow degradation processes. However, some events, such as maintenance, result in a step-change in the component's health. While high cycle fatigue degradation can be trended, a step-change in component health presents a challenge. This paper describes the development of two methods for step-change detection. These methods would allow the automated capture of maintenance or other events resulting in a step-change in component health. [ABSTRACT FROM AUTHOR]

Copyright of Rolling Stock (1002-7602) is the property of Rolling Stock Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As the vital auxiliary machine of the coal-fired power plant, monitoring the real-time operating status of coal mills is critical to the secure and stable operation of the power plant. In this study, a new method of construction of the coal mill health indicator (HI) is proposed, and the operation condition monitoring approaches of the device are designed based on the HI value. Firstly, an improved coal mill dynamic model considering the joint influence of drying force, ventilation force, and grinding force is established, and a synchronous optimization approach of model structures and parameters based on the genetic algorithm is designed. Then the deviation between the model output and the actual value is computed by the designed distance measuring approach, and the typical fault characteristic factors are designed based on the relation between the dynamic model and the actual operating state. And the HI value is calculated by fusing the deviation with the characteristic factors. Finally, the HI value is applied to the process of operation condition evaluation, fault diagnosis, and trend prediction, and has obtained favorable application effects. The results of this research show that the established HI value can reflect the operating status of the coal mill promptly and accurately, and the monitoring method designed based on the values have satisfactory practicality. [ABSTRACT FROM AUTHOR]

Battery energy storage systems, especially lithium-ion batteries, have become more common in power systems owing to their numerous advantages, such as supporting voltage and frequency regulation and contributing to peak shaving and load shifting. However, when the battery reaches its end-of-life, it becomes more unstable, leading to a higher probability of system operation failure and safety accidents. Therefore, to accurately predict the State of Health (SOH) and the Remaining Useful Life (RUL) of a battery system, a prediction method is proposed in this paper based on Empirical Mode Decomposition (EMD), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN), and Attention Mechanism (AM). Firstly, capacity and different health indicators with high correlation extracted from the battery’s charging and discharging characteristics are considered inputs. Then, the EMD method decomposes the battery data into several intrinsic mode functions (IMFs) and a residual. In the second part, with IMFs and a residual as input parameters, the SOH and RUL of different battery datasets are predicted by using the combined model CNN-BiLSTM-AM. To validate the accuracy of the proposed method, different comparative models are considered and carried out on CALCE and NASA battery degradation datasets. The results illustrate that the errors of the proposed method, which are root mean square error and mean absolute error are at least 48% and 19% more accurate than others in all battery datasets, showing the effectiveness and accuracy of the proposed model in predicting the SOH and RUL of the battery.

Cervical cancer is the main neoplasm among women, but it is not the only pathology that affects the female population. The "Previne Brasil" program was created by the Ministry of Health to outline preventive strategies and measures for specific Primary Health Care populations. Under the program, the collection of cytopathological tests is currently part of indicator 4 of "Previne Brasil", which uses strategic actions in women's health care. The aim was therefore to identify the impact of indicator 4 on women's health care, by comparing the perceptions of health professionals and service users. This is a descriptive study of a quantitative nature, using self-administered questionnaires and carried out in all the Basic Health Units in the municipality of Imaculada-PB, with doctors, nurses and women aged 25-64 who have their cytopathology collected at the UBS. From the data collected, it was possible to identify that the implementation of the indicator is of great importance for improving women's health care, analyzing the perception of users and professionals about the care provided and exposing weaknesses in the performance of the exam and in the indicator, which does not reflect well the target group it is intended for, as well as suggesting measures to expand women's health care. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries, essential components in new energy vehicles and energy storage stations, play a crucial role in health-status investigation and ensuring safe operation. To address challenges such as limited estimation accuracy and a weak generalization ability in conventional battery state of health (SOH) estimation methods, this study presents an integrated approach for SOH estimation that incorporates multiple health indicators and utilizes the multi-scale convolutional neural network (MSCNN) model. Initially, the aging characteristics of the battery are comprehensively analyzed, and then the health indicators are extracted from the charging data, including the temperature, time, current, voltage, etc., and the statistical transformation is performed. Subsequently, Pearson’s method is employed to analyze the correlation between these health indicators and identify those with strong correlations. A regression-prediction model based on the MSCNN model is then developed for estimating battery SOH. Finally, validation using a publicly available lithium-ion battery dataset demonstrates that, under similar operating conditions, the mean absolute error (MAE) for SOH estimation is less than 0.67%, the mean absolute percentage error (MAPE) is less than 0.37%, and the root mean square error (RMSE) is less than 0.74%. The MSCNN has good generalization for datasets with different working conditions.

