Process Identification Methods Research Articles

Leakage identification and correlation coefficient method for industrial workshop production process combining with computational fluid dynamics

Identifying leakage sources in industrial factory production is crucial to improving air quality, ensuring people’s health and safety and preventing safety accidents. In this study, a method for leakage source identification in industrial factories combining with computational fluid dynamics (CFD) and correlation coefficient was proposed and validated. The study first experimentally validated the numerical methods, which were fundamental to the leakage identification method. Then impacts of leakage sources, sensor errors and number of sensors on the source identification results were evaluated. The results showed that the identification accuracy could be significantly improved by refining the step size of the coefficient a r in this method. When the number of leakage sources was unknown, the accuracy of this method in identifying the number and location of leakages was 93.5%. The computation time spent on source identification depended on the maximum number of leakage sources. Using four sensors with errors were enough to identify the number of unknown leakage sources. The number of leakage sources did not exceed three at the same time. Overall, coupled CFD and correlation coefficients method could effectively identify the number, location and intensity of leakages.

Key process identification method for aircraft skin milling based on quality characteristics and process correlation analysis

As a key aircraft component, the machining quality of aircraft skin is difficult to control. This paper proposes a key process identification method for aircraft skin milling, which comprehensively considers the influence of key quality features and the correlation between features and processes. The Fuzzy Analytic Hierarchy Process and Entropy Weight Method are used to calculate the influence weights of quality features, and the key quality features are comprehensively identified by the improved Fuzzy Borda Method. The improved algorithm reduces the non-linearity of calculation and the weakening of evaluation, solving the problem of low discrimination in the recognition results of a single evaluation method; At the same time, combining factors such as process difficulty, the relationship between quality characteristics and processes was analyzed, and key processes were identified by calculating relational grade. The experimental results show that the key processes of skin milling are the previous procedures of key quality features and the process located in the middle of the skin. The recognition results are more accurate and scientific compared to the recognition methods that do not consider the relationship between features and processes. This has certain guiding significance for practical aircraft skin machining tools and has good generalizability.

A Robust Process Identification Method under Deterministic Disturbance

This study introduces a novel process identification method aimed at overcoming the challenge of accurately estimating process models when faced with deterministic disturbances, a common limitation in conventional identification methods. The proposed method tackles the difficult modeling problems due to deterministic disturbances by representing the disturbances as a linear combination of Laguerre polynomials and applies an integral transform with frequency weighting to estimate the process model in a numerically robust and stable manner. By utilizing a least squares approach for parameter estimation, it sidesteps the complexities inherent in iterative optimization processes, thereby ensuring heightened accuracy and robustness from a numerical analysis perspective. Comprehensive simulation results across various process types demonstrate the superior capability of the proposed method in accurately estimating the model parameters, even in the presence of significant deterministic disturbances. Moreover, it shows promising results in providing a reasonably accurate disturbance model despite structural disparities between the actual disturbance and the model. By improving the precision of process models under deterministic disturbances, the proposed method paves the way for developing refined and reliable control strategies, aligning with the evolving demands of modern industries and laying solid groundwork for future research aimed at broadening application across diverse industrial practices.

Characterisation and critical processes identification for production of herbal preparations using 1H-NMR and chemometrics: A case study of Trichosanthis Pericarpium injection.

Herbal preparations are extensively utilised for the treatment of diseases in Asian countries. However, the variations in origin, climate, and production processes can lead to inconsistencies in the quality of herbal preparations. Existing quality control methods only target a few components in the finished product but ignore the control in the pharmaceutical process. Therefore, this study intends to develop a comprehensive component analysis method for intermediates in the pharmaceutical process to reveal the change patterns of substances and deepen the process understanding. This study aims to develop a rapid and comprehensive process characterisation and critical process identification method for herbal preparations. Six batches of Trichosanthis Pericarpium injection (TPI) intermediates were collected from the production process. Proton nuclear magnetic resonance (1H-NMR) spectra were acquired for qualitative and quantitative analysis of the se intermediates. Subsequently, chemometrics were used to identify critical processes and potential chemical markers. A total of 39 components in intermediates were identified, and the transfer of 25 components during the production process was investigated. Column chromatography was determined as the critical process. Nine components were identified as chemical markers. The application of 1H-NMR facilitated a comprehensive reflection of the chemical composition information of process intermediates, enabling investigations into the transfer of multi-component substances and accurate identification of critical processes and chemical markers.

