Current Time Series Data Research Articles

A Fault Diagnosis Approach Utilizing Artificial Intelligence for Maritime Power Systems within an Integrated Digital Twin Framework

This research focuses on enhancing the preventive maintenance strategies currently employed for induction motors within ship propulsion systems, advocating for a shift towards a predictive maintenance model. It introduces a real-time monitoring framework that continuously observes the induction motor, providing essential support to maintenance personnel. The motor operates under a range of environmental and operational conditions, including temperature fluctuations, rotational speeds, and mechanical loads. These variations can obscure the current time series data, potentially masking signs of actual damage and hindering effective damage detection. To tackle this issue, the proposed framework utilizes artificial intelligence (AI) technology, specifically the well-established autoencoder, in conjunction with the Mahalanobis statistical distance. This approach accounts for the diverse operating conditions during the training phase, allowing it to model complex, non-linear relationships and effectively differentiate between normal and anomalous states. The framework is integrated into a decision support platform designed for real-time operations in maritime settings, offering a sophisticated system architecture that aims to align advanced damage detection methodologies with the maritime industry’s need for real-time, user-friendly solutions.

Fluvial Delivery and Wave Resuspension of Sediment in a Sheltered, Urbanized Pacific Northwest Estuary

The sequence and timing of sediment delivery and redistribution in coastal systems is important for shoreline stability, ecosystem services, and remediation planning. In temperate estuaries, understanding the role of fluvial sediment delivery and dispersal relative to wind and wave remobilization processes is particularly important to address the fate of contaminants, many of which adsorb to fine particles, and to assess changes in coastal systems under projected changes in climate. Here we present an integrated analysis of observations at multiple timescales to evaluate sediment dynamics and the sedimentary coupling between fluvial and oceanographic processes within Bellingham Bay, Washington, USA, an urban estuary. Time-series data of currents, waves, and turbidity at four moorings along with geochemical data from grab samples and cores of seabed sediment from across the bay are contrasted with the dynamics of the Nooksack River, its fluvial sediment source. Even during large (5-yr return interval) river-flood events, water-column suspended-sediment concentration (SSC) near the bed on the outer delta topset was not correlated with Nooksack River runoff and was instead closely correlated with local wind-wave height. In contrast, near-surface SSC was strongly correlated with fluvial discharge, suggesting intense water-column suspended-sediment stratification during flood events. Grain-size and geochemical (7\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{7}$$\\end{document}Be and excess 210\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{210}$$\\end{document}Pb) results from seabed-sediment samples and historical bathymetric measurements of the subaqueous Nooksack River delta reveal spatial gradients of fluvial and wave influence and sediment-accumulation rates. Analysis of historical bathymetric surveys of the Nooksack River delta combined with the geochemical data reveal that about 75% of the fluvial sediment load can be accounted for in the Bellingham Bay receiving system. Studies of this type in urbanized coastal settings can help address ecological and geological questions regarding the risks from contaminants associated with fine-grained sediment, predict longer-term delta morphological evolution, and inform managers planning future coastal restoration efforts

Anomaly Detection of Mountain Photovoltaic Power Plant Based on Spectral Clustering

Mountainous photovoltaic (PV) power plants cover a large area and are distributed dispersedly. The construction surface is complex and the slope is large. It is difficult to find and locate faults when dealing with defects. Effective anomaly detection and fault location technology can not only improve the reliability and stability of the power plant but also reduce the operation and maintenance costs. Because of the terrain and the influence of construction, the radiation received by each group string at the same time point may be different, which brings great difficulties to anomaly detection. In this article, a method based on spectral clustering is proposed for anomaly detection of mountain PV power plants. The data is preprocessed by filtering and reconstruction. By sorting the time series current data, the influence of dip angle and azimuth is eliminated to a certain extent. Further, the normal and abnormal strings can be distinguished by spectral clustering. Using current time series data, the proposed method can detect anomalies effectively in different weather without additional irradiance and temperature data. Moreover, since the high-dimensional data can be processed by spectral clustering, the process of feature extraction can be greatly simplified. The proposed algorithm is validated by the actual data of PV power plants, which nicely verifies that the anomaly diagnosis method can effectively identify anomaly clusters.

