Nonlinear Sensor Research Articles

A sparse piecewise calibration method for potentiometer with inherent nonlinearity: A comparison study

• Present a Sparse Piecewise Calibration Method (SPCM) for sensor nonlinearity calibration. • Calibration performance of the SPCM is compared with the PCM and BCM. • SPCM is demonstrated as high accuracy and rapid. The primary motivation of this paper is to present an accurate and rapid calibration method for sensor nonlinearity calibration. A Sparse Piecewise Calibration Method (SPCM) as well as its parameter optimization model has been proposed to achieve above goals. A comparison study is performed to quantitatively evaluate the calibration performance of the presented SPCM and existing methods, including Polynomial Calibration Method (PCM) and Bezier Calibrating Method (BCM). Experimental validation results reveal that the SPCM has the fastest execution speed among the above methods. Compared with the PCM and BCM, the computing cost of the SPCM could be reduced by 86.7% and 88.2%, respectively. The state-based SPCM could further enhance the calibration accuracy of the SPCM by 26.8% in the present study.

A Novel Bidirectional Gated Recurrent Unit-Based Soft Sensor Modeling Framework for Quality Prediction in Manufacturing Processes

Quality prediction is very important for improving the accuracy of quality control and the stability of product quality in manufacturing processes. However, the complex time series with high dimension, nonlinearity, and dynamics brings great challenges to the traditional quality prediction methods. To address this issue, a new soft sensor modeling framework is proposed for quality prediction. Specifically, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -nearest neighbor mutual information is first used to mine the inherent relationship between process and quality variables for dimension reduction and variable selection. Then, a bidirectional gated recurrent unit structure is designed for dynamic, nonlinear soft sensor modeling, where the historical and future information inside industrial time series and relevant features have been fully used for quality prediction, and the backpropagation learning algorithm is given in detail. Finally, a typical manufacturing process, the hot rolling process, is used for verification, and the simulation and comparison results show that the new method is able to predict the final product quality with higher accuracy and efficiency.

Similarity-based probabilistic remaining useful life estimation for an aeroengine under variable operational conditions

System-level remaining useful life (RUL) estimation is difficult due to multiple degrading components, external disturbances, and variable operational conditions. A similarity-based approach does not rely on health assessment and is more suitable for system-level RUL estimation. However, for practical applications, how to capture effective degradation features from raw data, how to fuse multiple nonlinear sensor data, and how to handle multiple source uncertainties need to be considered. To solve the above challenges, this study focuses on RUL estimation for systems under variable operational conditions. A similarity-based probabilistic RUL estimation strategy is proposed and verified using the NASA aeroengine dataset. First, measurement uncertainty can be addressed. Proper degradation features are extracted by three defined indicators. Subsequently, multiple nonlinear sensor data fusion and unsupervised synthesized health index construction can be realized using the proposed deep autoencoder-based polynomial regression approach. Finally, this strategy can handle the modeling and prediction uncertainties, including providing probabilistic RUL estimation results by well-trained residual-based similarity models. The verification results indicate the effectiveness and feasibility of the proposed strategy.

ADP-Based Remote Secure Control for Networked Control Systems Under Unknown Nonlinear Attacks in Sensors and Actuators

In this article, we concentrate on a class of networked control systems (NCSs) under the unknown nonlinear attacks, which target the sensors and actuators with remote communication channels. To attenuate the attacks remotely, the remote secure control scheme based on the adaptive dynamic programming (ADP) is proposed. First, the dynamic process of NCSs with nonlinear sensors and actuators attacks is analyzed and an adaptive remote estimator is designed to obtain the state information of the NCSs from a long distance, which is the key to achieve the purpose of remote control. Moreover, to compensate for the unknown nonlinear attacks, the adaptive law of the remote estimator is presented based on recursive least square. Furthermore, we consider the influence of the estimation errors, the minimax control theory is applied to improve the design of the policy iteration algorithm, and the ADP-based remote secure control strategy is proposed to rely on the remote estimation information. Then, the neural networks are built to implement the online algorithm. Finally, results’ analysis illustrates on two networked power systems the effectiveness of the proposed method.

