Model-based Monitoring Research Articles

An Overview of Software Sensor Applications in Biosystem Monitoring and Control

This review highlights the critical role of software sensors in advancing biosystem monitoring and control by addressing the unique challenges biological systems pose. Biosystems—from cellular interactions to ecological dynamics—are characterized by intrinsic nonlinearity, temporal variability, and uncertainty, posing significant challenges for traditional monitoring approaches. A critical challenge highlighted is that what is typically measurable may not align with what needs to be monitored. Software sensors offer a transformative approach by integrating hardware sensor data with advanced computational models, enabling the indirect estimation of hard-to-measure variables, such as stress indicators, health metrics in animals and humans, and key soil properties. This article outlines advancements in sensor technologies and their integration into model-based monitoring and control systems, leveraging the capabilities of Internet of Things (IoT) devices, wearables, remote sensing, and smart sensors. It provides an overview of common methodologies for designing software sensors, focusing on the modelling process. The discussion contrasts hypothetico-deductive (mechanistic) models with inductive (data-driven) models, illustrating the trade-offs between model accuracy and interpretability. Specific case studies are presented, showcasing software sensor applications such as the use of a Kalman filter in greenhouse control, the remote detection of soil organic matter, and sound recognition algorithms for the early detection of respiratory infections in animals. Key challenges in designing software sensors, including the complexity of biological systems, inherent temporal and individual variabilities, and the trade-offs between model simplicity and predictive performance, are also discussed. This review emphasizes the potential of software sensors to enhance decision-making and promote sustainability in agriculture, healthcare, and environmental monitoring.

Thermal Monitoring of an Internal Combustion Engine for Lightweight Fixed-Wing UAV Integrating PSO-Based Modelling with Condition-Based Extended Kalman Filter

The internal combustion engines of long-endurance UAVs are optimized for cruises, so they are prone to overheating during climbs, when power requests increase. To counteract the phenomenon, step-climb maneuvering is typically operated, but the intermittent high-power requests generate repeated heating–cooling cycles, which, over multiple missions, may promote thermal fatigue, performance degradation, and failure. This paper deals with the development of a model-based monitoring of the cylinder head temperature of the two-stroke engine employed in a lightweight fixed-wing long-endurance UAV, which combines a 0D thermal model derived from physical first principles with an extended Kalman filter capable to estimate the head temperature under degraded conditions. The parameters of the dynamic model, referred to as nominal condition, are defined through a particle-swarm optimization, minimizing the mean square temperature error between simulated and experimental flight data (obtaining mean and peak errors lower than 3% and 10%, respectively). The validated model is used in a so-called condition-based extended Kalman filter, which differs from a conventional one for a correction term in section prediction, leveraged as degradation symptom, based on the deviation of the model-state derivative with respect to the actual measurement. The monitoring algorithm, being executable in real-time and capable of identifying incipient degradations of the thermal flow, demonstrates applicability for online diagnostics and predictive maintenance purposes.

Investigation of a perturbation-based model updating approach for structural health and event monitoring

With the intention to detect structural events, for example, occurrence of damage and assessing their severity under service, structural health and event monitoring proved itself as a reliable evaluation tool. While many algorithmic approaches exist, model-based monitoring offers the prominent advantage to enable extensive analysis of damage states based on a structural digital twin. In order to accurately replicate the dynamical behaviour of a real-world structure, inherent physical properties, for example, mass, stiffness and structural damping, of the digital twin might be corrected from nominal values. This can be achieved by numerical model updating procedures. In this study, the application of a model updating approach is presented to eliminate modal discrepancies. Based on the perturbated modal dynamic residual, an updating equation is formulated. Stiffness and mass correction terms with preserved orthogonality and symmetry conditions are determined by the method of least squares. A case study using computationally generated modal data is conducted, evaluating the numerical updating performance in terms of correction accuracy and reproduction of modal parameters. Finally, the transient structural response to an impulse excitation is compared between updated and reference model. The gathered results prove the method’s suitability as accurate and robust updating procedure, fostering its application in model-based monitoring frameworks.