Background: In Brazil, the Ministry of Health (MH) monitors leprosy using 15 indicators, with the aim of implementing and evaluating evidence-based public policies. However, an excessive number of variables can complicate the definition of objectives and verification of epidemiological goals. Methods: In this paper, we develop the Global Leprosy Assessment Index (GLAI), a composite measure that integrates two key dimensions for the control the disease: epidemiological and operational. Using a confirmatory factor analysis model to examine 2020 state-level data, we have standardized GLAI to a range of 0 to 1. Results: Higher values within this range indicate a greater severity of the disease. The mean value of the GLAI was 0.67, with a standard deviation of 0.22. Roraima has the highest value, followed by Paraíba with 0.88 while Tocantins records the lowest value of the indicator, followed by Mato Grosso with 0.14. The epidemiological and operational indicators have a positive but statistically insignificant correlation (r = 0.25; p-value = 0.20). Conclusions: The development of evidence-based public policies depends on the availability of valid and reliable indicators. The GLAI presented in this paper is easily reproducible and can be used to monitor the disease with disaggregated information. Furthermore, the GLAI has the potential to serve as a more robust parameter for evaluating the impact of actions designed to eradicate leprosy in Brazil.

Remaining Useful Life (RUL) prediction is an essential aspect of Prognostics and Health Management (PHM), facilitating the assessment of mechanical components' health statuses and their times to failure. Currently, most deep learning-based RUL prediction methods can achieve accurate RUL point estimations. However, due to sample variability and degradation randomness, point estimations may contain uncertainties. To obtain both RUL prediction values and their corresponding uncertainty estimations, this paper proposes a novel hybrid-driven prediction method that effectively combines an Asymmetric Dual-Channel AutoEncoder and the Nonlinear Wiener Process (ADCAE-NWP). To achieve comprehensive feature extraction, two feature extraction channels are parallelly combined in the encoder. Moreover, to reduce the space-time overhead of the model training process, an asymmetric form of the autoencoder is composed by using only the fully connected layer in the decoder. Subsequently, the ADCAE model is trained to construct health indicators in an unsupervised manner. Finally, the RUL Probability Density Functions (PDFs) are calculated using the NWP. RUL predictions containing uncertainty estimations are obtained by calculating expectations over confidence intervals. The proposed model is experimentally validated and compared on two datasets, and the results demonstrate that the proposed scheme achieves better prediction performance than competing approaches. [ABSTRACT FROM AUTHOR]

Copyright of Eastern Mediterranean Health Journal is the property of World Health Organization and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a hybrid convolutional neural network (CNN)-bidirectional gated recurrent unit (BiGRU) model is integrated with the bootstrap method to endow the deep learning (DL) based prognostic method with the quantification capability of the prognostic intervals. The proposed hybrid method contains three parts: (I) The complete ensemble empirical mode decomposition with adaptive noise and principal component analysis and the CNN-BiGRU are utilized to automatically construct the health indicator (HI). (II) 3 σ criterion is employed to detect the first predicting time based on the HIs of rolling bearings. (III) The bootstrap method is imposed to endow the proposed DL method with the quantification capability of the prognostic intervals. The experimental validation is carried out on the XJTU-SY bearing dataset and the proposed method outperforms the other four methods in the majority of cases. In addition, the proposed method not only comprehensively considers the fault prognosis error caused by model parameters and noise, but also considers the prediction error caused by different combinations of features on the model. [ABSTRACT FROM AUTHOR]