Research on identification of key processes in machining process based on PageRank algorithm

Aiming at the difficulty of quantitatively evaluating the critical processes in the manufacturing process of complex mechanical products, a critical process identification method based on the PageRank algorithm is proposed with the goal of identifying key processes in the machining process. Based on the complex network theory, the error transfer network model of the machining process is established in this paper. Adopting the actual machining process as the data set of the complex network, the weights of the machining feature nodes are calculated by the PageRank ranking algorithm, and the nodes are ranked according to the weight values to assess the influence and importance of the nodes in the network model. Finally, taking the connecting rod machining process of a medium-speed marine diesel engine as an example, the results show that the method can quickly and effectively identify the key processes in the machining process.

Pulse response method for the Wiener-type nonlinear process identification

The Wiener-type nonlinear process where a nonlinear static block follows a linear dynamic subsystem is widely used to describe the dynamics of chemical processes. The present study proposes a new Wiener-type process identification method based on a narrow pulse response to cope with the relatively long process excitation requirement in the previous methods. The proposed identification method is developed based on the property of the Wiener-type nonlinear process that outputs of a linear dynamic subsystem is the same at two points when process outputs are equivalent at those points. The proposed method requires a relatively short process excitation (a single pulse response) but almost exactly estimates the Wiener-type nonlinear process. The simulation and experimental studies show the outstanding performance of the proposed method.

A method for selecting processes for automation with AHP and TOPSIS

Organizations are more frequently turning towards robotic process automation (RPA) as a solution for employees to focus on higher complexity and more valuable tasks while delegating routine, monotonous and rule-based tasks to their digital colleagues. These software robots can handle various rule-based, digital, repetitive tasks. However, currently available process identification methods must be qualified to select suitable automation processes accurately. Wrong process selection and failed attempts are often the origin of process automation's bad reputation within organizations and often result in the avoidance of this technology. As a result, in this research, a method for selecting processes for automation combining two multi-criteria decision-making techniques, 'Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), will be proposed, demonstrated, and evaluated. This study follows the Design Science Research Methodology (DSRM) and applies the proposed method for selecting processes for automation to a real-life scenario. The result will be a method to support the proper selection of business processes for automation, increasing the success of implementing RPA tools in an organization.

New Method of Degradation Process Identification for Reliability-Centered Maintenance of Energy Equipment

Advancements in energy technologies created a new application for gas turbine generators, which are used to balance load. This usage also brought new challenges for maintenance because of harsh operating conditions that make turbines more susceptible to random failures. At the same time, reliability requirements for energy equipment are high. Reliability-centered maintenance based on forecasting the remaining useful life (RUL) of energy equipment, offers improvements to maintenance scheduling. It requires accurate forecasting methods to be effective. Defining stages in energy equipment operation allows for the improvement of quality of data used for training. At least two stages can be defined: normal operation and degradation process. A new method named Head move—Head move is proposed to robustly identify the degradation process by detecting its starting point. The method is based on two partially overlapping sliding windows moving from the start of operation to the end of life of the energy equipment and Kruskal-Wallis test to compare data within these windows. Using this data separation, a convolutional neural network-based forecasting model is applied for RUL prediction. The results demonstrate that the proposed degradation process identification (DPI) method doubles the accuracy when compared to the same forecasting model but without degradation process identification.

Smoke and Flame Identification Method for the Entire Process of Grassland Fire Based on YOLOv5m-D and Static and Dynamic Characteristics

Smoke and Flame Identification Method for the Entire Process of Grassland Fire Based on YOLOv5m-D and Static and Dynamic Characteristics

High-precision identification of polarization processes of distribution of relaxation times by polarization curve model for proton exchange membrane fuel cell