Quantitative analytics for protein refolding states

Development and optimization of inclusion body (IB) refolding processes is often based on empirical strategies. Thus, generation of in-depth process understanding is crucial to shift towards more knowledge-driven approaches. Within this contribution the goal was to establish a systematic approach for the reliable quantification of process dynamics in protein refolding processes. Established offline- analytical tools like HPLC-based methods or photometric assays were analyzed regarding their specifications and consequent suitability to quantify protein states. An in-silico study was conducted to define requirements for state quantification by analyzing the influences of error, discrete sampling intervals and the pace of the reaction on signal quality. Refolding and aggregation reaction rates were calculated by finite difference approximation with errors propagated from the corresponding folding state. Application of the defined principles on two real Lactate dehydrogenase fed-batch refolding processes resulted in two individual sampling strategies. At reaction rates of up to 0.58 g h−1 description of the ongoing dynamics could be done accurately with high sampling intervals during the first few hours of processing as shown by comparison to a noise free simulation. The proposed method has the potential to be transferred to other proteins and to facilitate the development of model-based monitoring & control strategies.

Condition Monitoring of the Torque Imbalance in a Dual-Stator Permanent Magnet Synchronous Motor for the Propulsion of a Lightweight Fixed-Wing UAV

This paper deals with the development of a model-based technique to monitor the condition of torque imbalances in a dual-stator permanent magnet synchronous motor for UAV full-electric propulsion. Due to imperfections, degradations or uncertainties, the torque split between power lines can deviate from the design, causing internal force-fighting and reduced efficiency. This study demonstrates that, by only elaborating the measurements of speed and direct/quadrature currents of the stators during motor acceleration/deceleration, online estimations of demagnetization and electrical angle misalignment can be obtained, thus permitting the evaluation of the imbalance and total torque of the system. A relevant outcome is that the technique can be used for developing both signal-based and model-based monitoring schemes. Starting from physical first-principles, a nonlinear model of the propulsion system, including demagnetization and electrical angle misalignment, is developed in order to analytically derive the relationships between monitoring inputs (currents and speed) and outputs (degradations). The model is experimentally validated using a system prototype characterized by asymmetrical demagnetization and electrical angle misalignment. Finally, the monitoring effectiveness is assessed by simulating UAV flight manoeuvres with the experimentally validated model: injecting different levels of degradations and evaluating the torque imbalance.

Special Issue on Advanced Diagnostics and Prognostics for Automotive Systems

This special issue on Advanced Diagnostics and Prognostics for Automotive Systems provides an opportunity to discuss recent advances in different topics related to modern automotive systems. The topics include model-based monitoring algorithm for diesel vehicle aftertreatment system, air-path health management strategy for estimation of the mass flows and mitigation of the air-path faults, early detection of anomalies in fuel system evaporative and purge systems leveraging vehicles connectivity, review of intelligent maintenance of EVs at both component level and system level to identify existing gaps in EVs DnP, detection and isolation of ground connection faults in electronic control units, root cause detection of defects in arc stud welding that is used to join automotive structures, machine learning based anomaly detection framework demonstrated on the hydraulic system of electric off-road vehicles, and signal abstraction to assist fast root-cause detection of large scale control systems.

Predictive Value of Early Autism Detection Models Based on Electronic Health Record Data Collected Before Age 1 Year

Autism detection early in childhood is critical to ensure that autistic children and their families have access to early behavioral support. Early correlates of autism documented in electronic health records (EHRs) during routine care could allow passive, predictive model-based monitoring to improve the accuracy of early detection. To quantify the predictive value of early autism detection models based on EHR data collected before age 1 year. This retrospective diagnostic study used EHR data from children seen within the Duke University Health System before age 30 days between January 2006 and December 2020. These data were used to train and evaluate L2-regularized Cox proportional hazards models predicting later autism diagnosis based on data collected from birth up to the time of prediction (ages 30-360 days). Statistical analyses were performed between August 1, 2020, and April 1, 2022. Prediction performance was quantified in terms of sensitivity, specificity, and positive predictive value (PPV) at clinically relevant model operating thresholds. Data from 45 080 children, including 924 (1.5%) meeting autism criteria, were included in this study. Model-based autism detection at age 30 days achieved 45.5% sensitivity and 23.0% PPV at 90.0% specificity. Detection by age 360 days achieved 59.8% sensitivity and 17.6% PPV at 81.5% specificity and 38.8% sensitivity and 31.0% PPV at 94.3% specificity. In this diagnostic study of an autism screening test, EHR-based autism detection achieved clinically meaningful accuracy by age 30 days, improving by age 1 year. This automated approach could be integrated with caregiver surveys to improve the accuracy of early autism screening.

Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of E. coli

We discuss the application of a nonlinear model predictive control (MPC) and moving horizon estimation (MHE) framework to achieve optimal operation of E. coli fed-batch cultivations with intermittent bolus feeding. 24 parallel experiments were considered in a high-throughput mini-bioreactor platform at a 10 mL scale. The robotic facility can run up to 48 fed-batch processes in parallel with automated liquid handling, online and at-line analytics. Three main challenges emerge in implementing the model-based monitoring and control framework: First, the inputs are given in an instantaneous pulsed form by bolus injections; second, online and at-line measurement frequencies are severely imbalanced; and third, optimization for the distinctive multiple reactors can be either parallelized or integrated. We address these challenges by incorporating the concept of impulsive control systems, formulating multi-rate MHE with identifiability analysis, and suggesting criteria for deciding the reactor configuration. In this study, we present the key elements and background theory of the implementation with in silico simulations for bacterial fed-batch cultivations.

Quality-by-control of intensified continuous filtration-drying of active pharmaceutical ingredients.

Continuous manufacturing and closed-loop quality control are emerging technologies that are pivotal for next-generation pharmaceutical modernization. We develop a process control framework for a continuous carousel for integrated filtration-drying of crystallization slurries. The proposed control system includes model-based monitoring and control routines, such as state estimation and real-time optimization, implemented in a hierarchical, three-layer quality-by-control (QbC) framework. We implement the control system in ContCarSim, a publicly available carousel simulator. We benchmark the proposed control system against simpler methods, comprising a reduced subset of the elements of the overall control system, and against open-loop operation (the current standard in pharmaceutical manufacturing). The proposed control system demonstrates superior performance in terms of higher consistency in product quality and increased productivity, proving the benefits of closed-loop control and of model-based techniques in pharmaceutical manufacturing. This study represents a step forward toward end-to-end continuous pharmaceutical processing, and in the evolution of quality-by-design toward quality-by-control.

Gross error detection in steam turbine measurements based on data reconciliation of inequality constraints

Maintaining the online calculation accuracy of isentropic efficiency of a steam turbine stage is challenging due to widely existing gross errors in steam turbine measurements. They invalidate modelling results and hinder model-based monitoring and optimization. Data reconciliation is a mathematical method for gross error detection and has been applied in various industrial systems. In power plant systems, previous studies focus on gross error detection of flow rate measurements in regenerative systems based on equality constraints, which is insufficient for gross error detection of steam turbine systems. We propose a data reconciliation model adding inequality constraints to solve the problem. Statistical test is used to detect gross errors in steam turbine systems. Then an in-service 660 MW ultra-supercritical double reheat power plant is selected as a case study. Gross errors of flow rate measurements are detected and eliminated firstly. Then nonlinear inequality constraints, entropy increase of each stage, are added for further detection. Results show that the proposed model effectively detects gross errors in the steam turbine system and further improve the thermal calculation accuracy by 3.1–5.7%. It provides quantitative guidance for the calibration and maintenance of measurement instruments and facilitates performance monitoring and operation optimization in in-service power plants.

Model-Based Monitoring of Dengue Spreading

The problem of designing a system for monitoring the spread of dengue in a mixed population of humans and mosquitos is addressed. For this purpose a model–based observer approach is employed based on a model that describes the actual number of infected humans (with constant population size) and mosquitos (with time–varying population size) and which has been previously validated with data from different regions of the state of Morelos in Mexico using the number of new reported cases as available measurement. This model has time–varying reproduction parameters for the mosquitos and thus enables to account for different climatic conditions in the course of the year (in particular the rain and dry periods) that affect the mosquito population size. An analysis of the structural observability, based on the preliminary assumption of a continuous measurement of the accumulative number of reported cases of human infections is carried out to motivate a reduced order model that is structurally observable. For this reduced order model a continuous–discrete extended Kalman Filter is designed as basis for the monitoring and prediction scheme. The approach is validated using real data from Cuernavaca, Morelos, Mexico, showing a high potential for future developments towards automated monitoring schemes.