Research and technology innovation, food security and economic growth in Nigeria: Implications for agripreneurs and policymakers

Population growth and more recently, the Covid-19 pandemic, have increased the number of severely food insecure people in sub-Saharan Africa. Food security is one of the most prominent tasks facing the global environment especially in Asia and Africa. With the increase in the population, drought, floods, disease outbreaks, poor farm network systems, water poverty, weak political and economic institutions, selfish leadership and economic challenges in Africa, the food security crisis appears to be an important issue that will require strategic and urgent attention. There is no doubt that traditional ways of farming are not sustainable in Africa because the land available for agriculture reduces as the population increases over time. Investment in new technologies is one of the strategies to improve farm yields and livestock outputs, and reduces the risk and negative effects of weather. The role of agripreneurship, in this context, towards ensuring food security cannot be underestimated. This study is quantitative research which analyses the historical and current time series data in order to predict the future event using inferential statistics. This study investigates the relationship between food security (FST), agricultural research and technology innovation (RTI) and economic growth (GDP) in Nigeria between 1980 and 2018. The study utilizes cointegration and causality tests to determine the long run relationship among the variables and their causal directions so as to know which variable caused the others. Understanding this relationship among the variables and causal direction are vital to making appropriate suggestions for policy makers. The results of the Johansen cointegration test reveals an existence of a long run relationship between the three series (FST, RTI, GDP). The results of vector error correction model indicate a short run causality from GDP to FST and RTI, respectively. Furthermore, the results of long run causality show two-way causality between FST and RTI, one-way causality from GDP to FST and RTI without feedback. This implies that when the economic activities improve in Nigeria in the short run, this engenders agricultural technology innovation deployment in the country which facilitates food security in the long run, while food security also facilitates agricultural technology innovation. The two-way causality between RTI and FST established in this study implies that increase in agricultural research investment would improve food security and vice versa. Thus, government and other private players such as agripreneurs in agricultural sector should intensify their spending in agricultural research on the one hand, and government should also provide appropriate incentives to motivate private stakeholders for agricultural R&D investment on the other hand. Key words: Research and technology innovation, agripreneurship, food security, economic growth, Nigeria

A novel anomaly detection method for multimodal WSN data flow via a dynamic graph neural network

Anomaly detection is a critical technique that ensures the reliability of WSNs. However, most existing anomaly detection methods only consider the case of single modal data flow anomaly detection for each node or multiple modal time series data flow anomaly detection for a single node and do not consider the case of multiple nodes and multiple time series data flow simultaneously,and it limited the ability of anomaly detection. In this paper, a novel anomaly detection model is proposed for multimodal WSN data flows. First, the temporal features and modal correlation features extracted from each sensor node are fused into one vector representation, then it is further aggregated with the spatial features represented the spatial position relationship of the nodes; finally,the current time-series data of WSN nodes are predicted, and abnormal states are identified according to the fusion features. The simulation results obtained on a public dataset show that the proposed approach can significantly improve upon existing methods interms of robustness, and its F1 score reaches 0.90, which is 14.2% higher than that of the graph convolution network (GCN) with longshort-term memory (LSTM).

A Polishing Robot Force Control System Based on Time Series Data in Industrial Internet of Things

