Approach For Real-time Prediction Research Articles

A Real-Time Prediction Approach to Deep Soil Moisture Combining GNSS-R Data and a Water Movement Model in Unsaturated Soil

Deep soil moisture data have wide applications in fields such as engineering construction and agricultural production. Therefore, achieving the real-time monitoring of deep soil moisture is of significant importance. Current soil monitoring methods face challenges in conducting the large-scale, real-time monitoring of deep soil moisture. This paper innovatively proposes a real-time prediction approach to deep soil moisture combining GNSS-R data and a water movement model in unsaturated soil. This approach, built upon surface soil moisture data retrieved from GNSS-R signal inversion, integrates soil–water characteristics and soil moisture values at a depth of 1 m. By employing a deep soil moisture content prediction model, it provides predictions of soil moisture at depths from 0 to 1 m, thus realizing the large-scale, real-time dynamic monitoring of deep soil moisture. The proposed approach was validated in a study area in Goodwell, Texas County, Oklahoma, USA. Predicted values of soil moisture at a randomly selected location in the study area at depths of 0.1 m, 0.2 m, 0.5 m, and 1 m were compared with ground truth values for the period from 25 October to 19 November 2023. The results indicated that the relative error (δ) was controlled within the range of ±14%. The mean square error (MSE) ranged from 2.90 × 10−5 to 1.88 × 10−4, and the coefficient of determination (R2) ranged from 82.45% to 89.88%, indicating an overall high level of fitting between the predicted values and ground truth data. This validates the feasibility of the proposed approach, which has the potential to play a crucial role in agricultural production, geological disaster management, engineering construction, and heritage site preservation.

A novel artificial intelligent-based approach for real time prediction of telecom customer’s coming interaction

<p><span>Predicting customer’s behavior is one of the great challenges and obstacles for business nowadays. Companies take advantage of identifying these future behaviors to optimize business outcomes and create more powerful marketing strategies. This work presents a novel real-time framework that can predict the customer’s next interaction and the time of that interaction (when that interaction takes place). Furthermore, an extensive data exploratory analysis is performed to gain more insights from the data to identify the important features. Transactional data and static profile data are integrated to feed a deep learning model which is implemented using two methodologies: time-series approach and statistical approach. It is found that the time-series approach gives the best performance and fulfills all the requirements. The experiments show that the proposed framework introduces a good overall performance in comparison to existing approaches based on standard metrics like accuracy and mean absolute error (MAE) values. What makes the proposed work novel and special is that it is the first approach that addresses the telecom customer’s next future interaction not just churn prediction like the other approaches in literature.</span></p>

A new approach for real-time prediction of stick–slip vibrations enhancement using model agnostic and supervised machine learning: a case study of Norwegian continental shelf

Efficient and safe drilling operations require real-time identification and mitigation of downhole vibrations like stick-slip, which can significantly diminish performance, reliability, and efficiency. This pioneering research introduces a robust machine learning approach combining model-agnostic regression techniques with Bayesian Optimized Extra Trees (BO_ET) to accurately predict stick-slip events in real-time using downhole sensor data. The model is rigorously tested and validated on a substantial offshore dataset comprising over 78,000 data points from a Norwegian continental shelf (NCS) oil field. The key input features encompassing real-time downhole and surface drilling parameters are carefully selected, including critical variables such as collar rotational speed, shock risks, annular pressure, torque, mud flow rate, drill string vibration severity, and other relevant measurements. These parameters offer significant insights into the occurrence of harmful stick-slip vibrations. Among several sophisticated machine learning models, the Extra Trees (ET) algorithm demonstrates superior performance with the lowest errors of 5.5056 revolutions per minute (r/min) Mean Absolute Error (MAE) and 9.9672 r/min Root Mean Square Error (RMSE) on out-of-sample test data. Further hyperparameter tuning of the ET algorithm via Bayesian Optimization dramatically reduces errors down to 0.002156 MAE and 0.024495 RMSE, underscoring the significant innovation and advantages of the proposed approach. By seamlessly incorporating real-time downhole sensor data and drill string mechanics, the model enables reliable identification of stick-slip events as they occur downhole. This grants opportunities to optimize critical drilling parameters including revolutions per minute (RPM), weight-on-bit (WOB), mud flow rates, and more to effectively mitigate stick-slip severity and improve the rate of penetration (ROP). Integrating the approach into automatic driller systems on offshore rigs offers immense benefits for drilling operations through substantially increased efficiency, fewer premature failures, lower costs, and significantly improved productivity and safety. Overall, this research strongly emphasizes the immense transformative potential of advanced data analytics and machine learning in enabling more efficient, economical, and sustainable drilling practices. The proposed model demonstrates clear superiority over existing methods and establishes a robust and reliable platform for real-time stick-slip prediction and mitigation, maximizing drilling performance.Graphical abstract