The distribution of relaxation times (DRT) is a promising impedance analysis method that is often used to decompose polarization processes with different time constants. However, uncertainty in interpreting polarization processes by DRT arises from two main sources: (1) overlap of the DRT peaks and (2) lack of peak identification methods. Thus, herein, a high-precision polarization process identification method based on polarization curve model involving electrochemical processes of proton exchange membrane fuel cell (PEMFC) was proposed by establishing the intrinsic relationship between polarization curve and electrochemical impedance spectroscopy (EIS) and comparing the quantitative relationship between the steady-state resistance obtained by the inverse slope of polarization curve model and each DRT peak. Then, an improved DRT method based on the characteristic frequency resolution optimization was suggested to avoid the overlap of the DRT peaks in case of the narrow interval time constants of the electrochemical system. Based on these two methods, each polarization resistance of PEMFC and DRT peak was quantitatively analyzed. First, it was determined that the total steady-state resistance has a resistance recovery compared to the low-frequency intercept resistance obtained by the improved DRT method, because of the ultra-low-frequency pseudo-inductive behavior arising mainly from dissolved water transport. Second, the peak with a characteristic frequency in the range of 7–54 Hz corresponds to two processes: mass transport and dissolved water transport. The peak with a characteristic frequency in the range of 3–20 Hz represents charge transfer. Interestingly, the two DRT peaks in the low-frequency region overlapped at 400 mA cm−2 because their characteristic frequencies changed in opposite directions with the current density. Moreover, the peak with a high characteristic frequency in the range of 250–300 Hz corresponds to proton transport of the catalyst layer ionomer. The methodology based on the model lays the foundation for identifying the distribution of the relaxation times for PEMFCs.

Identification of Regional Drought Processes in North China Using MCI Analysis

Comprehensive identification of drought events is of great significance for monitoring and evaluating drought processes. Based on the date of daily precipitation, temperature and drought-affected area of 403 meteorological stations in North China from 1960 to 2019, the Comprehensive Drought Process Intensity Index (CDPII) has been developed by using the Meteorological-drought Composite Index (MCI) and regional drought process identification method, as well as the EIDR theory method. The regional drought processes in the past 60 years in North China, including Beijing, Tianjin, Hebei, Shanxi and Middle Inner Mongolia, were analyzed and identified. The result shows that the distribution characteristic of droughts with different intensities is as follows: The number of days of all annual-average mild droughts, moderate droughts and severe droughts was highest in Tianjin and that of extreme droughts was highest in Shanxi. The number of days of mild droughts was highest in May and lowest in January. The number of days of moderate droughts was highest in June. The number of days with mild and moderate drought showed an overall increasing trend, while the number of days with severe drought and above showed an overall decreasing trend (through a 95% significance test). The number of drought days was the highest in the 1990s. The annual frequency of drought is between 66.7% and 86.7%; the drought frequency in Hebei is the highest at 86.7%, followed by Beijing at 80%. There were 75 regional drought processes in North China from 1960 to 2019, and the correlation coefficient between process intensity and the drought-affected area was 0.55, which passed the 99% significance test. The comprehensive intensity of drought process from 27 April to 1 September 1972 was the strongest. From 18 May to 31 October 1965, the drought lasted 167 days. The overall drought intensity had a slight weakening trend in the past 60 years. A total of 75 regional drought processes occurred in North China, and the process intensity showed a trend of wavy decline with a determination coefficient (R2) of 0.079 (95% significance test). Overall, the regional drought process identification method and strength assessment result tally with the drought disaster, which can better identify the regional drought process. Furthermore, including the last days, the average intensity, average scope comprehensive strength, there are many angles to monitor and evaluate the drought and drought process. These provide a reference for drought control and decision-making.

Unsupervised classification of simulated magnetospheric regions

Abstract. In magnetospheric missions, burst-mode data sampling should be triggered in the presence of processes of scientific or operational interest. We present an unsupervised classification method for magnetospheric regions that could constitute the first step of a multistep method for the automatic identification of magnetospheric processes of interest. Our method is based on self-organizing maps (SOMs), and we test it preliminarily on data points from global magnetospheric simulations obtained with the OpenGGCM-CTIM-RCM code. The dimensionality of the data is reduced with principal component analysis before classification. The classification relies exclusively on local plasma properties at the selected data points, without information on their neighborhood or on their temporal evolution. We classify the SOM nodes into an automatically selected number of classes, and we obtain clusters that map to well-defined magnetospheric regions. We validate our classification results by plotting the classified data in the simulated space and by comparing with k-means classification. For the sake of result interpretability, we examine the SOM feature maps (magnetospheric variables are called features in the context of classification), and we use them to unlock information on the clusters. We repeat the classification experiments using different sets of features, we quantitatively compare different classification results, and we obtain insights on which magnetospheric variables make more effective features for unsupervised classification.

Application of critical component elimination process and method in a nuclear power plant

In order to improve the operation performance of domestic nuclear power plant and promote the application of CCM elimination technology, this paper introduces the process of CCM elimination, expounds in detail the qualitative and quantitative analysis methods to determine the priority of CCM elimination, and takes the Stator Cooling Water System of a nuclear power plant as an application pilot, and shows in detail the specific application process of CCM elimination process and priority identification method. the results show that the process and method are reasonable and efficiency, can provide guidance for the popularization application of CCM elimination technology system and process.

