Process Identification Methods Research Articles

Adaptive Dual Wavelet Threshold Denoising Function Combined with Allan Variance for Tuning FOG-SINS Filter

Allan variance (AV) stochastic process identification method for inertial sensors has successfully combined the wavelet transform denoising scheme. However, the latter usually employs a traditional hard threshold or soft threshold that presents some mathematical problems. An adaptive dual threshold for discrete wavelet transform (DWT) denoising function overcomes the disadvantages of traditional approaches. Assume that two thresholds for noise and signal and special fuzzy evaluation function for the signal with range between the two thresholds assure continuity and overcome previous difficulties. On the basis of AV, an application for strap-down inertial navigation system (SINS) stochastic model extraction assures more efficient tuning of the augmented 21-state improved exact modeling Kalman filter (IEMKF) states. The experimental results show that the proposed algorithm is superior in denoising performance. Furthermore, the improved filter estimation of navigation solution is better than that of conventional Kalman filter (CKF).

Research on TE process fault diagnosis method based on DBN and dropout

AbstractIn recent years, deep learning has shown outstanding performance and potential in pattern recognition and feature extraction, which has attracted an increasing amount of attention from engineering researchers and academics. Fault diagnosis methods based on deep learning have also become the focus of a significant amount of research. In this paper, a nonlinear process fault diagnosis and identification method based on DBN‐dropout is proposed. The deep belief network (DBN) has significant advantages in dealing with nonlinear processes, and it can extract the abstract representation of nonlinear process data to build a deep network to achieve the real‐time monitoring of process operations. Dropout technology can reduce overfitting and improve the generalization ability of the model. Afterwards, the Tennessee Eastman (TE) process is employed to analyze the performance of the proposed approach.

Quality analysis in metal additive manufacturing with deep learning

As a promising modern technology, additive manufacturing (AM) has been receiving increasing research and industrial attention in the recent years. With its rapid development, the importance of quality monitoring in AM process has been recognized, which significantly affects the property of the manufactured parts. Since the conventional hand-crafted features for quality identification are generally costly, time-consuming and sensitive to noises, the intelligent data-driven automatic process monitoring methods are becoming more and more popular at present. This paper proposes a deep learning-based quality identification method for metal AM process. To alleviate the requirement for large amounts of high-quality labeled training data by most existing data-driven methods, an identification consistency-based approach is proposed to better explore the semi-supervised training data. The proposed method is able to achieve promising performance using limited supervised samples with low quality, such as noisy and blurred images. Experiments on a real-world metal AM dataset are implemented to validate the effectiveness of the proposed method, which offers a promising tool for real industrial applications.

Fault diagnosis and economic performance evaluation for a simulated base metal leaching operation

A dynamic process model based on a base metal refinery including critical control layers has been developed. The critical control layers include sensors, actuators, regulatory controllers, alarm systems, safety interlocks and supervisory control. With the help of expert knowledge, a fault (abnormal event) library was incorporated into the dynamic model.Fault diagnosis methods are used to detect and identify abnormal process conditions. With the use of the dynamic process model, fault detection and identification methods can be more rigorously and accurately evaluated for hydrometallurgical industry use.An economic performance function is developed from expert knowledge. Once the fault diagnosis is complete, an economic impact analysis based on the fault diagnosis results is completed. A possible economic case for fault diagnosis is concluded from the results.

Research on Key Risk Source Identification of Asset Management in Power Grid Enterprise

Asset management uses a process analysis and identification method for various risk sources to identify a series of flowcharts in the form of several sub-modules in accordance with the steps or stages of grid asset management, demonstrate events or states that cause the risk to be generated by identifying various potential risk factors or risk events at each stage. Combined with the goal-oriented identification method to analyze and diagnose the risk indicator, list the detailed risk list, accurately record and manage the common risks existing in the enterprise, and use the reverse push down method, when the asset-related risks are identified, at the same time, record it in a manner that is appropriate to the type of asset or asset system.

Research on Key Risk Index System of Asset Management in Power Grid Enterprise

In this paper, a process analysis and identification method is adopted to identify various risk sources in asset management. According to the steps or stages of grid asset management, a set of key process risk factors is formed in the form of several sub-modules. By identifying various potential risk factors or risk events at each stage, we can describe the events or stages caused by risk. Thus we can build a key risk indicator system for power grid enterprise asset management.

