Model-based Monitoring Approach Research Articles

An Internet-of-Things (IoT) based cyber manufacturing framework for the assembly of microdevices

ABSTRACTThe emergence of Cyber-Physical Systems (CPS) based principles and technologies holds the potential to facilitate global collaboration in various fields of engineering. Micro Devices Assembly (MDA) is an emerging domain involving the assembly of micron-sized objects and devices. In this paper, a novel IoT based Cyber-Physical framework for MDA is discussed. The proposed IoT framework is the first of its kind for the process domain involving the assembly of micron-sized parts. Another innovation is the exploration of the feasibility next generation Software Defined Networking (SDN) principles to support distributed collaborations involving cyber and physical components within this framework. A unique information model-based monitoring approach is proposed to monitor and track the cyber-physical interactions. The advanced collaborative Cyber-Physical framework comprising of cyber and physical components linked using Next Generation Internet technologies has been developed to accomplish a targeted set of MDA life cycle activities which include assembly planning, path planning, Virtual Reality (VR) based assembly analysis, command generation and physical assembly. Genetic algorithm and modified Insertion algorithm-based methods have been proposed to support assembly planning activities. Advanced VR-based environments have been designed to support assembly analysis where plans can be proposed, compared and validated. The feasibility of the Cyber-Physical approach has been demonstrated by implementing an IoT Test Bed to assemble micro designs.

Model-based monitoring of industrial reversed phase chromatography to predict insulin variants.

Downstream processing in the manufacturing biopharmaceutical industry is a multistep process separating the desired product from process- and product-related impurities. However, removing product-related impurities, such as product variants, without compromising the product yield or prolonging the process time due to extensive quality control analytics, remains a major challenge. Here, we show how mechanistic model-based monitoring, based on analytical quality control data, can predict product variants by modeling their chromatographic separation during product polishing with reversed phase chromatography. The system was described by a kinetic dispersive model with a modified Langmuir isotherm. Solely quality control analytical data on product and product variant concentrations were used to calibrate the model. This model-based monitoring approach was developed for an insulin purification process. Industrial materials were used in the separation of insulin and two insulin variants, one eluting at the product peak front and one eluting at the product peak tail. The model, fitted to analytical data, used one component to simulate each protein, or two components when a peak displayed a shoulder. This monitoring approach allowed the prediction of the elution patterns of insulin and both insulin variants. The results indicate the potential of using model-based monitoring in downstream polishing at industrial scale to take pooling decisions.

Long-term carbon monoxide emission behavior of heavy-duty gas turbines: An approach for model-based monitoring and diagnostics

Emission measurements are a valuable source of information regarding the condition of gas turbine combustors. Aging of the hot gas path components can lead to an emission increase, which may ultimately require a readjustment of operational settings and accordingly impacts plant availability and maintenance. While NOx emissions may become crucial in high flame temperatures at full load, carbon monoxide emissions typically restrict low-load operation, which electricity markets demand more frequently due to the increasing penetration of intermittent renewable power. This paper presents a semiempirical carbon monoxide model that allows for quantifying the evolution of carbon monoxide emissions for GT24/GT26 heavy-duty gas turbines in commercial long-term operation. Input parameters to the derived carbon monoxide model are either directly measured or reconstructed by virtual measurements based on a simplified engine model. The method is developed with commissioning and operation data of three different gas turbines of GE’s GT24/GT26 fleet and validated over a total of 8.5 years of observation. Aging is accounted for by incorporating control sensor deviation and the formation of cold spots in the combustor into the semiempirical model. When these effects are taken into account, the carbon monoxide prediction is improved by up to 60% in terms of root mean square error of the log10(carbon monoxide) values compared to a benchmark case without consideration of aging.

