Rail neutral temperature estimation using impulse vibration and machine learning

2021 ◽  
Author(s):  
Yuning Wu ◽  
Xuan Zhu ◽  
Chi-Luen Huang ◽  
Sangmin Lee ◽  
Marcus Dersch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Temperature Estimation ◽  
Neutral Temperature

scholarly journals Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4655
Author(s):  
Dariusz Czerwinski ◽  
Jakub Gęca ◽  
Krzysztof Kolano
Keyword(s):  
Machine Learning ◽  
Temperature Measurement ◽  
Stochastic Gradient Descent ◽  
Estimation Accuracy ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Bldc Motor ◽  
Temperature Estimation ◽  
Motor Operation

In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R2 was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R2 to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.

scholarly journals Data-driven study on individual occupant comfort using heating setpoints and window openings in new low-energy apartments – preliminary insights

2019 ◽  
Vol 111 ◽  
pp. 04063 ◽  
Author(s):  
Lucile Sarran ◽  
Morten Herget Christensen ◽  
Christian Anker Hviid ◽  
Andrea Marin Radoszynski ◽  
Carsten Rode ◽  
...  
Keyword(s):  
Residential Building ◽  
Heating System ◽  
Influential Factors ◽  
Low Energy ◽  
Structured Interviews ◽  
Temperature Estimation ◽  
Neutral Temperature ◽  
Window Opening ◽  
Occupant Comfort ◽  
Valve Opening

This work suggests a method to evaluate residential building occupants’ neutral temperature in winter based on their interaction with their heating system. This study applies the developed method on eight new, low-energy apartments in Copenhagen, Denmark. A set of indoor temperature, heating setpoint, window opening and floor heating valve opening data was collected from mid-January to the end of April, spanning through a large part of the Danish heating season. Semi-structured interviews were performed with occupants of three of the eight apartments in order to understand their use of their heating system. This preliminary study permits to highlight the potential and the current limitations of the proposed method, both for neutral temperature estimation as such and for applications in optimizing the energy flexibility provided by the building. This article suggests directions for further elaboration of the model. The main two influential factors highlighted here affecting setpoint adjustment are the occupants’ acceptability of temperature variation and their ability to control the heating system.

Rail Neutral Temperature Estimation Using Field Data, Numerical Models, and Machine Learning

2021 ◽  
Author(s):  
Yuning Wu ◽  
Xuan Zhu ◽  
Chi-Luen Huang ◽  
Sangmin Lee ◽  
Marcus Dersch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Field Data ◽  
Thermal Stresses ◽  
Numerical Models ◽  
Estimation Method ◽  
Supervised Machine Learning ◽  
Estimation Accuracy ◽  
Optimization Approach ◽  
Neutral Temperature ◽  
Wide Range

Abstract Effective Rail Neutral Temperature (RNT) management is needed for continuous welded rail (CWR). RNT is the temperature at which the longitudinal stress of a rail is zero. Due to the lack of expansion joints, CWR develops internal tensile or compressive stresses when the rail temperature is below or above, respectively, the RNT. Mismanagement of RNT can lead to rail fracture or buckling when thermal stresses exceed the limits of rail steel. In this work, we propose an effective RNT estimation method structured around four hypotheses. The work leverages field-collected vibration test data, high-fidelity numerical models, and machine learning techniques. First, a contactless non-destructive and non-disruptive sensing technology was developed to collect real-world rail vibrational data. Second, the team established an instrumented field test site at a revenue-service line in the state of Illinois and performed multi-day data collection to cover a wide range of temperature and thermal stress levels. Third, numerical models were developed to understand and predict rail vibration behavior under the influence of temperature and longitudinal load. Excellent agreement between model and experimental results were obtained using an optimization approach. Finally, a supervised machine learning algorithm was developed to estimate RNT using the field-collected rail vibration data. Sensitivity studies and error analyses were included in this work. The system performance with field data indicates that the proposed framework can support reasonable RNT estimation accuracy when measurement or model noise is low.

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