Subband Decomposition Based Output-Only Modal Analysis

AbstractOutput-only modal analysis (OMA) is an indispensable alternative to experimental modal analysis for engineering structures while in operation. Conventional OMA often fails to identify the underlying modal structure with insufficient modal energy contribution. Such low modal participation is expected when the sampled response is subjected to sensor nonlinearity or when specific modes are not directly excited. A novel subband decomposition (SBD) method that resolves modal parameters even with biased modal energy distribution is proposed. It isolates the system response within a narrow frequency subband through a finite impulse response analysis filter bank. Whenever the filter subband captures a resonance, the filtered system response is close-to-singular and contains mainly the resonant mode contribution. A modal cluster metric is defined to identify the resonant normal modes automatically. The modal parameters are also identified and extracted within the subband possessing the locally maximal clustering measure. The proposed method assumes no a priori knowledge of the structure under operation other than the system should have any repeated natural frequencies. Therefore, the SBD algorithm is entirely data-driven and requires minimal user intervention. To illustrate the concept and the accuracy of the proposed SBD, numerical experiments of a linear cantilevered beam with various stationary and non-stationary loading are conducted and compared to other OMA methods. Furthermore, physical experiments on an aluminum cantilever beam examine the method’s applicability in field modal testing. Compared to traditional OMA methods, the numerical and physical experiments show orders of magnitude improvement in modal identification error using the proposed SBD.

ANALYSIS OF PROCESSES AND OPTIMIZATION OF POWER FREQUENCY OF ELECTROMAGNETIC SUPPLY SENSOR OF FUEL INJECTION

Information about the parameters of the fuel supply is necessary to assess the operational and environmental performance and condition of the diesel engine of the car and tractor. To obtain such information, a special sensor is required, which is highly sensitive and is not built into the power system. The paper provides an analytical description of the processes in the overhead electromagnetic diesel fuel injection sensor. According to the selected electromagnetic circuit, an equivalent circuit is presented. Reasonable assumptions are made for the converter under consideration. Analytical dependences of electrical and magnetic parameters of a non-linear sensor circuit are presented. The conditions for obtaining the maximum sensitivity of the sensor are determined. An algorithm for calculating the magnetic circuit is presented. For the selected design parameters and operating current, the optimal frequency and supply voltage were determined.

High-speed nonlinear focus-induced photoresponse in amorphous silicon photodetectors for ultrasensitive 3D imaging applications

A large and growing number of applications benefit from simple, fast and highly sensitive 3D imaging sensors. The Focus-Induced Photoresponse (FIP) can achieve 3D sensing functionalities by simply evaluating the irradiance dependent nonlinear sensor response in defect-based materials. Since this advantage is intricately associated to a slow response, the electrical bandwidth of present FIP detectors is limited to a few {text{kHz}} only. The devices presented in this work enable modulation frequencies of 700 kHz and beat frequency detection up to at least 3.8 MHz, surpassing the bandwidth of reported device architectures by more than two orders of magnitude. The sensors achieve a SNR of at least sim 53;{text{dB}} at 115;{text{cm}} and a DC FIP detection limit of 0.6 µW/mm2. The mature and scalable low-temperature a-Si:H process technology allows operating the device under ambient air conditions waiving additional back-end passivation, geometrical fill factors of 100% and tailoring the FIP towards adjustable 3D sensing applications.

Urethane-Foam-Embedded Silicon Pressure Sensors including Stress-Concentration Packaging Structure for Driver Posture Monitoring.