The development of probabilistic and statistical methods for identification of noisy transient processes of synchronous machines

Noisy transient processes (TPs) in the stator windings of synchronous machines (SMs) in experiments on sudden symmetrical short circuits (SCs), field dissipation, voltage recovery, shock excitation, and other issues are especially vulnerable when they are identified by the results of bench tests using the operating standards for testing of synchronous machines. The processing methods of such transient processes according to domestic and foreign standards are overloaded with labor-intensive graphic procedures and calculations using oscillogram data. These methods to date have not made it possible to achieve the desired accuracy of TP processing due to a considerable scatter of the results of their identification. The experiment concerning a sudden SC is the main test for all transient processes, as it is asymmetric and contains the largest number of current components in the SM stator windings. Consequently, this experiment is potentially promising for finding ways to provide a high accuracy and reliability of TP identification. The other above-listed TPs are symmetrical without an asymmetric component, and so their exact identification is provided in a way similar to in the sudden SC experiment. The developed probabilistic and statistical methods (PSMs) of TP identification in many respects solve existing problems. The article presents new possibilities for the development of these methods with effective use of variational series of a random sign with the detected nucleus of effective point samples. These capabilities increase the accuracy and reliability of identification results of these TPs and reduce the labor-intensiveness of studies of a random sign in the investigated range of a TP with a transition component when processing long-term TPs of powerful SMs.

New Coordination Software for Parameter Identification Applied to Thermal Models of an Actuator Strut

The practical worth of models of technical processes depends on their accuracy, that is, the difference between model outputs and real measurements. For minimizing these differences, process identification methods are used. In this article, coordination software for process identification is presented which has the unique feature that it allows the integration of models that have been created with external tools, for example, Matlab or Python scripts. There is no need to transform the models into another type of software format to use the common identification coordinator. The concept of the software is described and two examples for the coupling with external simulation software are given. Additionally, this article contains a detailed case study of the parameter identification of two models using that identification coordination software. This highlights the benefit of the new coordination software regarding similar work flow for different model types. The modeled physical subject is the thermal behavior of an actuator strut.

Femtosecond Charge-Injection Dynamics at Hybrid Perovskite Interfaces.

With a power conversion efficiency (PCE) exceeding 22 %, perovskite solar cells (PSCs) have thrilled photovoltaic research. However, the interface behavior is still not understood and is a hot topic of research: different processes occur over a hierarchy of timescales, from femtoseconds to seconds, which makes perovskite interface physics intriguing. Herein, through femtosecond transient absorption spectroscopy with spectral coverage extending into the crucial IR region, the ultrafast interface-specific processes at standard and newly molecularly engineered perovskite interfaces in state-of-the-art PSCs are interrogated. Ultrafast interfacial charge injection occurs with a time constant of 100 fs, resulting in hot transfer from energetic charges and setting the timescale for the first step involved in the complex charge-transfer process. This is also true for 20 % efficient devices measured under real operation, for which the femtosecond injection is followed by a slower picosecond component. These findings provide compelling evidence for the femtosecond interfacial charge-injection step and demonstrate a robust method for the straightforward identification of interfacial non-equilibrium processes on the ultrafast timescale.

Un Nuevo Método de Identificación Basado en la Respuesta Escalón en Lazo Abierto de Sistemas Sobre-amortiguados

In this paper a new process identification method based in open loop step response of overdamped systems is presented. Initially the transient behavior of transfer functions with multiple real poles is analyzed, establishing analytical formulas to estimate the time it takes for these systems to reach certain points of his reaction curve to stimuli of the step type, including settling time for error criteria of 2% and 5%. Subsequently developed a method of identification using 3 points characteristic of the reaction curve (tk, yk), allowing estimate the order process N, the time constant T of multiple poles and delay time L of the approximate model. To evaluate the performance of the proposed method, 3 minimum phase and 1 non mimium phase transfer functions are used to compare the proposed method with other of identification methods found in the literature, as well as also, one model of a non-isothermal chemical reactor; establishing as efficiency index the root mean square error (RMSE) of the transient response and the frequency resposne of the identified models with respect to the original transfer functions with different signal to noise ratio (SNR) values. From the results obtained show that the proposed method achieves excellent approximation of the transient responses and frequency compared to other methods. From the results, it was observed that the proposed method obtains good approximations of the transient and frequency responses, being a simple method and requires a low computational load compared with others methods.

PSO based Energy Efficient IR Heating System

Objectives: Maintaining uniform temperature with low power consumption is an important factor in various household and industrial applications. This paper presents the development of energy efficient IR heating system using particle swarm optimization. Methods/Statistical Analysis: The IR heating system model is developed by process identification method using step input, controllers are designed, simulated using MATLAB/Simulink and implemented using Arduino. Findings: Experimentally, the PSO based PID controller is proven to be a method for saving of power for temperature control of IR heating system and also shows superiority over the conventional PID controller in terms of performance indices. PSO based energy efficient IR heating system has overcome the drawbacks associated with temperature control of IR heating system like repeated two position switching and inferior time domain specifications which leads to more power consumption. Application/Improvements: PSO based energy efficient IR heating system can be used in various commercial and industrial applications to maintain the temperature with low power consumption.