Electromagnetic coils are indispensable components for energy conversion and transformation in various systems across industries. However, electromagnetic coil insulation failure occurs frequently, which can lead to serious consequences. To facilitate predictive maintenance for industrial systems, it is essential to monitor insulation degradation prior to the formation of turn-to-turn shorts. This paper experimentally investigates coil insulation degradation from both macro and micro perspectives. At the macro level, an evaluation index based on a weighted linear combination of trend, monotonicity and robustness is proposed to construct a degradation-sensitive health indicator (DSHI) based on high-frequency electrical response parameters for precise insulation degradation monitoring. While at the micro level, a coil finite element analysis and twisted pair accelerated degradation test are conducted to obtain the actual turn-to-turn insulation status. The correlation analysis between macroscopic and microscopic effects of insulation degradation is used to verify the proposed DSHI-based method. Further, it helps to determine the threshold of DSHI. This breakthrough opens new possibilities for predictive maintenance for industrial equipment that incorporates coils. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Standing and lying are the fundamental behaviours of quadrupedal animals, and the ratio of their durations is a significant indicator of calf health. In this study, we proposed a computer vision method for non-invasively monitoring of calves’ behaviours. Cameras were deployed at four viewpoints to monitor six calves on six consecutive days. YOLOv8n was trained to detect standing and lying calves. Daily behavioural budget was then summarised and analysed based on automatic inference on untrained data. The results show a mean average precision of 0.995 and an average inference speed of 333 frames per second. The maximum error in the estimated daily standing and lying time for a total of 8 calf-days is less than 14 min. Calves with diarrhoea had about 2 h more daily lying time (p < 0.002), 2.65 more daily lying bouts (p < 0.049), and 4.3 min less daily lying bout duration (p = 0.5) compared to healthy calves. The proposed method can help in understanding calves’ health status based on automatically measured standing and lying time, thereby improving their welfare and management on the farm.

Afghanistan has been in an active state of conflict and war for twenty continuous years. Social services like health and education have been badly affected, facing issues such as service disruption, brain drain, and generalized instability. Health indices that provide proxy indicators for general population wellness, such as maternal health, child mortality, and immunization coverage, show that the health services available to the Afghan population are sub-optimal. Investment in social service and interventions has increased. The World Bank and the United Nations through its agencies (The World Health Organization (WHO) and United Nations' Children's Fund (UNICEF) are providing social support through targeted and strategic programs. However, the topographic and environmental realities of Afghanistan, with its broad mountain coverage, propensity toward natural disasters, and latent conflict, has made data and information gathering arduous. Since data is essential for measurement and management, the WHO Health Emergencies (WHE) information management unit at WHO Afghanistan has delivered an innovative form of data analysis, specialized and targeted at providing improved information on communities that are not adequately covered by health services. Deploying a geographical information system (GIS) approach, the WHE team has collated primary and secondary data from a combination of datasets to produce a far-reaching piece of analysis. The analysis of underserved communities in hard to reach, remote locations, provides a live, evidence-based information product. This provides a working tool that is essential to primary health programming and intervention in Afghanistan. The estimates show that approximately 9.5 million individuals in 22,181 villages across 34 provinces are underserved by primary health services. This paper is presented to explain the underpinning methodology.