We propose urethane-foam-embedded silicon pressure sensors, including a stress-concentration packaging structure, for integration into a car seat to monitor the driver’s cognitive state, posture, and driving behavior. The technical challenges of embedding silicon pressure sensors in urethane foam are low sensitivity due to stress dispersion of the urethane foam and non-linear sensor response caused by the non-uniform deformation of the foam. Thus, the proposed package structure includes a cover to concentrate the force applied over the urethane foam and frame to eliminate this non-linear stress because the outer edge of the cover receives large non-linear stress concentration caused by the geometric non-linearity of the uneven height of the sensor package and ground substrate. With this package structure, the pressure sensitivity of the sensors ranges from 0 to 10 kPa. The sensors also have high linearity with a root mean squared error of 0.049 N in the linear regression of the relationship between applied pressure and sensor output, and the optimal frame width is more than 2 mm. Finally, a prototype 3 × 3 sensor array included in the proposed package structure detects body movements, which will enable the development of sensor-integrated car seats.

Zonotopic fault detection for discrete-time systems with limited communication capability and sensor nonlinearity

In this paper, the fault detection filter (FDF) design problem based on a dynamic event-triggered mechanism (DETM) is investigated for discrete-time systems with signal quantization and sensor nonlinearity. In order to conserve the limited network resources, a newly event-triggered mechanism with dynamic threshold is adopted to reduce the number of transmitted data through network more effectively. With the consideration of DETM, signal quantization and sensor nonlinearity, a fault detection filter is constructed to achieve the robustly asymptotic stability of established model with expected fault detection objective. In addition, by influence of DETM, external interference and quantization errors , a zonotopic residual evaluation mechanism is constructed to detect the occurring fault of plant. Finally, a practical example is provided to illustrate the effectiveness of proposed design approach.

A Fast-Response Breathing Monitoring System for Human Respiration Disease Detection

This paper presents a sensing system for the real-time monitoring of human respiration. The system is equipped with a fast response thermoresistive micro calorimetric flow (TMCF) sensor and a dedicated data processing algorithm. The TMCF sensor is designed with a proposed nonlinear sensor model and fabricated in a CMOS compatible process, which obtains a high sensitivity of 114 mV/SLM and a fast response time of less than 6 ms. By using this high-performance micro flow sensor and its proprietary data processing algorithm, critical human respiration information including respiratory rate (RR) and minute ventilation (MV) can be easily obtained. The proposed sensing system achieves a very small mean absolute error (MAE) of less than 2.7 mHz for RR, and the extracted MV is also in good agreement with the commonly reported value of 4 - 6 L/min. In addition, benefiting from the very short time constant of the developed TMCF sensor, the proposed sensing system can successfully distinguish different respiratory diseases, such as apnea, hypopnea, polypnea, etc. Therefore, this proposed human respiration monitoring system will be a promising sensing technology for respiration diagnosis in medical applications.

Quantized Fault-Tolerant Control for Descriptor Systems with Intermittent Actuator Faults, Randomly Occurring Sensor Non-Linearity, and Missing Data

This paper examines the fault-tolerant control problem for discrete-time descriptor systems that are susceptible to intermittent actuator failures, nonlinear sensor data, and probability-based missing data. The discrete-time non-homogeneous Markov chain was adopted to describe the stochastic behavior of actuator faults. Moreover, Bernoulli-distributed stochastic variables with known conditional probabilities were employed to describe the practical features of random sensor non-linearity and missing data. In this study, the output signals were quantized and a dynamic output feedback controller was synthesized such that the closed-loop system was stochastically admissible and satisfied the strictly (Q,S,R)-γ-dissipative performance index. The theoretical developments are illustrated through numerical simulations of an infinite machine bus.

Linear and Non-Linear Soft Sensors for Predicting the Research Octane Number (RON) through Integrated Synchronization, Resolution Selection and Modelling.

The Research Octane Number (RON) is a key quality parameter for gasoline, obtained offline through complex, time-consuming, and expensive standard methods. Measurements are usually only available a few times per week and after long delays, making process control very challenging. Therefore, alternative methods have been proposed to predict RON from readily available data. In this work, we report the development of inferential models for predicting RON from process data collected in a real catalytic reforming process. Data resolution and synchronization were explicitly considered during the modelling stage, where 20 predictive linear and non-linear machine learning models were assessed and compared using a robust Monte Carlo double cross-validation approach. The workflow also handles outliers, missing data, multirate and multiresolution observations, and processes dynamics, among other features. Low RMSE were obtained under testing conditions (close to 0.5), with the best methods belonging to the class of penalized regression methods and partial least squares. The developed models allow for improved management of the operational conditions necessary to achieve the target RON, including a more effective use of the heating utilities, which improves process efficiency while reducing costs and emissions.