An association rule mining approach to predict alarm events in industrial alarm floods

Industrial operators often experience overwhelming situations during ongoing alarm floods due to high alarm rates. In such situations, real-time assistance in the form of prediction of upcoming alarm events can ease off the decision-making for industrial operators. Accordingly, this work studies alarm prediction in alarm flood situations, and the main contribution lies in a novel association rule mining approach for real-time prediction of alarm events and their corresponding times of annunciation during an ongoing alarm flood. The proposed method is capable of performing predictions at the triggering instant and modifying the predictions with the increasing of the ongoing alarm flood. The proposed method is implemented mainly in the following steps: (1) A Compact Prediction Tree (CPT) model is modified with new features, namely, the time table and co-occurrence matrix, and constructed based on historical alarm sequences; (2) an alarm relevancy detection strategy is designed to detect and eliminate irrelevant alarms in alarm floods; (3) an online alarm prediction algorithm is designed to predict upcoming alarms at the early stage of the ongoing alarm flood; (4) the confidence intervals of the time differences between the annunciations of subsequent predicted alarm events are calculated for time prediction. To demonstrate the effectiveness of the proposed method, an industrial case study based on real alarm & event logs from an oil refinery is provided.

A data-driven approach for real-time prediction of thermal gradient in engineered structures

A data-driven approach for real-time prediction of thermal gradient in engineered structures

Air Pollution Prediction Using an Ensemble of Dynamic Transfer Models for Multivariate Time Series

Entering a new era of big data, analysis of large amounts of real-time data is important, and air quality data as streaming time series are measured by several different sensors. To this end, numerous methods for time-series forecasting and deep-learning approaches based on neural networks have been used. However, they usually rely on a certain model with a stationary condition, and there are few studies of real-time prediction of dynamic massive multivariate data. Use of a variety of independent variables included in the data is important to improve forecasting performance. In this paper, we proposed a real-time prediction approach based on an ensemble method for multivariate time-series data. The suggested method can select multivariate time-series variables and incorporate real-time updatable autoregressive models in terms of performance. We verified the proposed model using simulated data and applied it to predict air quality measured by five sensors and failures based on real-time performance log data in server systems. We found that the proposed method for air pollution prediction showed effective and stable performance for both short- and long-term prediction tasks. In addition, traditional methods for abnormality detection have focused on present status of objects as either normal or abnormal based on provided data, we protectively predict expected statuses of objects with provided real-time data and implement effective system management in cloud environments through the proposed method.

Online milk composition analysis with an on-farm near-infrared sensor

On-farm monitoring of milk composition can support close control of the udder and metabolic health of individual dairy cows. In previous studies, near-infrared (NIR) spectroscopy applied to milk analysis has proven useful for predicting the main components of raw milk (fat, protein, and lactose). In this contribution, we present and evaluate a precise tool for online milk composition analysis on the farm. For each milking, the online analyzer automatically collects and analyses a representative milk sample. The system acquires the NIR transmission spectra of the milk samples in the wavelength range from 960 to 1690 nm and performs a milk composition prediction afterward.Over a testing period of 8 weeks, the sensor collected 1165 NIR transmittance spectra of raw milk samples originating from 36 cows for which reference values were obtained for fat, protein, and lactose. For the same online sensor system, two calibration scenarios were evaluated: training post-hoc prediction models based on a representative set of calibration samples (n = 319) acquired over the entire testing period, with different cows in the calibration and test set, and training real-time prediction models exclusively on the samples acquired in the first week of the testing period (n = 308).The obtained prediction models were thoroughly tested on all the remaining samples not included in the calibration sets (n respectively 846 and 857). For the post-hoc prediction models, this resulted in an overall prediction error (root-mean-squared error of prediction, RMSEP) smaller than 0.080% (all % are in wt/wt) for milk fat (range 1.5–6.3%), protein (2.6–4.3%) and lactose (4–5.1%), with a coefficient of determination R2 of 0.989, 0.947 and 0.689 for fat, protein, and lactose respectively. For the real-time prediction models, the RMSEP was smaller than 0.092% for milk fat and lactose, and 0.110% for protein, with an R2 of 0.989 (fat), 0.894 (protein) and 0.644 (lactose). The milk lactose predictions could be further improved (RMSEP = 0.088%, R2 = 0.675) by taking into account a cow-specific bias. The presented online sensor system using the real-time prediction approach can thus be used for detailed and autonomous on-farm monitoring of milk composition after each individual milking, as its accuracy is well within the requirements by the International Committee for Animal Recording (ICAR) for on-farm milk analyzers and even meet the standards for laboratory analysis systems for fat and lactose. For this real-time prediction approach, a drift was observed in the predictions, especially for protein. Therefore, further research on the development of online calibration maintenance techniques is required to correct for this model drift and further improve the performance of this sensor system.