The prediction of system degradation is very important as it serves as an important basis for the formulation of condition-based maintenance strategies. An effective health indicator (HI) plays a key role in the prediction of system degradation as it enables vital information for critical tasks ranging from fault diagnosis to remaining useful life prediction. To address this issue, a method for monitoring data fusion and health indicator construction based on an autoencoder (AE) and a long short-term memory (LSTM) network is proposed in this study to improve the predictability and effectiveness of health indicators. Firstly, an unsupervised method and overall framework for HI construction is built based on a deep autoencoder and an LSTM neural network. The neural network is trained fully based on the normal operating monitoring data and then the construction error of the AE model is adopted as the health indicator of the system. Secondly, we propose related machine learning techniques for monitoring data processing to overcome the issue of data fusion, such as mutual information for sensor selection and t-distributed stochastic neighbor embedding (T-SNE) for operating condition identification. Thirdly, in order to verify the performance of the proposed method, experiments are conducted based on the CMAPSS dataset and results are compared with algorithms of principal component analysis (PCA) and a vanilla autoencoder model. Result shows that the LSTM-AE model outperforms the PCA and Vanilla-AE model in the metrics of monotonicity, trendability, prognosability, and fitness. Fourthly, in order to analyze the impact of the time step of the LSMT-AE model on HI construction, we construct and analyze the system HI curve under different time steps of 5, 10, 15, 20, and 25 cycles. Finally, the results demonstrate that the proposed method for HI construction can effectively characterize the health state of a system, which is helpful for the development of further failure prognostics and converting the scheduled maintenance into condition-based maintenance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A healthy ecosystem is a subject matter of interest because of its rich biodiversity. But natural climatic changes, as well as various man-made anthropogenic factors, not only emit greenhouse gases into the atmosphere resulting in global warming but influence various ecosystems of the earth. The aquatic ecosystem is one such example where the dolphin, with its complex behaviour, is recognized as one of the most intelligent members. The natural hydrodynamic body system speeds up underwater and the SONAR system communication technology helps dolphins to work as an efficient aquatic health indicator. But human activities, including aquatic poaching and fishing, creating underwater noise pollution and undesirable infrastructures and producing debris from their generated wastes obstruct the proper functioning of these systems and ultimately contribute to their habitat loss. So, dolphins’ ( Gangetic ones in India) disappearance from their comfortable living zone was inevitable and observed in the past few decades as they migrated to areas with more favourable conditions. The available information related to the rejuvenation in the qualities of the Gangetic water and the restoration of the dolphin’s habitat during COVID pandemic confirmed the absence of the negative impact of human activities on the Gangetic ecosystem. Hence, identification of the important steps to be implemented by the concerned authorities in post-COVID situation and their maintenance by the common people is necessary to balance such important aquatic biodiversity in the long run, keeping the country’s developmental activities operational. This review article is an endeavour towards that direction. [ABSTRACT FROM AUTHOR]

Abstract Background Anthropogenic disturbance has the potential to negatively affect wildlife health by altering food availability and diet composition, increasing the exposure to agrochemicals, and intensifying the contact with humans, domestic animals, and their pathogens. However, the impact of these factors on the fecal microbiome composition of wildlife hosts and its link to host health modulation remains barely explored. Here we investigated the composition of the fecal bacterial microbiome of the insectivorous bat Kuhl’s pipistrelle (Pipistrellus kuhlii) dwelling in four environmental contexts with different levels of anthropogenic pressure. We analyzed their microbiome composition, structure and diversity through full-length 16S rRNA metabarcoding using the nanopore long-read sequencer MinION™. We hypothesized that the bacterial community structure of fecal samples would vary across the different scenarios, showing a decreased diversity and richness in samples from disturbed ecosystems. Results The fecal microbiomes of 31 bats from 4 scenarios were sequenced. A total of 4,829,302 reads were obtained with a taxonomic assignment percentage of 99.9% at genus level. Most abundant genera across all scenarios were Enterococcus, Escherichia/Shigella, Bacillus and Enterobacter. Alpha diversity varied significantly between the four scenarios (p

The aim of this paper is to argue the case of using life expectancy at age 60 (LE60) as a significant health indicator closely related to sustainable economic development. To this purpose, we investigate the impact of GDP on LE60 in parallel with the impact of GDP on Infant Mortality Rate (IMR). The rationale for selecting IMR as a comparison indicator is twofold. First, the relationship between IMR and GDP has been widely studied. Second, the two indicators display opposite trajectories, making the comparison more striking. For our comparison, we conduct several statistical analyses on LE60, IMR and GDP using global country data grouped by income level and region. Our results endorse the effect of GDP on LE60 and IMR and suggest a differentiation of the effect based on region and ultimately on income. We observe that as countries develop, their IMR values lower and their LE60 values increase. We conclude that, once countries reach the upper stages of development, LE60 becomes a better health indicator than IMR.