Electromagnetic wave-based extreme deep learning with nonlinear time-Floquet entanglement

Wave-based analog signal processing holds the promise of extremely fast, on-the-fly, power-efficient data processing, occurring as a wave propagates through an artificially engineered medium. Yet, due to the fundamentally weak non-linearities of traditional electromagnetic materials, such analog processors have been so far largely confined to simple linear projections such as image edge detection or matrix multiplications. Complex neuromorphic computing tasks, which inherently require strong non-linearities, have so far remained out-of-reach of wave-based solutions, with a few attempts that implemented non-linearities on the digital front, or used weak and inflexible non-linear sensors, restraining the learning performance. Here, we tackle this issue by demonstrating the relevance of time-Floquet physics to induce a strong non-linear entanglement between signal inputs at different frequencies, enabling a power-efficient and versatile wave platform for analog extreme deep learning involving a single, uniformly modulated dielectric layer and a scattering medium. We prove the efficiency of the method for extreme learning machines and reservoir computing to solve a range of challenging learning tasks, from forecasting chaotic time series to the simultaneous classification of distinct datasets. Our results open the way for optical wave-based machine learning with high energy efficiency, speed and scalability.

Adaptive event‐triggered H∞ state estimation of semi‐Markovian jump neural networks with randomly occurred sensor nonlinearity

AbstractThis article mainly discusses the problem for adaptive event‐triggered H state estimation of semi‐Markovian jump neural networks (s‐MJNNs) subject to random sensor nonlinearity. To reduce the communication load, adaptive event‐triggered scheme (AETS) is introduced to decide whether to transmit sampled data or not. Also, considering the possible sensor nonlinearity, a new estimation error model is established under the framework of AETS. An appropriate Lyapunov–Krasovskii functional (LKF) containing the proposed adaptive event trigger condition is constructed, and sufficient conditions are obtained to guarantee the asymptotic stability of the estimation error system. Then, through a set of feasible linear matrix inequalities (LMIs), the co‐design method of estimator and AETS is proposed. Finally, the feasibility of this paper is proved by three numerical examples.

Nonlinear Dynamic Soft Sensor Development with a Supervised Hybrid CNN-LSTM Network for Industrial Processes.

A soft sensor is a key component when a real-time measurement is unavailable for industrial processes. Recently, soft sensor models based on deep-learning techniques have been successfully applied to complex industrial processes with nonlinear and dynamic characteristics. However, the conventional deep-learning-based methods cannot guarantee that the quality-relevant features are included in the hidden states when the modeling samples are limited. To address this issue, a supervised hybrid network based on a dynamic convolutional neural network (CNN) and a long short-term memory (LSTM) network is designed by constructing multilayer dynamic CNN-LSTM with improved structures. In each time instant, data augmentation is implemented by dynamic expansion of the original samples. Moreover, multiple supervised hidden units are trained by adding quality variables as part of the layer input to acquire a better quality-related feature learning performance. The effectiveness of the proposed soft senor development is validated through two industrial applications, including a penicillin fermentation process and a debutanizer column.

Closing the Control Loop with Time-Variant Embedded Soft Sensors and Recurrent Neural Networks.

Embedded soft sensors can significantly impact the design and control of soft-bodied robots. Although there have been considerable advances in technology behind these novel sensing materials, their application in real-world tasks, especially in closed-loop control tasks, has been severely limited. This is mainly because of the challenge involved with modeling a nonlinear time-variant sensor embedded in a complex soft-bodied system. This article presents a learning-based approach for closed-loop force control with embedded soft sensors and recurrent neural networks (RNNs). We present learning protocols for training a class of RNNs called long short-term memory (LSTM) that allows us to develop accurate and robust state estimation models of these complex dynamical systems within a short period of time. Using this model, we develop a simple feedback force controller for a soft anthropomorphic finger even with significant drift and hysteresis in our feedback signal. Simulation and experimental studies are conducted to analyze the capabilities and generalizability of the control architecture. Experimentally, we are able to develop a closed-loop controller with a control frequency of 25 Hz and an average accuracy of 0.17 N. Our results indicate that current soft sensing technologies can already be used in real-world applications with the aid of machine learning techniques and an appropriate training methodology.

