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

Simulations of charged droplet collisions in shear flow

Acknowledgments This research has been enabled by the use of computing resources provided by WestGrid, the Shared Hierarchical Academic Research Computing Network (SHARCNET: www.sharcnet.ca), and Compute/Calcul Canada. O.S. thanks NSERC for an Alexander Graham Bell Canada Graduate Scholarship.Peer reviewedPostprin

Shear thickening and history-dependent rheology of monodisperse suspensions with finite inertia via an immersed boundary lattice Boltzmann method

This the pre-print of an article submitted to International Journal of Multiphase Flow,Volume 125, April 2020, 103205. The final published version is available at http://dx.doi.org/10.1016/j.ijmultiphaseflow.2019.103205Three-dimensional direct numerical simulations of dense suspensions of monodisperse spherical particles in simple shear flow have been performed at particle Reynolds numbers between 0.1 and 0.6. The particles translate and rotate under the influence of the applied shear. The lattice Boltzmann method was used to solve the flow of the interstitial Newtonian liquid, and an immersed boundary method was used to enforce the no-slip boundary condition at the surface of each particle. Short range spring forces were applied between colliding particles over sub-grid scale distances to prevent overlap. We computed the relative apparent viscosity for solids volume fractions up to 38% for several shear rates and particle concentrations and discuss the effects of these variables on particle rotation and cluster formations. The apparent viscosities increase with increasing particle Reynolds number (shear thickening) and solids fraction. As long as the particle Reynolds number is low (0.1), the computed viscosities are in good agreement with experimental measurements, as well as theoretical and empirical equations. For higher Reynolds numbers, we find much higher viscosities, which we relate to slower particle rotation and clustering. Simulations with a sudden change in shear rate also reveal a history (or hysteresis) effect due to the formation of clusters. We quantify the changes in particle rotation and clustering as a function of the Reynolds number and volume fraction

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