To realize the online rapid prediction of the remaining useful life (RUL) of electric vehicle (EV) power battery under direct current (DC) fast charging conditions and reduce the influence of complex health indicator (HI) on the prediction accuracy, an adaptive prediction method of the RUL of EV power battery based on whale swarm algorithm–long short-term memory (WSA-LSTM) algorithm is proposed in this paper. Firstly, a complete set of HI is built based on a dynamic data-driven application system (DDDAS) to ensure the real-time combination of condition monitoring data and simulation systems. Then, combined with the calculation of Pearson and Spearman correlation coefficients and the entropy weight method, the main features that affect the battery capacity are screened out to reduce the interference of secondary HI on RUL prediction. Thirdly, the whale swarm algorithm is used to globally optimize the hyperparameters of the long short-term memory to achieve rapid RUL prediction. Finally, we conduct simulation experiments based on real-time data under EV DC charging conditions. The simulation results show that the HI screening method proposed in this paper is effective, and the RUL prediction method can achieve a fast and accurate prediction of DC fast charging conditions.

This paper proposes and evaluates the behavior of a new health indicator to estimate the capacity fade of lithium-ion batteries and their state of health (SOH). This health indicator is advantageous because it does not require the acquisition of data from full charge–discharge cycles, since it is calculated within a narrow SOC interval where the voltage vs. SOC relationship is very linear and that is within the usual transit range for most practical charge and discharge cycles. As a result, only a small fraction of the data points of a full charge–discharge cycle are required, reducing storage and computational resources while providing accurate results. Finally, by using the battery model defined by the Nernst equation, the behavior of future charge–discharge cycles can be accurately predicted, as shown by the results presented in this paper. The proposed approach requires the application of appropriate signal processing techniques, from discrete wavelet filtering to prediction methods based on linear fitting and autoregressive integrated moving average algorithms. [ABSTRACT FROM AUTHOR]

The health condition of milling cutters (HCOMC) could heavily affect workpiece quality. However, it is extremely difficult to be quantified online. To solve this problem, an online quantitative monitoring method (OQM) is proposed based on a deep convolutional autoencoder (CAE). In this method, a health indicator (HI) is constructed for fast HCOMC monitoring. The OQM is composed of two parts, offline training and online monitoring. In the offline stage, the multi-sensor monitoring data that record in the cutter normal wear stage (named normal wear data, NWD) are selected from a subsampled life testing dataset to train a deep CAE. In the online stage, each monitoring data segment (MDS) is directly input into the trained CAE to obtain deep representations. Then, the HI is constructed by the mean square error (MSE) between the MDS and the deep representations to monitor the HCOMC. It is called convolutional-autoencoder-reconstruction-error-based health indicator (CARE-HI). In addition to the above-mentioned method, a new metric named isometric fusion metric (IFM) is also designed to assess HI. IFM is able to address the uneven problem of property contribution when using some widely used HI metrics. In the experiment, 28 milling cutters were subjected to cutting experiments under different working conditions. The experimental result demonstrates that the proposed OQM can efficiently improve feature quality and precisely monitor HCOMC. It also illustrates that the CARE-HI outperformed some existing ones in five metric dimensions. Therefore, the proposed CARE-HI can provide more accurate guidance for tool changing in machining. [ABSTRACT FROM AUTHOR]