Installing a six-dimensional force/torque sensor on an industrial arm for force feedback is a common robotic force control strategy. However, because of the high price of force/torque sensors and the closedness of an industrial robot control system, this method is not convenient for industrial mass production applications. Various types of data generated by industrial robots during the polishing process can be saved, transmitted, and applied, benefiting from the growth of the industrial internet of things (IIoT). Therefore, we propose a constant force control system that combines an industrial robot control system and industrial robot offline programming software for a polishing robot based on IIoT time series data. The system mainly consists of four parts, which can achieve constant force polishing of industrial robots in mass production. (1) Data collection module. Install a six-dimensional force/torque sensor at a manipulator and collect the robot data (current series data, etc.) and sensor data (force/torque series data). (2) Data analysis module. Establish a relationship model based on variant long short-term memory which we propose between current time series data of the polishing manipulator and data of the force sensor. (3) Data prediction module. A large number of sensorless polishing robots of the same type can utilize that model to predict force time series. (4) Trajectory optimization module. The polishing trajectories can be adjusted according to the prediction sequences. The experiments verified that the relational model we proposed has an accurate prediction, small error, and a manipulator taking advantage of this method has a better polishing effect.

Time series clustering and physical implication for photovoltaic array systems with unknown working conditions

How to distinguish exact working conditions is an open topic for a real world solar power station operated under unclear conditions and environmental variations. It is obvious that abnormal working conditions are rarely known beforehand. We hypothesize that currents and voltages of the solar power system could yield some statistical clusters that have certain physical implications. Without any prior knowledge on operation conditions, time series data of currents and voltages are divided into many subsequences to measure the similarities among subsequences through a dynamic time warping method, and then the similarities are classified through the k-means method. Meanwhile, the clusters yielded by the k-means method alone are used for comparison. Both approaches are evaluated by two external measures which suggest four clusters being the best number of clusters. To acquire real world data, a small test facility that is a scale-down version of a solar power station is built and operated under four working conditions such as normal working, partial shading, overall shading and open circuit fault. The experimental results show that four clusters of the unlabeled raw data are related to actual unknown working conditions where the accuracy of the coincidence rates is within 60–100%. It is also observed that the electrical faults are easily distinguished from other conditions with irradiation and temperature variations. The discovery to physical implication of time series clusters is important to develop advanced forecasting and fault diagnosis technologies in the field of photovoltaic power systems.

Semantic Diagnosis Approach for Buildings

The detection and diagnosis of abnormal building behavior is key to further improve the comfort and energy efficiency in buildings. An increasing number of sensors can be utilized for this task but these lead to higher integration effort and the need to capture the sensor interactions. This paper presents a novel diagnostic approach for buildings with complex heating, ventilation, air-conditioning (HVAC) systems. It uses semantic graphs to automatically create the diagnostic model from the building's data points and to identify potential cause-effect-relationships based on past and current time series data. The approach is validated on various simulated examples of a multiroom HVAC control system. The experimental results show that it can diagnose multiple faults with and without delays with high accuracy.

Volatility Spillover USD-IDR Exchange Rate With Indonesia Stock Price

Value of imports higher than exports, causing the trade deficit, the appreciation of the currency in the developed countries, and withdrawals by foreign investors on the Indonesian stock exchange market. These things cause the movement of the exchange rate USD-IDR and Indonesian Stock Price are volatile and tend to weaken in the period from January 3, 2011 - August 31, 2016. This study to determine spillover volatility of the exchange rate USD-IDR with Indonesian Stock Price. By using current time series data, analyzed by Augmented Dickey-Fuller (ADF), GARCH, and Granger Causality. The results of the data processing rate of USD-IDR and data Indonesian Stock Price stationary at first difference by changing the level of daily data into a return of the respective data. Data having problems heteroskedasticity so it can be analyzed using GARCH. Results of the analysis showed that there is a volatility spillover between the two data. Then granger causality test results show that the causality occurs in both directions, meaning that changes in the foreign exchange market to give effect to the capital markets, and vice versa.

동해 우산해곡 해수 유동 특성

One year long time-series current data were obtained at two stations (K1 and K2) located in the Usan Trough in the area north of Ulleungdo in the East Sea from September 2006. The observed data reveal enhanced seafloor flows in both stations with variabilities of about 20 days which is possibly governed by the topographic Rossby wave. After February 2007, strong flow in the upper layer in St. K1 appears throughout the mooring period and this is due to the passage of the warm eddy comparing with satellite sea surface temperature data. During this period, no significant correlation between the current in the upper layer and those in two deep layers is shown indicating the eddy does not affect flows in the deep ocean. It is also observed that the flow direction rotates clockwise with depth in both stations except for the upper of the K1. This implies that the deep flow does not parallel to the isobaths exactly and it has a downwelling velocity component. The possibility of the flow from the Japan Basin to the Ulleung Basin across the Usan Trough is not evidenced from the data.

