Virtual sensors for estimation of energy consumption and thermal comfort in buildings with underfloor heating

Ploennigs, Joern; Ahmed, Ammar; Hensel, Burkhard; Stack, Paul; Menzel, Karsten

doi:10.1016/j.aei.2011.07.004

Cited by 60 publications

(25 citation statements)

References 24 publications

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“…They use a model (analytic, stochastic, or simulative) to either compute a required sensor value out of readings of other sensors [29], or out of previous readings of the same sensor to eliminate for example dead times [30]. They can also be used for confining sensor failures by using comparable sensors in the same or nearby rooms or a predictive model out of historical data.…”

Section: A Virtual Sensorsmentioning

confidence: 99%

Sensors, models and platform for ambient control

Stein

Lehmann

Ploennigs

et al. 2012

IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society

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Section: A Virtual Sensorsmentioning

confidence: 99%

Sensors, models and platform for ambient control

Stein

Lehmann

Ploennigs

et al. 2012

IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society

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“…To bridge the gap between direct sensing and indirect sensing, virtual sensing, as a complement to physical sensing, has emerged as a viable, noninvasive, and cost effective method to infer difficult-to-measure or expensive-to-measure parameters in dynamic systems based on computational models [22]. It has been investigated for active noise and vibration control [23], industrial process control [24], building operation optimization [25], lead-through robot programming [26], product quality of semiconductor industry [27], and tool condition monitoring [28,29].…”

Section: Introductionmentioning

confidence: 99%

Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing

Wang

Xie

Zhao

et al. 2017

Robotics and Computer-Integrated Manufacturing

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a b s t r a c tPervasiveness of ubiquitous computing advances the manufacturing scheme into a ubiquitous manufacturing era which poses significant challenges on sensing technology and system reliability. To improve manufacturing system reliability, this paper presents a new virtual tool wear sensing technique based on multisensory data fusion and artificial intelligence model for tool condition monitoring. It infers the difficult-to-measure tool wear parameters (e.g. tool wear width) by fusing in-process multisensory data (e.g. force, vibration, etc.) with dimension reduction technique and support vector regression model. Different state-of-the-art dimension reduction techniques including kernel principal component analysis, locally linear embedding, isometric feature mapping, and minimum redundancy maximum relevant method have been investigated for feature fusion in a virtual sensing model, and the kernel principal component analysis performs best in terms of sensing accuracy. The effectiveness of the developed virtual tool wear sensing technique is experimentally validated in a set of machining tool run-to-failure tests on a computer numerical control milling machine. The results show that the estimated tool wear width through virtual sensing is comparable to that measured offline by a microscope instrument in terms of accuracy, moreover, in a more cost-effective manner.

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“…It has been investigated for active noise and vibration control (Petersen et al 2008), industrial process control (Cheng et al 2004), building operation optimization (Ploennigs et al 2011), lead-through robot programming (Ragaglia et al 2016), product quality of hydrodesulfurization (HDS) (Shokri et al 2015), and tool condition monitoring (Bustillo et al 2011;Li and Tzeng 2000). Data-driven virtual sensing techniques are favorable by fusing the extracted features from noisy online measurements to infer the difficult-tomeasure parameters based on artificial intelligence models (Gelman et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Virtual sensing for gearbox condition monitoring based on kernel factor analysis

et al. 2017

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Vibration and oil debris analysis are widely used in gearbox condition monitoring as the typical indirect and direct sensing techniques. However, they have their own advantages and disadvantages. To better utilize the sensing information and overcome its shortcomings, this paper presents a virtual sensing technique based on artificial intelligence by fusing low-cost online vibration measurements to derive a gearbox condition indictor, and its performance is comparable to the costly offline oil debris measurements. Firstly, the representative features are extracted from the noisy vibration measurements to characterize the gearbox degradation conditions. However, the extracted features of high dimensionality present nonlinearity and uncertainty in the machinery degradation process. A new nonlinear feature selection and fusion method, named kernel factor analysis, is proposed to mitigate the aforementioned challenge. Then the virtual sensing model is constructed by incorporating the fused vibration features and offline oil debris measurements based on support vector regression. The developed virtual sensing technique is experimentally evaluated in spiral bevel gear wear tests, and the results show that the developed kernel factor analysis method outperforms the state-of-the-art feature selection techniques in terms of virtual sensing model accuracy.

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Virtual sensors for estimation of energy consumption and thermal comfort in buildings with underfloor heating

Cited by 60 publications

References 24 publications

Sensors, models and platform for ambient control

Sensors, models and platform for ambient control

Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing

Virtual sensing for gearbox condition monitoring based on kernel factor analysis

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