Iterative method for frequency domain identification of continuous processes with delay time

AbstractDelay time that affects the performances of many control synthesis techniques in controlled systems is common in most chemical industries. Estimating the delay time is also a difficult problem in identification fields. In this paper, aiming at continuous processes with delay time, an iterative least square identification method is proposed in the frequency domain. By introducing a truncated first‐order Taylor expansion, the delay time term is linearized. Linear regression equation for least squares (LS) is directly derived from the transfer function whose nonlinear term is replaced by a linear one. For reducing the linear approximation error and getting more accurate estimations of the model parameters, an iterative algorithm is developed based on the LS. Moreover, the proposed method can be easily extended to a closed‐loop system without increasing the order of the identified model. Simulations verify the effectiveness, fast convergence rates, and robustness of the proposed identification algorithm.

Use of numerical methods for identification of hydrodynamic field and hydrogeochemical processes in the Quaternary multi-aquifer system

Abstract The paper presents spatial analysis and numerical methods to describe the hydrodynamic and hydrochemical conditions in a groundwater system. The research was conducted in the northern part of the Białystok High Plane, eastern Poland, within a Quaternary multi-aquifer system. Spatial analysis was used for imaging the quasi-continuous structure of the system based on a discrete set of hydrogeological data. A high-resolution regional flow model was used to identify the groundwater discharge and discharge zones of the individual aquifers. Calculations have shown a marked asymmetry of the flow system. Deeply incised river valleys in the Niemen River basin more strongly affect the groundwater than the discharge zones in the valleys of the Vistula River basin. As a result, the underground watershed in deeper aquifers is clearly shifted westward in relation to the morphological watershed. The hydrodynamic conditions of the system determined by numerical methods were used to identify the points along the groundwater flow-path for the hydrochemical study. It was the basis for the identification of points located along the groundwater flow direction, which were used in the hydrochemical study. Computational schemes of water solution models were calculated for the quasi-equilibrium state of chemical reactions between the solution and the solid and gaseous phases. Presentation of the chemical reactions allowed determining the origin of changes in the concentrations of individual components dissolved in groundwater. It was found that kaolinitization, i.e. chemical weathering of feldspars and plagioclases is the basic process that most affects the groundwater chemistry.

A new multidimensional design space identification method for a quality-oriented drug development process

Food and Drug Administration and the European Medicines Agency have tightened regulations pertaining to drug manufacturing and research based on the concept of Quality by Design (QbD) in order to improve drug quality and safety. Although many QbD methods have been reported in the pharmaceutical research community, there is room for improvement associated with design space identification. The primary motivation of this paper is to propose an alternative identification method for generating a safe, multidimensional design space for quality-oriented drug development. First of all, the functional relationships between input factors and their associated output responses are exploited and estimated by using response surface methodology (RSM). Second, the normal acceptable ranges can also be obtained while customising statistical confidence and prediction intervals for RSM. Following this, a safe multidimensional design space is generated on an overlay contour plot including the importance of input factors. Finally, a case study for a drug development process is performed for verification purposes. From the proposed method, significant improvement in drug quality through factors such as variability reduction, statistical reliability, and the reproducibility of quality characteristics can be achieved.

Online process operating performance assessment and nonoptimal cause identification for industrial processes

Although industrial processes are usually operated at the optimal point in the early stage of the production, the operating performance may deteriorate with time due to process disturbances. In order to pursue optimal comprehensive economic benefit (CEB), online process operating performance assessment on optimality has become a key issue. However, a little work has been published in this research area. In this paper, a new online operating performance assessment and nonoptimal cause identification method for industrial process are proposed. The contributions of this paper can be summarized as follows: a novel performance-similarity-based online operating performance assessment method is proposed; total projection to latent structures (T-PLS) is applied to the area of process performance assessment for the first time; the online assessment results include not only the deterministic performance grades, but also the performance grade conversions which were not covered in the existing assessment method; when the assessment result is nonoptimal, a novel automatic nonoptimal cause identification strategy is developed based on variable contributions, which is meaningful for guiding the further production adjustment. Finally, the feasibility and efficiency of the proposed method are illustrated with a case of gold hydrometallurgical process.

