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Early Detection Techniques for Sensor Aging/Biasing/Degrading/Faulty Issues

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

Deadline for manuscript submissions: closed (25 May 2024) | Viewed by 8553

Special Issue Editors


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Guest Editor
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710061, China
Interests: mechanic/electrical system dynamics and control; fundamentals of fractional calculus application; vibration energy harvesting
School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
Interests: fault diagnosis of machinery; degradation modeling; remaining useful life prediction; intelligent maintenance
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Special Issue Information

Dear Colleagues,

In recent years, digital twins and edge computing have enabled the application of overall control systems and made them much smarter than ever before. This is also referred to as “digital twin enabled smart control engineering (SCE)” (e.g., [1]). However, at the component level, making the sensors “cognitive” and “reflective” is one of the major challenges [1]. Some essential techniques are needed to make the sensors much smarter, which mostly involve running online using real-time analytics based on advanced signal processing techniques. This Special Issue makes an attempt to detail such new techniques toward the early detection of sensor aging/biasing/degrading/faulty issues, such that we can claim “smart sensors” within the emerging SCE framework [1]. Note that the detection task can perform at a slower sampling rate than the main closed-loop, serving as health monitoring or prognostics, which makes it possible to carry out the detection task in edge-computing devices. With this early detection task in mind and an SCE notion, we seek contributions that can use any technique or a combination of existing ones, such as:

  • Model-based methods;
  • Rule-based methods;
  • Deep-learning-based methods;
  • Tiny-machine-learning-based methods;
  • Wavelet or other time-frequency representations;
  • Dynamical mode decomposition (DMD);
  • Empirical mode decomposition (EMD);
  • Entropy, mutual information, and information metrics (AIC, BIC, etc.);
  • Fractional order signal processing (FOSP) techniques;
  • ETC

[1] https://doi.org/10.1109/IAI50351.2020.9262203

Prof. Dr. YangQuan Chen
Prof. Dr. Junyi Cao
Dr. Naipeng Li
Guest Editors

Manuscript Submission Information

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Published Papers (4 papers)