Despite an increasing appreciation of the importance of host–microbe interaction in healthy growth, information on gut microbiota changes of the Chinese giant salamander (Andrias davidianus) during growth is still lacking. Moreover, it is interesting to identify gut microbial structure for further monitoring A. davidianus health. This study explored the composition and functional characteristics of gut bacteria in different growth periods, including tadpole stage (ADT), gills internalization stage (ADG), 1 year age (ADY), 2 year age (ADE), and 3 year age (ADS), using high-throughput sequencing. The results showed that significant differences were observed in microbial community composition and abundance among different growth groups. The diversity and abundance of intestinal flora gradually reduced from larvae to adult stages. Overall, the gut microbial communities were mainly composed of Fusobacteriota, Firmicutes, Bacteroidota, and Proteobacteria. More specifically, the Cetobacterium genus was the most dominant, followed by Lactobacillus and Candidatus Amphibiichlamydia. Interestingly, Candidatus Amphibiichlamydia, a special species related to amphibian diseases, could be a promising indicator for healthy monitoring during A. davidianus growth. These results could be an important reference for future research on the relationship between the host and microbiota and also provide basic data for the artificial feeding of A. davidianus. [ABSTRACT FROM AUTHOR]

Rotating machines are widely used in several fields such as railways, renewable energies, robotics, etc. This diversity of application implies a large variety of faults of critical components susceptible to fail. For this purpose, prognostics and health management (PHM) is deployed to effectively monitor these components through the detection, diagnostics as well as prognostics of faults. In the literature, there exist numerous methods to ensure the above monitoring activities. However, few of them consider different failure types using heterogeneous data and various operating conditions. Also, there are no dominant methods that can be generalized for monitoring. For this reason, the genericity of these methods and their applicability in several systems is a crucial issue. To help researchers to achieve the above challenges, this paper presents a detailed description of data sources from experimental test benches. These data-sets correspond to different case studies that monitor the health states of multiple critical components in various operating conditions using numerous sensors.

Background: Anthropogenic disturbance has the potential to negatively affect wildlife health by altering food availability and diet composition, increasing the exposure to agrochemicals, and intensifying the contact with humans, domestic animals, and their pathogens. However, the impact of these factors on the fecal microbiome composition of wildlife hosts and its link to host health modulation remains barely explored. Here we investigated the composition of the fecal bacterial microbiome of the insectivorous bat Kuhl's pipistrelle (Pipistrellus kuhlii) dwelling in four environmental contexts with different levels of anthropogenic pressure. We analyzed their microbiome composition, structure and diversity through full-length 16S rRNA metabarcoding using the nanopore long-read sequencer MinION™. We hypothesized that the bacterial community structure of fecal samples would vary across the different scenarios, showing a decreased diversity and richness in samples from disturbed ecosystems. Results: The fecal microbiomes of 31 bats from 4 scenarios were sequenced. A total of 4,829,302 reads were obtained with a taxonomic assignment percentage of 99.9% at genus level. Most abundant genera across all scenarios were Enterococcus, Escherichia/Shigella, Bacillus and Enterobacter. Alpha diversity varied significantly between the four scenarios (p < 0.05), showing the lowest Shannon index in bats from urban and intensive agriculture landscapes, while the highest alpha diversity value was found in near pristine landscapes. Beta diversity obtained by Bray–Curtis distance showed weak statistical differentiation of bacterial taxonomic profiles among scenarios. Furthermore, core community analysis showed that 1,293 genera were shared among localities. Differential abundance analyses showed that the highest differentially abundant taxa were found in near pristine landscapes, with the exception of the family Alcaligenaceae, which was also overrepresented in urban and intensive agriculture landscapes. Conclusions: This study suggests that near pristine and undisturbed landscapes could promote a more resilient gut microbiome in wild populations of P. kuhlii. These results highlight the potential of the fecal microbiome as a non-invasive bioindicator to assess insectivorous bats' health and as a key element of landscape conservation strategies. [ABSTRACT FROM AUTHOR]