Process Modeling, Identification Methods, and Control Schemes for Nonlinear Physical Systems – A Comprehensive Review

AbstractA state‐of‐the‐art review on various identification schemes proposed for the Hammerstein, Wiener, and Volterra systems is presented with respect to the special problems arising in the identification of unknown nonlinear systems. Past and recent developments in the field of nonlinear system identification, parameter estimation, and nonlinear control schemes along with the nonlinearity issues are also deeply investigated. A comprehensive analysis on various parameter estimation approaches and nonlinear control laws are made adding credit to the noteworthy contributions, constructive arguments, and remarkable breakthroughs of researchers in various research fields. The application of most popular nonlinear control strategies for many nonlinear systems in the existing literature are presented spotlighting their merits and major shortcomings. The challenges faced in the nonlinear control field and their emergences to future directions are projected.

Mill Load Identification Method for Ball milling Process Based on Grinding Signal

Mill Load Identification Method for Ball milling Process Based on Grinding Signal

A Novel Seepage Behavior Prediction and Lag Process Identification Method for Concrete Dams Using HGWO-XGBoost Model

Seepage monitoring is a vital task in the risk management of concrete dams. Considering the lag effect of input factors, this paper presents a novel seepage monitoring model for concrete dams and proposes an effective identification method of lag process. Firstly, extreme gradient boosting (XGBoost) were adopted to predict the dam seepage. Hybridizing grey wolf optimization (HGWO) which integrates differential evolution (DE) into grey wolf optimization (GWO) and five-fold cross validation were utilized to optimize the hyper-parameters of XGBoost. Secondly, under the same search range and four evaluation indicators, the models optimized respectively by HGWO and three other algorithms were compared to confirm the global optimization capability of HGWO. Six state-of-art methods were also introduced to verify the effectiveness and feasibility of the proposed model. Then, based on the computation method of factor importance in decision tree models, we evaluated the relative importance of each component in the proposed model. Finally, according to the factor importance, the lag process of upstream water level and rainfall was identified, meanwhile a new equivalent water level calculation method is proposed. Monitoring data from three piezometric tubes on a concrete dam were taken as the experimental object. The results show that the improved HGWO has stronger global optimization ability, and the HGWO-XGBoost model achieves satisfactory prediction for seepage in concrete dams. Compared with the traditional trial-and-error method, the lag process computation method proposed in this paper provides a better recognition effect, which is of great value to the seepage monitoring and control of concrete dams.

Online Assessment of Conservation Voltage Reduction Effects With Micro-perturbation

Online assessment of conservation voltage reduction (CVR) has great effects on the performance of CVR-based applications, especially for real-time CVR-based controls. The challenges of online CVR assessment mainly come from time-varying load composition and inevitable noise. Besides, the dynamic process resulting from motors further complicates this problem. This article proposes a micro-perturbation based load-to-voltage (LTV) process identification method to online assess the CVR effects. In particular, we construct a well-designed pseudo-random-binary-sequence voltage perturbation to excite the CVR process continually and fully. With the experimental data, the CVR factor and transfer function of LTV are estimated by the cross-correlation method, which can reflect both steady-state and dynamic CVR effects without being affected by noise. The proposed method is verified on a specific hardware-in-the-loop testbed under various cases. In addition, through the energy-saving and power smoothing applications, the impact of CVR assessment on performance of CVR-based applications is also analyzed, which further verify the validity and economic benefits of the proposed assessment method.

Nonparametric Identification Based on Multi-inherited Gaussian Process Regression for Batch Process

This study presents a multi-inherited Gaussian process regression (GPR)-based nonparametric identification method for batch process. In the GPR framework, the impulse response of each time point co...

Braking process identification of high-speed trains for automatic train stop control

Automatic train stop control (ATSC) is a key function of the automatic train operation (ATO) system. An accurate braking process model can help to improve the control strategy. In this paper, the braking process for stop control of high-speed trains is formulated as a single-point time delay model, based on the principle of practical braking processes. Furthermore, a Picard iteration based identification method is first applied to the time delay system, and a train braking process identification method is proposed. The method is straightforward, and the parameters can be identified based on the principle of ordinary differential equations. The effectiveness of the braking process model and the identification method is illustrated by real-life experimental data.

A method for systematic identification of chemical and biotechnological processes for decentral, modular processing

A method for systematic identification of chemical and biotechnological processes for decentral, modular processing