Long-Term NOx Emission Behavior of Heavy Duty Gas Turbines: An Approach for Model-Based Monitoring and Diagnostics

Accurate monitoring of gas turbine performance is a means to an early detection of performance deviation from the design point and thus to an optimized operational control. In this process, the diagnosis of the combustion process is of high importance due to strict legal pollution limits as aging of the combustor during operation may lead to an observed progression of NOx emissions. The method presented here features a semi-empirical NOx formulation incorporating aging for the GT24/GT26 heavy duty gas turbines: Input parameters to the NOx-correlation are processed from actual measurement data in a simplified gas turbine model. Component deterioration is accounted for by linking changes in air flow distribution and control parameters to specific operational measurements of the gas turbine. The method was validated on three different gas turbines of the GE GT24/GT26 fleet for part- and baseload operation with a total of 374,058 long-term data points (5 min average), corresponding to a total of 8.5 years of observation, while only commissioning data were used for the formulation of the NOx correlation. When input parameters to the correlation are adapted for aging, the NOx prediction outperforms the benchmark prediction method without aging by 35.9, 53.7, and 26.2% in terms of root mean square error (RMSE) yielding a root-mean-squared error of 1.27, 1.84, and 3.01 ppm for the investigated gas turbines over a three-year monitoring period.

Monitoring energy efficiency of condensing boilers via hybrid first-principle modelling and estimation

The operating principle of condensing boilers is based on exploiting heat from flue gases to pre-heat cold water at the inlet of the boiler: by condensing into liquid form, flue gases recover their latent heat of vaporization, leading to 10–12% increased efficiency with respect to traditional boilers. However, monitoring the energy efficiency of condensing boilers is complex due to their nonlinear dynamics: currently, (static) nonlinear efficiency curves of condensing boilers are calculated at quasi-stationary regime and ‘a posteriori’, i.e. from data collected during chamber tests: therefore, with this static approach, it is possible to monitor the energy efficiency only at steady-state regime. In this work we propose a novel model-based monitoring approach for condensing boilers that extends the operating regime for which monitoring is possible: the approach is based on a hybrid dynamic model of the condensing boiler, where state-dependent switching accounts for dynamically changing condensing/non condensing proportions. Monitoring the energy efficiency over the boiler's complete dynamic regime is possible via switching estimators designed for the different condensing/non condensing modes. By using real-world boiler efficiency data we show that the proposed approach results in a (dynamic) nonlinear efficiency curve which gives a more complete description of the condensing boilers operation than static nonlinear efficiency curves: in addition, the dynamic curve can be derived ‘a priori’, i.e. from first principles, or from data collected during normal boiler operation (without requiring special chamber tests).

A New Approach for Model-Based Monitoring of Turbine Heat Rate

In this paper, a new approach for model-based monitoring of turbine heat rate is developed, where the superheat steam flow is calculated according to the output power of the turbine generation instead of the flow of feed water. A regenerative system model is built based on the operating state and historical data to predict the parameter values in the heat rate calculation. The results of the model calculation also verify the turbine operating parameters that are measured on site. The new approach in this paper was applied in a 660 MW generation unit. The monitoring results of this approach are more stable and accurate than traditional monitoring results.

A model-based approach for monitoring of shape deviations in peripheral milling

This paper presents a model-based approach for monitoring of shape deviations for milling operations. In order to detect occurring shape deviations of the machined workpiece during the milling process, different kinds of process models are presented and discussed for their application on manufacturing quality monitoring. Thereby, a model-based system was presented for the monitoring of shape deviations based on measured cutting forces. For the transformation of cutting forces into shape deviations, a tool deflection model and material removal model were designed and applied to a monitoring system. The presented model-based monitoring approach delivers accurate quality information, like geometric shape deviations, which can be monitored against geometric tolerances, providing a quality monitoring of manufacturing processes. The reconstruction of shape deviations from measured cutting forces is verified experimentally by comparing measured and reconstructed shape contours.

On-board state-of-health monitoring of lithium-ion batteries using linear parameter-varying models

This article presents a model-based monitoring approach for the internal resistance dependent state-of-health (SOH) of a high power lithium-ion battery cell used in hybrid electric vehicles. The monitoring method requires only signals which are already available in vehicles by measurement during regular driving. As varying battery temperatures occur during vehicle operation, a monitoring method has to cover the usual operation range from 0 °C to 50 °C. For this, a battery cell model from the class of linear parameter-varying (LPV) models is identified from measurement data. In contrast to many publications, the proposed monitoring method is validated using cell measurement data from driving cycles.