Discrete-time frequency response identification method for processes with final cyclic-steady-state

A new non-parametric process identification method is proposed to obtain the discrete-time frequency response model from the process input and output data. The existing discrete-time Fourier transform approach can be applied to only the case that the initial part and the final part of the process data are zero-steady-state to estimate perfect frequency response data without modeling errors. The proposed method using a new transform can estimate the exact frequency response model from more various process excitation cases including initial-steady-state/final-steady-state and initial-steady-state/final-cyclic-steady-state. It can estimate exact frequency response model because no approximations are used in developing the proposed algorithm. Also, the proposed method can still provide exact model even in the case of static disturbances and sinusoidal disturbances of which the frequencies are multiples of the cyclic-steady-state frequency.

An novel identification method of the environmental risk sources for surface water pollution accidents in chemical industrial parks

The chemical industry is a major source of various pollution accidents. Improving the management level of risk sources for pollution accidents has become an urgent demand for most industrialized countries. In pollution accidents, the released chemicals harm the receptors to some extent depending on their sensitivity or susceptibility. Therefore, identifying the potential risk sources from such a large number of chemical enterprises has become pressingly urgent. Based on the simulation of the whole accident process, a novel and expandable identification method for risk sources causing water pollution accidents is presented. The newly developed approach, by analyzing and stimulating the whole process of a pollution accident between sources and receptors, can be applied to identify risk sources, especially on the nationwide scale. Three major types of losses, such as social, economic and ecological losses, were normalized, analyzed and used for overall consequence modeling. A specific case study area, located in a chemical industry park (CIP) along the Yangtze River in Jiangsu Province, China, was selected to test the potential of the identification method. The results showed that there were four risk sources for pollution accidents in this CIP. Aniline leakage in the HS Chemical Plant would lead to the most serious impact on the surrounding water environment. This potential accident would severely damage the ecosystem up to 3.8 km downstream of Yangtze River, and lead to pollution over a distance stretching to 73.7 km downstream. The proposed method is easily extended to the nationwide identification of potential risk sources.

Identification and Modeling of Process Damping in Milling

In this study, a practical identification method for process damping is presented for milling, and the information obtained from identification is used for modeling purposes. In the proposed approach, the process-damping coefficients in x and y directions are identified directly from the experimental stability limits. Then, they are used in identification of the indentation constant through energy balance formulation. The identified indentation constant is further used in modeling of process damping and estimation of stability limit for different cutting conditions and tool geometries. Milling tools with two different types of flank geometries, namely, planar and cylindrical, are considered in this study. The predictions are verified by time-domain simulations and experimental results. It is shown that the presented method can be used for identification and modeling of process damping in milling to determine chatter-free cutting depths at relatively low cutting speeds.

Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform

Chatter detection is an important task to improve productivity and part quality in the machining process. Since measured signals from sensors are usually contaminated by background noise and other disturbances, it is necessary to find efficient signal processing algorithms to identify the chatter as soon as possible. This paper presents an effective chatter identification method for the end milling process based on the study of two advanced signal processing techniques, i.e., wavelet package transform (WPT) and Hilbert–Huang transform (HHT). The WPT works as a preprocessor to denoise the measured signals and hence the performance of the HHT is enhanced. The proposed method consists of four steps. First, the measured signals are decomposed by the WPT, so that the chatter signals are allocated in a certain frequency band. Secondly, wavelet packets with rich chatter information are selected and are used to reconstruct new signals. Thirdly, the reconstructed signals are analyzed with HHT to obtain a Hilbert–Huang spectrum, which is a full time–frequency–energy distribution of the signals. Finally, the mean value and standard deviation of the Hilbert–Huang spectrum are calculated to detect the chatter and identify its levels as well. The proposed method is applied to the end milling process and the experimental results prove that the method can identify the chatter effectively.

The Study of Low Frequency Oscillations Parameters Identification in Power System Based on Improved HHT Method

The problem of low frequency oscillations (LFOs) parameters identification in power system with the method of improved Hilbert-Huang transform (HHT) is addressed. LFOs probably cause the great potential threat to the stable operation of power system, so identifying the parameters of LFOs is very important to develop the control strategies of restraining oscillations. In this paper, the LFOs signal is first processed by empirical mode decomposition (EMD) which is improved by the approach of endpoint extension used AR data prediction, stop shifting criterion and end condition via energy difference factor. The improved EMD is able to avoid the energy leakage and damping loss and also make the intrinsic mode functions (IMFs) closer to the true oscillations. Then the approach of parameters identification using least squares (LS) fitting algorithm based on the results of IMFs’ Hilbert transform is presented. Finally, case studies demonstrate the effectiveness of improved HHT process and LS parameters identification methods used in LFOs analysis of power system.