Input variables selection criteria for data-driven Soft Sensors design

Xibilia, Maria Gabriella; Gemelli, Nathaniel; Consolo, G.

doi:10.1109/icnsc.2017.8000119

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…Classification of references of application of the methods on a real case study. [185] LASSO [49,162] Hybrid [29,49,83,[193][194][195][196][197]…”

Section: Discussionmentioning

confidence: 99%

“…In order to evaluate each subset of variables (or to evaluate the importance of the variable or variables excluded), each Lipschitz's quotient computed for that subset is compared with the one computed for the whole candidate set. However, this approach requires the computation of the quotient for all the possible combinations of the input variables, resulting in a high computational demand [162].…”

Section: Filter Methodsmentioning

confidence: 99%

“…Elastic Net (EN) [191] linearly combines the L 1 and L 2 penalties from LASSO and RR and can be optimized to effectively perform coefficient shrinkage as well as setting some of them to 0 for sparse variable selection. LASSO regularization for inputs selection is extended to the nonlinear case as well with the name of LASSO-MLP [162,192]. In this case, the L 1 penalty term is added to the error function of a single-layer MLP and then variable selection is performed as in linear LASSO.…”

Section: Embedded Methodsmentioning

confidence: 99%

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Input Selection Methods for Soft Sensor Design: A Survey

2020

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Soft Sensors (SSs) are inferential models used in many industrial fields. They allow for real-time estimation of hard-to-measure variables as a function of available data obtained from online sensors. SSs are generally built using industries historical databases through data-driven approaches. A critical issue in SS design concerns the selection of input variables, among those available in a candidate dataset. In the case of industrial processes, candidate inputs can reach great numbers, making the design computationally demanding and leading to poorly performing models. An input selection procedure is then necessary. Most used input selection approaches for SS design are addressed in this work and classified with their benefits and drawbacks to guide the designer through this step.

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“…Classification of references of application of the methods on a real case study. [185] LASSO [49,162] Hybrid [29,49,83,[193][194][195][196][197]…”

Section: Discussionmentioning

confidence: 99%

Section: Filter Methodsmentioning

confidence: 99%

Section: Embedded Methodsmentioning

confidence: 99%

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Input Selection Methods for Soft Sensor Design: A Survey

2020

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“…In the context of the sustainable development of industrial process control, the importance of monitoring a large set of processes variables with the help of adequate measuring instruments is clear. However, the key obstacle to implementing a large-scale monitoring and control policy is the high cost of online meters [1][2][3].…”

Section: The Rationalisation Of Using Soft Sensors In the Automated Cmentioning

confidence: 99%

Application of soft sensors in the automated process control of different industries

et al. 2020

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Sustainable development of industry is closely related with tries to automate industrial processes in all possible ways. Recent advances in automated control systems have led to decreasing the cost of hardware and energy consumption. This article describes examples of soft sensors using in various industries. The main advantages of soft sensors are low cost, flexibility and versatility. In addition, the soft sensors are environmentally friendly as they significantly reduce the amount of equipment and do not require utilization. Despite these benefits, there are some problems with using them. First problem consists in what information and how needs to be measured to use received data in calculation of another virtual data. Second problem is using proper software and the time of mathematical calculations. The goal of the soft sensors is generation of valid virtual data for the controller to increase the accuracy and quality of the automated control. The article is of interest from the point of view of possibilities to applicate modern technology in solving various tasks of automated control.

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“…An open issue is how to model and handle such kinds of uncertain information. To address this issue, a variety of theoretical methods has been exploited for multi-sensor data fusion, like the rough sets theory [ 16 , 17 ], fuzzy sets theory [ 18 , 19 , 20 , 21 , 22 ], evidence theory [ 23 , 24 , 25 ], Z numbers [ 26 , 27 ], and D numbers theory [ 28 , 29 , 30 ], evidential reasoning [ 31 , 32 , 33 , 34 ], and so on [ 35 , 36 , 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Evidence Theory and Fuzzy Preference Approach-Based Multi-Sensor Data Fusion Technique for Fault Diagnosis

Xiao

2017

Sensors

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The multi-sensor data fusion technique plays a significant role in fault diagnosis and in a variety of such applications, and the Dempster–Shafer evidence theory is employed to improve the system performance; whereas, it may generate a counter-intuitive result when the pieces of evidence highly conflict with each other. To handle this problem, a novel multi-sensor data fusion approach on the basis of the distance of evidence, belief entropy and fuzzy preference relation analysis is proposed. A function of evidence distance is first leveraged to measure the conflict degree among the pieces of evidence; thus, the support degree can be obtained to represent the reliability of the evidence. Next, the uncertainty of each piece of evidence is measured by means of the belief entropy. Based on the quantitative uncertainty measured above, the fuzzy preference relations are applied to represent the relative credibility preference of the evidence. Afterwards, the support degree of each piece of evidence is adjusted by taking advantage of the relative credibility preference of the evidence that can be utilized to generate an appropriate weight with respect to each piece of evidence. Finally, the modified weights of the evidence are adopted to adjust the bodies of the evidence in the advance of utilizing Dempster’s combination rule. A numerical example and a practical application in fault diagnosis are used as illustrations to demonstrate that the proposal is reasonable and efficient in the management of conflict and fault diagnosis.

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Input variables selection criteria for data-driven Soft Sensors design

Cited by 6 publications

References 19 publications

Input Selection Methods for Soft Sensor Design: A Survey

Input Selection Methods for Soft Sensor Design: A Survey

Application of soft sensors in the automated process control of different industries

A Novel Evidence Theory and Fuzzy Preference Approach-Based Multi-Sensor Data Fusion Technique for Fault Diagnosis

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