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Soft Sensors
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Soft Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 28167

Special Issue Editor

Prof. Dr. János Abonyi

E-Mail Website
Guest Editor
MTA-PE Lendület Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, H-8200 Veszprém, Hungary
Interests: chemical engineering; complex systems; computational intelligence; network science; process engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soft sensors estimate unmeasured variables using computational models. The resulted inferential measurements can be used in fault diagnosis, control and real-time optimization applications and for the validation of online analyzers.

This Special Issue solicits papers that cover the development, validation, application, and maintenance of software sensors. The potential topics include, but are not limited to:

  • Data-driven modeling for soft sensor development (from classical systems identification and multivariate chemometric techniques to deep learning)
  • Semi-mechanistic and first-principle models in soft sensor development (including grey box models)
  • Validation and maintenance of soft-sensors
  • Control-oriented soft sensor development (g., inferential control, senseless control)
  • Applications in fault detection and diagnosis and monitoring of complex processes
  • Applications in state estimation, control, and optimization (g., sensorless motor control, nonlinear model predictive control)
  • Special applications in process analytical technology (PAT), manufacturing, chemical-, bio-, pharmaceutical-, oil-, and process engineering

Prof. Dr. Janos Abonyi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Systems identification and state estimation
  • Multivariate chemometric techniques
  • Machine learning
  • Sensor validation, condition monitoring, and maintenance
  • Fault detection and diagnosis
  • State estimation, control, and optimization
  • Process analytical technology (PAT)

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Published Papers (5 papers)