Detection of Cyber Attacks on a Water Treatment Process

This paper investigates the detection of cyber attacks in a kind of critical infrastructure, the water treatment processes. For this purpose, the real data from the secure water treatment testbed (SWaT) in the iTrust Centre for Research in Cyber Security at the Singapore University of Technology and Design have been used in the investigation. More than 30 different attacks happen on the communication network between the programmable logic controller (PLC) and the sensors/actuators. After understanding the basic working mechanism of the SWaT process, an attack detection system is developed systematically, which consists of a limit value check and safety rule unit, a model-based monitoring unit and a data-driven monitoring unit. To cope with the nonstationary behavior of some water chemical properties such as pH and oxidation reduction potential, the recursive principal component analysis (RPCA) is applied in the data-driven part. It is shown how to divide the SWaT process suitably to integrate the model-based and data-driven monitoring for the SWaT process. The comparison shows that the proposed attack detection system outperforms other existing attack detection systems for the SWaT process.

Sensor Fault Accommodation for a Plug Flow Reactor using an M-Estimator

The presence of gross errors in the measurements can lead to biased state estimates when conventional Bayesian estimators are used. This can hamper the model-based monitoring and control schemes that rely on the accurate state estimates. In this work, we have developed a framework for robust estimation of the state profiles for Distributed parameter systems (DPSs), in the presence of biased measurements. The proposed approach uses an M-estimator to identify the faulty sensor. The sensor fault diagnosis is then used to augment the state estimator with an extra state that estimates the drifting sensor bias. The proposed approach has been applied to an Auto-Thermal tubular reactor system. The proposed scheme successfully isolates the biased temperature sensors and includes or removes additional bias states as and when required. The gross errors/biases are estimated and subsequently accommodated to provide accurate estimates of spatial profiles of reactor concentration and temperature.

Model-based monitoring of diffuser fouling using standard sensors

Fouling of fine-pore diffusers can cause substantial aeration energy wastage. It remains challenging to monitor their condition and decide the optimal time for labour-intensive and costly cleaning actions. In this study, we show that data from standard sensors (airflow rate, dissolved oxygen concentration, pressure and airflow valve position), which are fed to simple models, can track the diffuser's condition. Additionally, the parameter estimation of diffuser dynamic wet pressure, oxygen transfer rate, respiration rate and the joint alpha fouling factor (αF) was facilitated by an active fault detection inspired method. The method executes a sequence with piecewise constant valve positions via the control system. As a result, airflow rates in a sequence similar to a staircase are obtained, which simplifies the estimation of dissolved oxygen dynamics and airflow rate dynamics. The proposed method was evaluated on a full scale over 18 months and successfully detected a reduced cleaning in the diffusers and several sensor-related disturbances. Ultimately, the findings motivate further research on how modelling combined with repetitive process disturbances can leverage data-driven insights from standard instrumentation.

Model-Based Monitoring of Biotechnological Processes—A Review

The monitoring of the main variables and parameters of biotechnological processes is of key importance for the research and control of the processes, especially in industrial installations, where there is a limited number of measurements. For this reason, many researchers are focusing their efforts on developing appropriate algorithms (software sensors (SS)) to provide reliable information on unmeasurable variables and parameters, based on the available on-line information. In the literature, a large number of developments related to this topic that concern data-based and model-based sensors are presented. Up-to-date reviews of data-driven SS for biotechnological processes have already been presented in the scientific literature. Hybrid software sensors as a combination between the abovementioned ones are under development. This gives a reason for the article to be focused on a review of model-based software sensors for biotechnological processes. The most applied model-based methods for monitoring the kinetics and state variables of these processes are analyzed and compared. The following software sensors are considered: Kalman filters, methods based on estimators and observers of a deterministic type, probability observers, high-gain observers, sliding mode observers, adaptive observers, etc. The comparison is made in terms of their stability and number of tuning parameters. Particular attention is paid to the approach of the general dynamic model. The main characteristics of the classic variant proposed by D. Dochain are summarized. Results related to the development of this approach are analyzed. A key point is the presentation of new formalizations of kinetics and the design of new algorithms for its estimation in cases of uncertainty. The efficiency and applicability of the considered software sensors are discussed.