Battery capacity is an important metric for evaluating and predicting the health status of lithium-ion batteries. In order to determine the answer, the battery's capacity must be, with some difficulty, directly measured online with existing methods. This paper proposes a multi-dimensional health indicator (HI) battery state of health (SOH) prediction method involving the analysis of the battery equivalent circuit model and constant current discharge characteristic curve. The values of polarization resistance, polarization capacitance, and initial discharge resistance are identified as the health indicators reflective of the battery's state of health. Moreover, the retention strategy genetic algorithm (e-GA) selects the optimal voltage drop segment, and the corresponding equal voltage drop discharge time is also used as a health indicator. Based on the above health indicator selection strategy, a battery SOH prediction model based on particle swarm optimization (PSO) and LSTM neural network is constructed, and its accuracy is validated. The experimental results demonstrate that the suggested strategy is accurate and generalizable. Compared with the prediction model with single health indicator input, the accuracy is increased by 0.79%. [ABSTRACT FROM AUTHOR]

The search for sustainable but environmentally friendly medium of harnessing energy for the automotive industry has led to the evolution of various energy generating and converting devices. One of such energy converting device is fuel cells. Despite the merits associated to the performance of proton exchange membrane (PEM) fuel cells, issues relating to the cost and remaining useful life prediction still persist hence impeding their further commercialization especially in the automotive industry. In spite of the progress made by the research community in developing various predictive models in order to mitigate these challenges, the accuracy of these developed models has lately become active research direction. The current study explored the accuracy of recurrent neural network, bi recurrent neural network, combined convolutional neural network and bi recurrent neural network in predicting the remaining useful life of a PEM fuel cell. The presence of the convolutional neural network was mainly to ensure pre – processing of the bi recurrent neural network for the extraction of high level features. To reduce the possibility of overfitting, a dropout approach coupled with callback technique is adopted. Validation of the model was executed based on an experimental data. The outcome of the investigation highlighted the key role of the convolutional neural network in improving the accuracy of the recurrent neural network. Comparing the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the present model with other models, the developed model yielded the least values indicating a higher accuracy. For instance, the relative error showed a least value of 0.12 for the combined convolutional neural network and bi recurrent neural network compared to the long short term memory with 2.61 reported in previous studies. • Evaluation of various existing predictive models for PEM fuel cells is studied. • Convolutional neural network accuracy in predicting the rate of degradation for PEM fuel cell is captured. • The developed predictive model have higher accuracy compared to other models. • Future studies should explore further combination of various predictive models and the effect on the accuracy. [ABSTRACT FROM AUTHOR]

The vibration and acoustic signals collected from rotating machinery are often non-stationary and aperiodic, and it is a challenge to post-process and extract the defect sensitive health indicators. In this paper, we demonstrate how the estimated Hurst exponent of such measured data can be advantageous for analyzing non-stationary and aperiodic data due to its self-similarity and scale-invariance properties. To illustrate this, the paper demonstrates detection of fault diagnostics of a multi-stage gearbox operating under fluctuating speeds through estimated Hurst exponent of the raw vibration and acoustic signals as a health indicator. Thirteen health states of the gearbox are considered, and the raw vibration and acoustic signals are collected. The Hurst exponents are calculated using three different approaches: generalized Hurst exponent (q = 1, 2, 3, and 4), rescale range statistical (R/S) analysis, and dispersion analysis from the vibration and acoustic signals. Three different health indicator datasets are formulated and subjected to feature learning through conventional machine-learning (decision tree and support vector machine) and advanced machine-learning (deep-learning) classifiers. The effectiveness of these datasets while discriminating between the health states of the gearbox is investigated, yielding classification accuracies of 96.4% when compared with the individual health indicator datasets. The ability of the fault diagnosis and defect severity analysis with reduced reliance on the signal post-processing algorithms is demonstrated. [ABSTRACT FROM AUTHOR]

