Physics-Based Digital Twins Merging With Machines: Cases of Mobile Log Crane and Rotating Machine

Kurvinen, Emil; Kutvonen, Antero; Ukko, Juhani; Khadim, Qasim; Hagh, Yashar Shabbouei; Jaiswal, Suraj; Neisi, Neda; Zhidchenko, Victor; Kortelainen, Juha; Timperi, Mira; Kokkonen, Kirsi; Virtanen, Juha M.; Zeb, Akhtar; Lämsä, Ville; Nieminen, Vesa; Junttila, Jukka; Savolainen, Mikko; Rantala, Tuija; Valjakka, Tiina; Donoghue, Ilkka; Elfvengren, Kalle; Nasiri, Mina; Rantala, Tero; Kurinov, Ilya; Sikanen, Eerik; Pyrhönen, Lauri; Hannola, Lea; Handroos, Heikki; Rantanen, Hannu; Saunila, Minna; Sopanen, Jussi; Mikkola, Aki

doi:10.1109/access.2022.3170430

Cited by 17 publications

(11 citation statements)

References 70 publications

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“…The robustness of the algorithm is validated through a simulation model based on Lorenz process and an industrial diesel hydrotreating case. A research work in [ 23 ] presented technical features of merging machinery with a physics-based DTs based on EKF methods for providing successful implementation information. In this regard, two industrial processes were analyzed to demonstrate the methodology; the first is a mobile log crane, which is applicable for the heavy mobile machinery, and the second process concerned a rotating electric machine in case of unexpected failures.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin Model of Electric Drives Empowered by EKF

Ebadpour

Jamshidi

Talla

et al. 2023

Sensors

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Digital twins, a product of new-generation information technology development, allows the physical world to be transformed into a virtual digital space and provide technical support for creating a Metaverse. A key factor in the success of Industry 4.0, the fourth industrial revolution, is the integration of cyber–physical systems into machinery to enable connectivity. The digital twin is a promising solution for addressing the challenges of digitally implementing models and smart manufacturing, as it has been successfully applied for many different infrastructures. Using a digital twin for future electric drive applications can help analyze the interaction and effects between the fast-switching inverter and the electric machine, as well as the system’s overall behavior. In this respect, this paper proposes using an Extended Kalman Filter (EKF) digital twin model to accurately estimate the states of a speed sensorless rotor field-oriented controlled induction motor (IM) drive. The accuracy of the state estimation using the EKF depends heavily on the input voltages, which are typically supplied by the inverter. In contrast to previous research that used a low-precision ideal inverter model, this study employs a high-performance EKF observer based on a practical model of the inverter that takes into account the dead-time effects and voltage drops of switching devices. To demonstrate the effectiveness of the EKF digital twinning on the IM drive system, simulations were run using the MATLAB/Simulink software (R2022a), and results are compared with a set of actual data coming from a 4 kW three-phase IM as a physical entity.

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Section: Introductionmentioning

confidence: 99%

Digital Twin Model of Electric Drives Empowered by EKF

Ebadpour

Jamshidi

Talla

et al. 2023

Sensors

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“…Especially, realtime capable simulation models enable the development of digital twins that can operate in parallel to the actual machine, and therefore give insights into the machine and its operation. For example, multibody-based methods are highly capable in dynamic applications, e.g., in mobile heavy machinery, providing high accuracy, computational efficiency, and real-time capability [1]- [3]. Real-time capability is desired, because it can be used, for example, in simulators that respond to real-time commands, i.e., operating the virtual machine in the same way as its physical counterpart.…”

Section: Introductionmentioning

confidence: 99%

“…Further, the multibody formulations can be combined with VOLUME 4, 2016 state and parameter estimation methods, [8]- [11] to enhance the measurement data of a real system, [12]- [14]. The estimated data provides information about the unknown states and parameters, which can be used in the wide range of condition monitoring and predictive maintenance of the real system [3], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

State Estimation in a Hydraulically Actuated Log Crane Using Unscented Kalman Filter

et al. 2022

Self Cite

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Multibody system dynamics approaches together with state estimation methods can reduce the need for a large number of sensors, especially in the digital twin of working mobile machinery. To demonstrate this, a hydraulically actuated machine was modeled using the double-step semi-recursive multibody formulation and lumped fluid theory in terms of system independent states. Next, because of the high non-linearity of the modeled system and with respect to the reported performance degradation of the Extended Kalman Filters (EKF), which are mostly related to the linearization procedure of this filter, the Unscented Kalman Filter (UKF) was implemented to achieve high accuracy and performance. The methodology of the proposed approaches was applied to a mobile log crane model PATU 655. The implementation of the proposed estimation algorithms is demonstrated with three different multibody based simulation models: the synthetic real system producing the artificial measurements, the simulation model, and the estimation model. Encoders and pressure sensors, installed on the synthetic real system, provided synthetic sensor measurement data. To mimic real-world conditions, the estimation and simulation models used in the processing of the state estimation algorithm were assumed to have errors in the initial conditions and force model. The proposed UKF was applied to the estimation model with the synthetic sensor measurement data. The minimum percent normalized root mean square errors in the associated measured and unmeasured states of case example were 0.11% and 1.86%, respectively. The UKF using the multibody system dynamics formulations is able to estimate the case example states despite 15% and 60% errors in mass and inertial properties of bodies and Payload, respectively, confirming the accuracy and performance of the algorithm.INDEX TERMS state estimation, multibody system dynamics, hydraulic actuators, unscented Kalman filter, working machine, digital twin

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“…Rotating machinery is an indispensable main equipment in industrial machinery because of its unique properties and functions [ 2 ]. Typical rotating machinery includes gearboxes, reducers, steam turbines, gas turbines, fans, generators, and engines, which are widely used in electric power, petrochemical, metallurgy, automobile manufacturing, aerospace, and other sectors [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…According to the research, it is found that the bearing damage fault accounts for about 40% of the faults of rotating machinery, and the fault caused by gear failure accounts for about 10.3%. Therefore, it is of great significance to find out the fault conditions in the rotating machinery in time and conduct accurate fault diagnosis and maintenance for the rotating machinery to ensure the safe and stable operation of the production system and reduce the probability of catastrophic accidents [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis Method of Rotating Machinery Based on Conditional Random Field

Zheng

Qiao

2022

Computational Intelligence and Neuroscience

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Rotating machinery is indispensable mechanical equipment in modern industrial production. However, rotating machinery is usually under heavy load. Due to the complexity of its structure and the severity of its working conditions, it is urgent to find effective condition monitoring methods and fault maintenance strategies for its safe and reliable operation. The conditional random field is derived from the maximum entropy model, which solves the problem of label bias and improves the convergence speed of model training. Combining Kriging theory and random field theory, this study proposes a three-dimensional conditional random field generation method based on failure time, applies this method to the comparison of measured data and other nonconditional random fields, and then analyzes the failure probability of rotating machinery in the failure process by combining the numerical calculation results and reliability theory. It is found that the conditional random field generation method can effectively describe the spatial variability of rotating machinery parameters. Compared with the nonconditional random field, the reliability index of rotating machinery failure time is improved by 0.8823, so the conditional random field can better describe the reliability of rotating machinery.

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Physics-Based Digital Twins Merging With Machines: Cases of Mobile Log Crane and Rotating Machine

Cited by 17 publications

References 70 publications

Digital Twin Model of Electric Drives Empowered by EKF

Digital Twin Model of Electric Drives Empowered by EKF

State Estimation in a Hydraulically Actuated Log Crane Using Unscented Kalman Filter

Reliability Analysis Method of Rotating Machinery Based on Conditional Random Field

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