Dynamics Modeling and Parameter Identification of a Cooling Fan System

Peng, Chao-Chung; Lin, Yung-I

doi:10.1109/amcon.2018.8614957

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…The LS algorithm has been widely applied in identifying the system parameters of dynamic systems [6,28,34,35]. The advantage of using the LS algorithm is that it enables Viscous coefficient of Joint 1…”

Section: Least-squares Algorithmmentioning

confidence: 99%

“…For some open-loop stable systems [6,35], it is allowed direct open-loop excitation for system identification. The so-called open-loop excitation means that the system inputs do not involve the system states.…”

Section: Design and Implementation Of Excitation Trajectorymentioning

confidence: 99%

“…For robotic systems, the highly coupled nonlinear dynamics in conjunction with the measurement quantization errors make the system parameter identification very difficult if not impossible. For these systems, the least-squares (LS) algorithm is an approach widely applied to identify system parameters [6,16,24,28,34,35]. For the application of the LS algorithm, it is necessary to formulate the overdetermined set of equations Y(𝑡) = 𝚽(𝑡)X (the detailed definition is given in Section 3).…”

Section: Introductionmentioning

confidence: 99%

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An Integral Method for Parameter Identification of a Nonlinear Robot subject to Quantization Error

Peng

Juang

2023

Preprint

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This paper focuses on the parameter identification of a class of nonlinear mechanical dynamic systems subject to feedback quantization errors.} A second-order integral method for the parameter identification of a nonlinear two-degrees-of-freedom robotic manipulator is studied. To obtain accurate parameter estimates, an optimal excitation trajectory subject to state constraints is proposed. The designated excitation trajectory is able to minimize estimation uncertainties, providing the controlled system trajectory reaches the desired optimal excitation trajectory. Moreover, for most of the nonlinear robotic systems, the output measurements are acquired from the encoders subject to apparent quantization error. Directly differentiating the position to estimate the velocity and acceleration trajectories is unrealistic and unsuitable due to the amplified quantization errors will cause considerable estimation errors. Instead, the finite Fourier series is introduced to perform optimal estimation of the position, velocity, and acceleration histories to capture the dominant frequencies of the system response. A weighted least-squares solution is further proposed to improve parameter identification precision. The comparative studies reveal that the integral method has superior parameter estimation performance than the direct difference model and the filtered regression model in the existing literature. Experimental simulations illustrate the effectiveness of the presented method.

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Section: Least-squares Algorithmmentioning

confidence: 99%

Section: Design and Implementation Of Excitation Trajectorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Integral Method for Parameter Identification of a Nonlinear Robot subject to Quantization Error

Peng

Juang

2023

Preprint

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“…Figure 10 shows that the preliminary LPC model (14) is able to achieve more than 95% VAF of the system response. Owing to the series connection of the fan and the LPC section, a fan dynamic like regression model [30] is considered for the transient response modeling of t 235…”

Section: B Lpc Modelingmentioning

confidence: 99%

A Model Following Based Real-Time Prediction and Monitoring of TFE-731 Turbofan Engine Compressors

Chen

Peng

2019

IEEE Access

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An engine is the heart of an aircraft. It produces thrust, drives the generator, pumps the hydraulic system and provides compressed air for all the systems on the aircraft. Its health plays an essential role in flight safety. In the past, the standard operation procedure to evaluate the health status of engines usually depended on some specific parameters, like inter-stage turbine temperature, low-pressure spool rotating speed or high-pressure spool rotating speed. Once part of the parameters pass over certain safety boundaries that were previously set by the manufacturers or the operators, the engine would be regarded as an unhealthy engine. Nevertheless, in practical applications, such threshold-style mechanism cannot reflect engine fault immediately and therefore could lead to potential flight risk. To solve this issue, a precise forecast model of the engine has to be established. Consequently, this research is dedicated to develop algorithms for engine modeling as well as the identification of optimal parameters. For the TFE-731 engine, there are three section models considered, including low pressure compressor (LPC) model, high pressure compressor (HPC) model and overall turbofan dynamics model. Those models are derived with the consideration of physical isentropic compression equation as well as a data-driven regression technique. Experiments show that a precise modeling fitting can be achieved by using regression analysis and nonlinear optimal parameter estimation. Finally, to compare the prediction stability and accuracy, associated training models using neural network (NN) are also presented. Comparison studies verify that the proposed method is able to achieve stable as well as accurate TFE-731 real-time response prediction and monitoring. INDEX TERMS Turbofan engine modeling, parameter identification, fault monitoring.

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“…They are often used to maintain temperature and ensure an optimal operation environment. Due to their low development cost, efficient heat dissipation, and air circulation, a cooling fan system is often regarded as the primary solution for environmental temperature control use [4,5]. Despite playing an important role in the manufacturing process, the rotary mechanism of the cooling fan is highly susceptible to failure, making it one of the top 10 failing components in electronic products [6].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Neural Ordinary Differential Equation Based Digital Twin Modeling and Online Diagnosis for an Industrial Cooling Fan

Peng,

Chen

2023

Future Internet

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Digital twins can reflect the dynamical behavior of the identified system, enabling self-diagnosis and prediction in the digital world to optimize the intelligent manufacturing process. One of the key benefits of digital twins is the ability to provide real-time data analysis during operation, which can monitor the condition of the system and prognose the failure. This allows manufacturers to resolve the problem before it happens. However, most digital twins are constructed using discrete-time models, which are not able to describe the dynamics of the system across different sampling frequencies. In addition, the high computational complexity due to significant memory storage and large model sizes makes digital twins challenging for online diagnosis. To overcome these issues, this paper proposes a novel structure for creating the digital twins of cooling fan systems by combining with neural ordinary differential equations and physical dynamical differential equations. Evaluated using the simulation data, the proposed structure not only shows accurate modeling results compared to other digital twins methods but also requires fewer parameters and smaller model sizes. The proposed approach has also been demonstrated using experimental data and is robust in terms of measurement noise, and it has proven to be an effective solution for online diagnosis in the intelligent manufacturing process.

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Dynamics Modeling and Parameter Identification of a Cooling Fan System

Cited by 9 publications

References 9 publications

An Integral Method for Parameter Identification of a Nonlinear Robot subject to Quantization Error

An Integral Method for Parameter Identification of a Nonlinear Robot subject to Quantization Error

A Model Following Based Real-Time Prediction and Monitoring of TFE-731 Turbofan Engine Compressors

A Hybrid Neural Ordinary Differential Equation Based Digital Twin Modeling and Online Diagnosis for an Industrial Cooling Fan

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