An Adaptive and Parameter-Free Recurrent Neural Structure for Wireless Channel Prediction

Predicting channel state information (CSI) is a fundamental element in wireless communications systems. An accurate CSI estimation and prediction is critical to the system performance. This paper introduces a recurrent neural network (RNN) based approach for real-time prediction in real-world non-stationary channels. It uses the recent history data for online training, followed by prediction employing the trained model, in order to adapt to the changing channel and obtain a more accurate CSI prediction compared to conventional methods. Furthermore, the proposed method needs no additional knowledge, such as the internal properties of the channel itself, or the external features that affect the channel propagation, greatly facilitating its use in practical systems. Simulation results show that the proposed adaptive and parameter free recurrent neural structure (APF-RNS) outperforms the existing methods under a dynamically changing non-stationary environment. Therefore, the proposed online training based RNN approach is a promising method for channel prediction in wireless communications.

A novel approach for real-time prediction and prevention of tool chipping in intermittent turning machining

An integrated tool condition monitoring system, based on a novel signal processing approach, for online prediction and prevention of tool chipping in intermittent turning is presented. It identifies the unstable crack propagation features of the prefailure phase, independent of the cutting parameters and workpiece material. A correlation between the chipping size and these features was developed for decision making, to protect machined surfaces. Experimental validation results confirmed the accuracy of the proposed system. The time required for signal processing, decision making and communication with the machine controller allows stopping the operation before part damage. No such system is presently available.

Recurrent Neural Network-Based Stencil Cleaning Cycle Predictive Modeling

This paper presents a real-time predictive approach to improve solder paste stencil printing cycle decision making process in surface mount assembly lines. Stencil cleaning is a critical process that influences the quality and efficiency of printing circuit board. Stencil cleaning operation depends on various process variables, such as printing speed, printing pressure, and aperture shape. The objective of this research is to help efficiently decide stencil printing cleaning cycle by applying data-driven predictive methods. To predict the printed circuit board quality level, a recurrent neural network (RNN) is applied to obtain the printing performance for the different cleaning aging. In the prediction model, not only the previous printing performance statuses are included, but also the printing settings are used to enhance the RNN learning. The model is tested using data collected from an actual solder paste stencil printing line. Based on the predicted printing performance level, the model can help automatically identify the possible cleaning cycle in practice. The results indicate that the proposed model architecture can predictively provide accurate solder paste printing process information to decision makers and increase the quality of the stencil printing process.

An Efficient Approach for Real-Time Prediction of Rate of Penetration in Offshore Drilling

Predicting the rate of penetration (ROP) is critical for drilling optimization because maximization of ROP can greatly reduce expensive drilling costs. In this work, the typical extreme learning machine (ELM) and an efficient learning model, upper-layer-solution-aware (USA), have been used in ROP prediction. Because formation type, rock mechanical properties, hydraulics, bit type and properties (weight on the bit and rotary speed), and mud properties are the most important parameters that affect ROP, they have been considered to be the input parameters to predict ROP. The prediction model has been constructed using industrial reservoir data sets that are collected from an oil reservoir at the Bohai Bay, China. The prediction accuracy of the model has been evaluated and compared with the commonly used conventional artificial neural network (ANN). The results indicate that ANN, ELM, and USA models are all competent for ROP prediction, while both of the ELM and USA models have the advantage of faster learning speed and better generalization performance. The simulation results have shown a promising prospect for ELM and USA in the field of ROP prediction in new oil and gas exploration in general, as they outperform the ANN model. Meanwhile, this work provides drilling engineers with more choices for ROP prediction according to their computation and accuracy demand.