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Research

18 pages, 2515 KiB  
Article
Detection of Degraded Star Observation Using Singular Values for Improved Attitude Determination
by Kiduck Kim
Sensors 2024, 24(2), 593; https://doi.org/10.3390/s24020593 - 17 Jan 2024
Viewed by 535
Abstract
This study introduces an innovative approach aimed at enhancing the accuracy of attitude determination through the computation of star observation quality. The proposed algorithm stems from the inherent invariance of singular values under attitude transformations, leveraging the concept of assessing error magnitude through [...] Read more.
This study introduces an innovative approach aimed at enhancing the accuracy of attitude determination through the computation of star observation quality. The proposed algorithm stems from the inherent invariance of singular values under attitude transformations, leveraging the concept of assessing error magnitude through the deviation of singular values. Quantization becomes imperative to employ this error magnitude as a weighting factor within the attitude determination process. To fulfill this purpose, this study applies p-value hypothesis testing to calculate quantized error levels. Simulation results validate that the calculated weights derived from the proposed algorithm lead to a discernible enhancement in attitude determination performance. Full article
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15 pages, 3401 KiB  
Article
Parameter Sensitivity Analysis of Mounting Pedestals and Multi-Objective Optimization for a Multi-Support Rigid Body System
by Qingyu Zhu, Qingkai Han, Xiaodong Yang and Junzhe Lin
Sensors 2022, 22(18), 7067; https://doi.org/10.3390/s22187067 - 19 Sep 2022
Viewed by 1265
Abstract
In this paper, a parameter sensitivity analysis of mounting pedestals and a multi-objective optimization design for vibration reduction in a multi-support rigid body system, taking an aeroengine-lubricating oil tank supported by multiple mounting pedestals as an example, are conducted based on the third [...] Read more.
In this paper, a parameter sensitivity analysis of mounting pedestals and a multi-objective optimization design for vibration reduction in a multi-support rigid body system, taking an aeroengine-lubricating oil tank supported by multiple mounting pedestals as an example, are conducted based on the third version of non-dominated sorting genetic algorithm (NSGA-Ⅲ) combined with Sobol’s sensitivity analysis method (SSAM). An aeroengine-lubricating oil tank with three mounting pedestals is simplified as a three-support dynamic system, and its dynamics model is established. Several structural parameters of mounting pedestals are taken as the design variables, and the system vibration response and the reaction force of the front and rear mounting pedestals are considered as the objective functions. The first-order results and total sensitivity index of different design parameters for each objective function are obtained via SSAM, and the five most sensitive parameters are selected. Based on the above five design parameters, multi-objective optimization designing for vibration reduction in a simplified lubricating oil tank system is conducted based on NSGA-Ⅲ, and the results of the above triple-objective optimization are obtained as a Pareto-front surface with an obvious frontier. It can be observed from the simulation results that the oil tank vibration of the optimized system is effectively suppressed under the unbalanced excitation of two typical engine speeds. The established method and the main results can provide guidance for designers of aeroengine external structural systems, which can help to achieve superior system dynamic performances in engineering applications. Full article
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16 pages, 2614 KiB  
Article
Sensor Fault Diagnostics Using Physics-Informed Transfer Learning Framework
by Furkan Guc and Yangquan Chen
Sensors 2022, 22(8), 2913; https://doi.org/10.3390/s22082913 - 11 Apr 2022
Cited by 4 | Viewed by 2368
Abstract
The field of smart health monitoring, intelligent fault detection and diagnosis is expanding dramatically in order to maintain successful operation in many engineering applications. Considering possible fault scenarios that can occur in a system, indicating the type of fault in a sensor is [...] Read more.
The field of smart health monitoring, intelligent fault detection and diagnosis is expanding dramatically in order to maintain successful operation in many engineering applications. Considering possible fault scenarios that can occur in a system, indicating the type of fault in a sensor is one of the most important and challenging problems in the area of intelligent sensor fault diagnostics. Within this frame of reference, we extended the physics-informed transfer learning framework, first presented previously for a fault cause assignment, to the level of sensor fault diagnostics for a range of different fault scenarios. Hence, the framework is utilized to perform intelligent sensor fault diagnostics for the first time. The underlying dynamics of the reference system are extracted using a completely data-driven methodology and dynamic mode decomposition with control (DMDc) in order to generate time-frequency illustrations of each sample with continuous wavelet transform (CWT). Then, sensor fault diagnostics for bias, drift over time, sine disturbance and increased noise sensor fault scenarios are achieved using the idea of transfer learning with a pre-trained image classification algorithm. The classification results yields a good performance on sensor fault diagnostics with 91.5% training and 84.7% test accuracy along with a fair robustness level with a set of reference benchmark system parameters. Full article
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19 pages, 4595 KiB  
Article
Error Fusion of Hybrid Neural Networks for Mechanical Condition Dynamic Prediction
by Wentao Zhang, Yucheng Liu, Shaohui Zhang, Tuzhi Long and Jinglun Liang
Sensors 2021, 21(12), 4043; https://doi.org/10.3390/s21124043 - 11 Jun 2021
Cited by 5 | Viewed by 2250
Abstract
It is important for equipment to operate safely and reliably so that the working state of mechanical parts pushes forward an immense influence. Therefore, in order to enhance the dependability and security of mechanical equipment, to accurately predict the changing trend of mechanical [...] Read more.
It is important for equipment to operate safely and reliably so that the working state of mechanical parts pushes forward an immense influence. Therefore, in order to enhance the dependability and security of mechanical equipment, to accurately predict the changing trend of mechanical components in advance plays a significant role. This paper introduces a novel condition prediction method, named error fusion of hybrid neural networks (EFHNN), by combining the error fusion of multiple sparse auto-encoders with convolutional neural networks for predicting the mechanical condition. First, to improve prediction accuracy, we can use the error fusion of multiple sparse auto-encoders to collect multi-feature information, and obtain a trend curve representing machine condition as well as a threshold line that can indicate the beginning of mechanical failure by computing the square prediction error (SPE). Then, convolutional neural networks predict the state of the machine according to the original data when the SPE value exceeds the threshold line. It can be seen from this result that the EFHNN method in the prediction of mechanical fault time series is available and superior. Full article
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