Dynamic near-term traffic flow prediction: system-oriented approach based on past experiences

Short-term prediction is one of the essential elements of intelligent transportation systems (ITS). Although fine prediction methodologies have been reported, most prediction methods with current time-series data lead to inefficient predictions when current or future time-series data either exhibit fluctuations or abruptly change. In order to deal with this problem, a dynamic multi-interval traffic volume prediction model, based on the k-nearest neighbour non-parametric regression (KNN-NPR), is introduced in this study. In an empirical study with real-world data, the input parameters of the proposed model including the k-values for the nearest neighbours in the neighbourhood and the dm-values for the number of lags were optimised according to the multi-interval prediction horizon in order to immediately capture the directionality of the future states and to minimise the prediction errors. The presented model performed effectively in terms of prediction accuracy, despite multi-interval schemes, to the same degree as applications of the real ITS, even if the time-series data abruptly varied or exhibited wide fluctuations. It can clearly be seen that the proposed methodology is one of the promising system-oriented approaches in the area of multi-interval traffic flow forecasting.

A review on time series data mining

Time series is an important class of temporal data objects and it can be easily obtained from scientific and financial applications. A time series is a collection of observations made chronologically. The nature of time series data includes: large in data size, high dimensionality and necessary to update continuously. Moreover time series data, which is characterized by its numerical and continuous nature, is always considered as a whole instead of individual numerical field. The increasing use of time series data has initiated a great deal of research and development attempts in the field of data mining. The abundant research on time series data mining in the last decade could hamper the entry of interested researchers, due to its complexity. In this paper, a comprehensive revision on the existing time series data mining research is given. They are generally categorized into representation and indexing, similarity measure, segmentation, visualization and mining. Moreover state-of-the-art research issues are also highlighted. The primary objective of this paper is to serve as a glossary for interested researchers to have an overall picture on the current time series data mining development and identify their potential research direction to further investigation.

Current oscillations of indium(III) at a dropping mercury electrode

Abstract Oscillations in currents observed at potentials around the polarographic maximum during the reduction of indium(III) in potassium nitrate solutions have been characterized. Effects of surfactants, supporting electrolyte concentration, mercury drop rate, resistors in series, and temperature are reported. The current–time series data of the oscillations obtained by holding the potential fixed are used to generate a one-dimensional return map. The minima of adjacent peaks of the oscillations are used for the return map. Analysis of the return map reveals the noise, repeatability of the oscillations, and enhances differences observed in the oscillations between potential settings.

Joint Probability of Extreme Waves and Currents on Norwegian Shelf

Simultaneous wave and current measurements on the Norwegian Shelf were used to estimate the appropriate current to associate with extreme waves in platform design. The primary data consisted of seven years of measurements at Tromsoflaket (71°30′N, 19 °E, 230 m depth), including 38 storms with peak significant wave heights above 7 m. The maximum wave height and maximum current occurred simultaneously in only one of the 38 storms. The wave and current timeseries data were used to calculate the time series of drag load on a simple structure and statistics of wave height and drag load were developed. The data showed that the in‐line current that must be added to extreme waves in order to preserve the extreme loads approaches 25 cm/s asymptotically as storm severity increases. Sensitivity studies suggest that this limit is largely insensitive to reasonable variations in current profile, wave kinematics theory, structure geometry, tidal current removal, length of data base, or site location. These results show that the current specified by Norwegian regulatory authorities is conservative, and provide a basis for reducing the design hydrodynamic loads on drag‐dominated offshore structures on the Norwegian shelf.