Systematic simplification of the Anaerobic Digestion Model No. 1 (ADM1) – Laboratory experiments and model application

Due to a limited number of available measurements on agricultural biogas plants, established process models, such as the Anaerobic Digestion Model No. 1 (ADM1), are rarely applied in practise. To provide a reliable basis for model-based monitoring and control, different model simplifications of the ADM1 were implemented for process simulation of semi-continuous anaerobic digestion experiments using agricultural substrates (maize silage, sugar beet silage, rye grain and cattle manure) and industrial residues (grain stillage). Individual model structures enable a close depiction of biogas production rates and characteristic intermediates (ammonium nitrogen, propionic and acetic acid) with equal accuracy as the original ADM1. The impact of different objective functions and standard parameter values on parameter estimates of first-order hydrolysis constants and microbial growth rates were evaluated. Due to the small number of required model parameters and suitable system characteristics, simplified model structures show clear advantages for practical application on agricultural biogas plants.

Systematic simplification of the Anaerobic Digestion Model No. 1 (ADM1) – Model development and stoichiometric analysis

Rigorous process models provide a reliable basis for model-based monitoring and control of anaerobic digestion plants. Due to the complex model structure and non-linear system characteristics, the established Anaerobic Digestion Model No. 1 (ADM1) is rarely applied in industrial plant operation. The present investigation proposes a systematic procedure for successive model simplification and presents the description of five model variants of a mass-based ADM1. Individual model structures greatly differ in their number of implemented process phases, characteristic components and required parameters. Simplified model variants combine nutrient degradation and biogas formation based on first-order sum reactions, whereas complex model structures describe individual degradation pathways and intermediates during acido- and acetogenesis. Characteristic features of the derived model structures as well as the stoichiometric methane potentials and microbial biomass yields of the underlying degradation pathways of individual model variations are evaluated and discussed in detail.

Model-Based Monitoring of Patient Response to Staged Thyroidectomy

The goal of this study is to develop a model-based control chart for monitoring patient behavior in a staged thyroidectomy considering risk factors and clinical prescription. prospectively collected data are gathered from thyroid surgery unit of a hospital located in Tehran, Iran for 80 staged thyroidectomy patients discharged from 2009 to 2013. A risk adjusted state space model is developed based on the staged thyroidectomy. Variables to be included in the model are determined as a part of the model building process. Performance criteria, clinical prescription and patient risk factors are three variable components for the model. The appropriate risk factors are directly involved in the model and no scoring system is used for the model construction. Model identification is performed in two steps; model order selection and parameter estimation. In the first step, Hankel singular value decomposition (HSVD) is used for detecting the model order and in the second step, unknown parameters are estimated by the prediction error minimization (PEM) method. For monitoring patient responses, a group individual (GI) control chart is introduced and applied to a real-world problem. Results indicate that the suggested control chart can monitor the staged thyroidectomy patient’s behavior with an acceptable accuracy. Also, computer aided diagnosis (CAD) systems can be developed based on the proposed identification and monitoring method.

In-process identification of machine tool dynamics

An accurate description of the machine tool dynamics is essential in machining process simulation, chatter avoidance, and model-based monitoring. However, the standard practice of experimental modal analysis of an idle machine tool does not always provide an accurate representation of its dynamics under operating conditions. This paper proposes a modified transmissibility function-based operational modal analysis (TOMA) that assumes consistent mode shape for the spindle assembly to monitor the changes in natural frequency and damping ratio of the spindle. The transmissibility function-based solution considers only the relative vibrations between two sensors mounted on the spindle housing while the machine is excited by the cutting forces during machining. Experimental application of the Mode Shape Compensated TOMA was successfully demonstrated in multiple milling tests.

Model-Based Structural Health Monitoring of Box Girders

In the recent years, interest has been expressed towards incorporating Structural Health Monitoring (SHM) systems to ship hulls in order to transition from preventive to predictive maintenance procedures. In this work, an initial approach is undertaken to investigate the capabilities of a model-based method treating damage identification as an optimization problem solved using a genetic algorithm. An idealization of the hull structure is considered based on hull girder theory that allows for lab scale experimental testing. Specifically, a box girder is considered with a circular discontinuity as the generalized damage that causes extensive stress redistribution, replicating the effect of hull damage modes of interest. A three-point bending load case is considered to emulate still water bending loads. Damage is considered to exist, and the goal of the proposed strategy is to provide a prediction on its location and magnitude (level 2 SHM). This is achieved using strain measurements obtained from sensors located on theoretical zero-strain directions as inputs to the optimization scheme treating the damage identification problem. Results from both assessment strategies highlighted the influence of measurement-related uncertainties on the method’s predictive capabilities.