Modulation-function-based finite-horizon sensor fault detection for salient-pole PMSM using parity-space residuals

An online model-based fault detection and isolation method for salient-pole permanent magnet synchronous motors over a finite horizon is proposed. The proposed approach combines parity-space-based residual generation and modulation-function-based filtering. Given the polynomial model equations, the unknown variables (i.e. the states, unmeasured inputs) are eliminated resulting in analytic redundancy relations used for residual generation. Furthermore, in order to avoid needing the derivatives of measured signals required by such analytic redundancy relations, a modulation-function-based evaluation is proposed. This results in a finite-horizon filtered version of the original residual. The fault detection and isolation method is demonstrated using simulation of various fault scenarios for a speed controlled salient motor showing the effectiveness of the presented approach

Long-term dependency slow feature analysis for dynamic process monitoring

Industrial processes are large scale, highly complex systems. The complex flow of mass and energy, as well as the compensation effects of closed-loop control systems, cause significance cross-correlation and autocorrelation between process variables. To operate the process systems stably and efficiently, it is crucial to uncover the inherent characteristics of both variance structure and dynamic relationship. Long-term dependency slow feature analysis (LTSFA) is proposed in this paper to overcome the Markov assumption of the original slow feature analysis to understand the long-term dynamics of processes, based on which a monitoring procedure is designed. A simulation example and the Tennessee Eastman process benchmark are studied to show the performance of LTSFA. The proposed method can better extract the system dynamics and monitor the process variations using fewer slow features

Ponomar Project Slavonic Computing Initiative Proposal to Encode Combining Glagolitic Letters in Unicode

Glagolitic, also known as “Glagolitsa”, is an alphabetic writing system used to record Church Slavonic and other Slavic languages. Originating in the 9 th century, it is the earliest known Slavonic alphabet. The creation of the alphabet is attributed to the younger of the teachers of the Slavs, St. Cyril

Fault classification in dynamic processes using multiclass relevance vector machine and slow feature analysis

This paper proposes a modifed relevance vector machine with slow feature analysis fault classification for industrial processes. Traditional support vector machine classification does not work well when there are insufficient training samples. A relevance vector machine, which is a Bayesian learning-based probabilistic sparse model, is developed to determine the probabilistic prediction and sparse solutions for the fault category. This approach has the benefits of good generalization ability and robustness to small training samples. To maximize the dynamic separability between classes and reduce the computational complexity, slow feature analysis is used to extract the inner dynamic features and reduce the dimension. Experiments comparing the proposed method, relevance vector machine and support vector machine classification are performed using the Tennessee Eastman process. For all faults, relevance vector machine has a classification rate of 39%, while the proposed algorithm has an overall classification rate of 76.1%. This shows the efficiency and advantages of the proposed method

Multi-output soft sensor with a multivariate filter that predicts errors applied to an industrial reactive distillation process

The paper deals with the problem of developing a multi-output soft sensor for the industrial reactive distillation process of methyl tert-butyl ether production. Unlike the existing soft sensor approaches, this paper proposes using a soft sensor with filters to predict model errors, which are then taken into account as corrections in the final predictions of outputs. The decomposition of the problem of optimal estimation of time delays is proposed for each input of the soft sensor. Using the proposed approach to predict the concentrations of methyl sec-butyl ether, methanol, and the sum of dimers and trimers of isobutylene in the output product in a reactive distillation column was shown to improve the results by 32%, 67%, and 9.5%, respectively

Modeling for the performance of navigation, control and data post-processing of underwater gliders