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Research

19 pages, 3488 KiB  
Article
An Improved Normalized Mutual Information Variable Selection Algorithm for Neural Network-Based Soft Sensors
by Kai Sun, Pengxin Tian, Huanning Qi, Fengying Ma and Genke Yang
Sensors 2019, 19(24), 5368; https://doi.org/10.3390/s19245368 - 5 Dec 2019
Cited by 10 | Viewed by 3593
Abstract
In this paper, normalized mutual information feature selection (NMIFS) and tabu search (TS) are integrated to develop a new variable selection algorithm for soft sensors. NMIFS is applied to select influential variables contributing to the output variable and avoids selecting redundant variables by [...] Read more.
In this paper, normalized mutual information feature selection (NMIFS) and tabu search (TS) are integrated to develop a new variable selection algorithm for soft sensors. NMIFS is applied to select influential variables contributing to the output variable and avoids selecting redundant variables by calculating mutual information (MI). A TS based strategy is designed to prevent NMIFS from falling into a local optimal solution. The proposed algorithm performs the variable selection by combining the entropy information and MI and validating error information of artificial neural networks (ANNs); therefore, it has advantages over previous MI-based variable selection algorithms. Several simulation datasets with different scales, correlations and noise parameters are implemented to demonstrate the performance of the proposed algorithm. A set of actual production data from a power plant is also used to check the performance of these algorithms. The experiments showed that the developed variable selection algorithm presents better model accuracy with fewer selected variables, compared with other state-of-the-art methods. The application of this algorithm to soft sensors can achieve reliable results. Full article
(This article belongs to the Special Issue Soft Sensors)
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11 pages, 1349 KiB  
Article
Soft Sensing of Silicon Content via Bagging Local Semi-Supervised Models
by Xing He, Jun Ji, Kaixin Liu, Zengliang Gao and Yi Liu
Sensors 2019, 19(17), 3814; https://doi.org/10.3390/s19173814 - 3 Sep 2019
Cited by 18 | Viewed by 3417
Abstract
The silicon content in industrial blast furnaces is difficult to measure directly online. Traditional soft sensors do not efficiently utilize useful information hidden in process variables. In this work, bagging local semi-supervised models (BLSM) for online silicon content prediction are proposed. They integrate [...] Read more.
The silicon content in industrial blast furnaces is difficult to measure directly online. Traditional soft sensors do not efficiently utilize useful information hidden in process variables. In this work, bagging local semi-supervised models (BLSM) for online silicon content prediction are proposed. They integrate the bagging strategy, the just-in-time-learning manner, and the semi-supervised extreme learning machine into a unified soft sensing framework. With the online semi-supervised learning method, the valuable information hidden in unlabeled data can be explored and absorbed into the prediction model. The application results to an industrial blast furnace show that BLSM has better prediction performance compared with other supervised soft sensors. Full article
(This article belongs to the Special Issue Soft Sensors)
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25 pages, 9173 KiB  
Article
Capacitive Bio-Inspired Flow Sensing Cupula
by James P. Wissman, Kaushik Sampath, Simon E. Freeman and Charles A. Rohde
Sensors 2019, 19(11), 2639; https://doi.org/10.3390/s19112639 - 11 Jun 2019
Cited by 33 | Viewed by 8209
Abstract
Submersible robotics have improved in efficiency and versatility by incorporating features found in aquatic life, ranging from thunniform kinematics to shark skin textures. To fully realize these benefits, sensor systems must be incorporated to aid in object detection and navigation through complex flows. [...] Read more.
Submersible robotics have improved in efficiency and versatility by incorporating features found in aquatic life, ranging from thunniform kinematics to shark skin textures. To fully realize these benefits, sensor systems must be incorporated to aid in object detection and navigation through complex flows. Again, inspiration can be taken from biology, drawing on the lateral line sensor systems and neuromast structures found on fish. To maintain a truly soft-bodied robot, a man-made flow sensor must be developed that is entirely complaint, introducing no rigidity to the artificial “skin.” We present a capacitive cupula inspired by superficial neuromasts. Fabricated via lost wax methods and vacuum injection, our 5 mm tall device exhibits a sensitivity of 0.5 pF/mm (capacitance versus tip deflection) and consists of room temperature liquid metal plates embedded in a soft silicone body. In contrast to existing capacitive examples, our sensor incorporates the transducers into the cupula itself rather than at its base. We present a kinematic theory and energy-based approach to approximate capacitance versus flow, resulting in equations that are verified with a combination of experiments and COMSOL simulations. Full article
(This article belongs to the Special Issue Soft Sensors)
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21 pages, 1253 KiB  
Article
Transfer Learning Based Fault Diagnosis with Missing Data Due to Multi-Rate Sampling
by Danmin Chen, Shuai Yang and Funa Zhou
Sensors 2019, 19(8), 1826; https://doi.org/10.3390/s19081826 - 17 Apr 2019
Cited by 44 | Viewed by 5403
Abstract
Deep learning is an effective feature extraction method widely applied in fault diagnosis fields since it can extract fault features potentially involved in multi-sensor data. But different sensors equipped in the system may sample data at different sampling rates, which will inevitably result [...] Read more.
Deep learning is an effective feature extraction method widely applied in fault diagnosis fields since it can extract fault features potentially involved in multi-sensor data. But different sensors equipped in the system may sample data at different sampling rates, which will inevitably result in a problem that a very small number of samples with a complete structure can be used for deep learning since the input of a deep neural network (DNN) is required to be a structurally complete sample. On the other hand, a large number of samples are required to ensure the efficiency of deep learning based fault diagnosis methods. To solve the problem that a structurally complete sample size is too small, this paper proposes a fault diagnosis framework of missing data based on transfer learning which makes full use of a large number of structurally incomplete samples. By designing suitable transfer learning mechanisms, extra useful fault features can be extracted to improve the accuracy of fault diagnosis based simply on structural complete samples. Thus, online fault diagnosis, as well as an offline learning scheme based on deep learning of multi-rate sampling data, can be developed. The efficiency of the proposed method is demonstrated by utilizing data collected from the QPZZ- II rotating machinery vibration experimental platform system. Full article
(This article belongs to the Special Issue Soft Sensors)
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16 pages, 6619 KiB  
Article
Artificial Neural Network Application for Current Sensors Fault Detection in the Vector Controlled Induction Motor Drive
by Mateusz Dybkowski and Kamil Klimkowski
Sensors 2019, 19(3), 571; https://doi.org/10.3390/s19030571 - 29 Jan 2019
Cited by 47 | Viewed by 5870
Abstract
This paper describes a Fault Tolerant Control structure for the Induction Motor (IM) drive. We analyzed the influence of current sensor faults on the properties of the vector-controlled IM drive system. As a control algorithm, the Direct Field Oriented Control structure was chosen. [...] Read more.
This paper describes a Fault Tolerant Control structure for the Induction Motor (IM) drive. We analyzed the influence of current sensor faults on the properties of the vector-controlled IM drive system. As a control algorithm, the Direct Field Oriented Control structure was chosen. For the proper operation of this system and for other vector algorithms, information about the stator currents components is required. It is important to monitor and detect these sensor faults, especially in drives with an increased safety level. We discuss the possibility of the neural network application in detecting stator current sensor faults in the vector control algorithm. Simulation and experimental results for various drive conditions are presented. Full article
(This article belongs to the Special Issue Soft Sensors)
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