Accurately predicting the remaining useful life (RUL) of a key component, such as gear, is significant for guaranteeing the safe operation of mechanical equipment and making a proper maintenance plan. The health indicator (HI) plays an essential role in the data-driven RUL prediction technique. HI can be constructed from the perspective of the data distribution discrepancy. However, some existing methods cannot utilize different types of distributions to estimate the distribution discrepancy in various domains. In addition, the constructed HI may not comprehensively describe the tendency of performance degradation by using a type of distribution to obtain the distribution discrepancy in a domain. To overcome these challenging problems, a novel lognormal-normal mixture model (LNMM) that utilizes lognormal and normal distributions is constructed to estimate data distributions from two data domains, including the raw data domain and exponentially transformed data domain. Then, the distribution contact ratio metric (DCRM) is applied to calculate the discrepancies between benchmark distribution of healthy data and distributions of whole life-cycle data in two domains. The gear HI is generated without supervision by combining the DCRMs of two domains. The developed unsupervised HI is employed to estimate gear's RUL via an improved multi-hierarchical long-term memory augmented network. Finally, the experimental results indicate the feasibility and merit of the developed LNMM in gear HI construction. The LNMM-based HI has a better predictive efficacy than the conventional and state-of-the-art unsupervised HIs. [ABSTRACT FROM AUTHOR]

Recently, the Shapley additive explanations models have been extensively studied to enhance explainability of artificial intelligence algorithms, while most of them simply use Shapley additive explanations to rank or measure the importance of different features. In this study, a novel methodology that studies the relation between fault characteristic frequencies and Shapley values generated by local interpretability Shapley additive explanations for machine health monitoring is proposed. Firstly, a simulation model is introduced to generate vibration signals at different health conditions and their spectral amplitudes transformed from Fourier transform are used to investigate the relationship between fault characteristic frequencies and local interpretability Shapley values. It is interestingly found that Shapley values can be used to locate fault characteristic frequencies. Moreover, most of them have negative values in a normal stage and have positive values in an abnormal stage. Based on this finding and Shapley additive explanations, a health indicator construction methodology is proposed to continuously monitor incipient machine faults. Subsequently, an automatic signal filtering method is proposed to remove and eliminate burrs and noise in Shapley values so that fault characteristic frequencies can be clearly revealed by Shapley values for physical fault diagnosis. Two run-to-failure cases are conducted to demonstrate the effectiveness of the proposed methodology and then the superiority of this study is demonstrated by comparing with existing methods for health indicator construction and fault diagnosis, including sparsity parameters, Hjorth parameters, and fast Kurtogram. Comparison results show that the proposed health indicator is more sensitive to the time of incipient fault initiation and interpretable fault diagnosis based on Shapley values has a robust performance. This study first sheds a light on the relationship between fault characteristic frequencies and Shapley values under the scenario of continuous machine health monitoring and seamlessly guides applicants to realize Shapley additive explanations based incipient fault detection and diagnosis. • The relation between fault characteristic frequencies and local interpretability Shapley values studied. • It is newly found that Shapley values can be used to locate fault characteristic frequencies. • A health indicator construction methodology is proposed based on Shapley values. • An automatic signal filtering method is proposed to remove and eliminate burrs and noise in Shapley values. [ABSTRACT FROM AUTHOR]

Electromagnetic coils are indispensable components for energy conversion and transformation in various systems across industries. However, electromagnetic coil insulation failure occurs frequently, which can lead to serious consequences. To facilitate predictive maintenance for industrial systems, it is essential to monitor insulation degradation prior to the formation of turn-to-turn shorts. This paper experimentally investigates coil insulation degradation from both macro and micro perspectives. At the macro level, an evaluation index based on a weighted linear combination of trend, monotonicity and robustness is proposed to construct a degradation-sensitive health indicator (DSHI) based on high-frequency electrical response parameters for precise insulation degradation monitoring. While at the micro level, a coil finite element analysis and twisted pair accelerated degradation test are conducted to obtain the actual turn-to-turn insulation status. The correlation analysis between macroscopic and microscopic effects of insulation degradation is used to verify the proposed DSHI-based method. Further, it helps to determine the threshold of DSHI. This breakthrough opens new possibilities for predictive maintenance for industrial equipment that incorporates coils.