Underwater gliders allow efficient monitoring in oceanography. In contrast to buoys, which log oceanographic data at individual depths at only one location, gliders can log data over a period of up to one year by following predetermined routes. In addition to the logged data from the available sensors, usually a conductivity-temperature-depth (CTD) sensor, the depth-average velocity can also be estimated using the horizontal glider velocity and the GPS update in a dead-reckoning algorithm. The horizontal velocity is also used for navigation or planning a long-term glider mission. This paper presents an investigation to determine the horizontal glider velocity as accurately as possible. For this, Slocum glider flight models used in practice will be presented and compared. A glider model for a steady-state gliding motion based on this analysis is described in detail. The approach for estimating the individual model parameters using nonlinear regression will be presented. In this context, a robust method to accurately detect the angle of attack is presented and the requirements of the logged vehicle data for statistically verified model parameters are discussed. The approaches are verified using logged data from glider missions in the Indian Ocean from 2016 to 2018. It is shown that a good match between the logged and the modeled data requires a time-varying model, where the model parameters change with respect to time. A reason for the changes is biofouling, where organisms settle and grow on the glider. The proposed method for deciphering an accurate horizontal glider velocity could serve to improve the dead-reckoning algorithm used by the glider for calculating depth-average velocity and for understanding its errors. The depth-average velocity is used to compare ocean current models from CMEMS and HYCOM with the glider logged data

Comparison of semirigorous and empirical models derived using data quality assessment methods

With the increase in available data and the stricter control requirements for mineral processes, the development of automated methods for data processing and model creation are becoming increasingly important. In this paper, the application of data quality assessment methods for the development of semirigorous and empirical models of a primary milling circuit in a platinum concentrator plant is investigated to determine their validity and how best to handle multivariate input data. The data set used consists of both routine operating data and planned step tests. Applying the data quality assessment method to this data set, it was seen that selecting the appropriate subset of variables for multivariate assessment was difficult. However, it was shown that it was possible to identify regions of sufficient value for modeling. Using the identified data, it was possible to fit empirical linear models and a semirigorous nonlinear model. As expected, models obtained from the routine operating data were, in general, worse than those obtained from the planned step tests. However, using the models obtained from routine operating data as the initial seed models for the automated advanced process control methods would be extremely helpful. Therefore, it can be concluded that the data quality assessment method was able to extract and identify regions sufficient and acceptable for modeling

An ADRC-based control strategy for FRT improvement of wind power generation with a doubly-fed induction generator

This paper proposes a second-order active disturbance rejection control (ADRC)-based control strategy with an integrated design of the flux damping method, for the fault ride-through (FRT) improvement in wind power generation systems with a doubly-fed induction generator (DFIG). First, a first principles model of the rotor and grid side converter of DFIG is developed, which is then used to theoretically analyze the system characteristics and show the damage caused to the DFIG system by a grid voltage fault. Then, the flux damping method is used to suppress the rotor current during a fault ride-through. In order to enhance the robustness and effectiveness of the flux damping method under complex working conditions, an ADRC approach is proposed for disturbance attenuation of the DFIG systems. Finally, a comparison of the proposed method with three other control approaches on a 1.5-MV DFIG system benchmark is performed. It is shown that the proposed method can adaptively and effectively improve the system performance during an FRT

Robust decoupling mixed sensitivity controller design of looper control system for hot strip mill process

This article considers a robust decoupling controller design for a multivariate control system with parameter uncertainties for the hot rolling mill process. The left and right coprime factorization theory is used to properly select the free and weighting matrices. The necessary and sufficient conditions for robust decoupling controller are also proposed. Then, by analyzing the changes in the dynamic response resulting from perturbations in the tension and angle system parameters in the hot strip rolling process, a modified multivariate model is developed. Furthermore, the selection method for a practical weighting function is studied, so that the robust and decoupling performance can be simultaneously realized for the controller implementation. Finally, the effectiveness of the proposed control approach is demonstrated using a case study from an industrial hot rolling mill

Optimal design of a photovaltaic station using Markov and energy price modelling

This paper addresses the optimization of photovoltaic (PV) systems to increase their efficiency. The study introduces a new pricing model that considers the current price of PV inverters. In addition, Markov modeling is used in a new optimization framework to determine the optimal configuration, considering the number of PV modules and inverters, operational constraints, and failure events of PV inverters up to 100 kW. A case study with six real PV inverters confirms the effectiveness of the proposed framework. It calculates the average daily hours of rated power generation considering geographic location, temperature, and solar irradiance using real data from a real PV system. The study identifies both local and global optimal solutions for PV inverters (15 kW to 100 kW), while minimizing the effective levelized cost of energy. The results of the study have important implications for future assessments of PV module failures and repairs