Phase Demodulation of Rotor Torsional Vibration Measurement under Time-Varying Speed

When torsional vibration is measured with the zebra-tape method, the modulation signals can be contaminated by electrical noise, environmental noise, step noise, sensor nonlinearities, and so on. Although the amplitudes of these types of noise are very weak relative to the amplitude of the ideal pulse test signal, they may introduce large-scale random broad-spectrum noise to the signal phases. Such noise is difficult to remove using frequency domain, time-frequency-domain, or threshold sampling methods and seriously affects the accuracy of torsional vibration measurements. This paper presents a phase demodulation algorithm based on downsampling and local resampling (DSLR) to improve the accuracy of torsional vibration measurements. To verify the proposed DSLR algorithm, torsional vibration simulations and experiments are conducted under a time-varying rotation speed. The results show that the angular displacement signal obtained from the torsional vibration test using zebra tapes is the superposition of the dynamic and static angular displacements, with the latter generated because of the varying rotation speed with respect to time. The DSLR algorithm effectively reduces the phase noise and demodulates the pulse signal phases. The work in this paper provides a method for obtaining reference torsional vibration measurements of rotors based on the zebra-tape method under time-varying speeds.

Nonlinear Optical and Ion Sensor Properties of Novel Molecules Conjugated by Click Chemistry

The molecular structure, luminescence behavior, and electronic energy level of an organic optoelectronic materials are important parameters for its synthesis. The electro-optical properties can be changed by modifying the structure of the molecule to make the electronic energy level adjustable. In this article, a series of organic conjugated micro-molecules are successfully synthesized by linking small compound units. This metal-free [2 + 2] click chemistry process generates donor–acceptor chromophore substances with high yield, high solubility, and adjustable energy levels, which can be widely used for sensors and nonlinear optics in different fields. A-TCNE, A-TCNQ, and A-F4-TCNQ molecules are characterized comprehensively via UV-Vis-NIR spectra, 1H NMR spectra, infrared spectroscopy, and mass spectrometry. The unique nonlinear optical phenomena and powerful intra-molecular charge–transfer interactions of these new materials give them fascinating potential for application as optoelectronic materials.

On the Question of the Intrinsic Nonlinearity of the Induction Sensor

On the Question of the Intrinsic Nonlinearity of the Induction Sensor

Design of active fault-tolerant control system for Air-fuel ratio control of internal combustion engines using fuzzy logic controller.

Fault-Tolerant Control Systems (FTCS) are used in critical and safety applications to improve performance and stability despite failure modes. As a result, costly production losses related to unusual and unplanned shutdowns can be prevented by incorporating these systems in the critical process plant machines. The Internal Combustion (IC) engines are highly used process plant machines and faults in their sensors will cause their shutdown instigating the need to install FTCS in them. In this paper, an Active Fault-Tolerant Control System (AFTCS) based on a Fuzzy Logic Controller (FLC) is suggested to improve the reliability of the Air-Fuel Ratio (AFR) control system of an IC engine. For analytical redundancy, a nonlinear Fuzzy Logic (FL) based observer is implemented in the proposed system for the Fault Detection and Isolation (FDI) unit for nonlinear sensors of the AFR system. Lyapunov stability analysis was used for designing a stable system in both faulty and normal conditions. To evaluate its performance, this system was developed in the MATLAB/Simulink platform. The simulation results show that the developed system is robust under sensor fault conditions, retaining stability with a minimum decrease of AFR. This study's comparison with the existing literature demonstrates that the proposed system is effective for maintaining the AFR in IC engines during sensor faulty conditions thus reducing shutdown of engine and production loss for increased profits.