Sensor Performance Degradation Research Articles

Multi-Stage Wiener Process Based Remaining Useful Life Prediction for Load Cells

Abstract Nowadays, sensor technology is used in a wide range of fields, and the demand for its reliability continues to increase. Accurate estimation of the remaining useful life of sensors is critical. Since the performance degradation of sensors involves multiple stages, the single-stage Wiener degradation model is difficult to accurately characterize. To resolve this issue, a multi-stage Wiener process degradation model is established. The model utilizes the cumulative sum algorithm to identify change points among stages. Then, the performance of the multi-stage Wiener model for characterizing degradation trends is validated with load cell accelerated aging test data. Furthermore, the results of experiment demonstrate that the proposed method provides a more accurate remaining useful life estimation compared to the single-stage Wiener model, and the relative error is reduced by 18.82%.

Research on performance degradation of force sensors based on improved error back propagation algorithm

Research on performance degradation of force sensors based on improved error back propagation algorithm

Impact of proton radiation and annealing of CMOS image sensors on star sensor performance

Star sensors play a crucial role as high-precision optical attitude navigation devices in satellite attitude control systems. The CMOS image sensor is an important component of the imaging system of the star sensor and experiences performance degradation due to the total ionizing dose effect and displacement damage effect in the radiation environment of its long-term operational workspace. As a result, the detection capability and attitude positioning accuracy of the star sensor gradually deteriorate. The performance of CMOS image sensors can be partially restored after annealing but the impact on the performance of star sensors is unclear. This study conducted 100 MeV proton irradiation experiments with different fluences on CMOS image sensors and performed room temperature and high temperature annealing treatments. By testing and analyzing the performance of star sensors equipped with irradiated and different annealing treatments on CMOS image sensors, the impact mechanism of CMOS image sensor radiation damage and annealing recovery on the performance variation of star sensors was obtained. This study provides theoretical support for reliability of long-term in-orbit star sensors.

Recent advances in hydrogel-based flexible strain sensors for harsh environment applications.

Flexible strain sensors are broadly investigated in electronic skins and human-machine interaction due to their light weight, high sensitivity, and wide sensing range. Hydrogels with unique three-dimensional network structures are widely used in flexible strain sensors for their exceptional flexibility and adaptability to mechanical deformation. However, hydrogels often suffer from damage, hardening, and collapse under harsh conditions, such as extreme temperatures and humidity levels, which lead to sensor performance degradation or even failure. In addition, the failure mechanism in extreme environments remains unclear. In this review, the performance degradation and failure mechanism of hydrogel flexible strain sensors under various harsh conditions are examined. Subsequently, strategies towards the environmental tolerance of hydrogel flexible strain sensors are summarized. Finally, the current challenges of hydrogel flexible strain sensors in harsh environments are discussed, along with potential directions for future development and applications.

Design of Adaptive Sensor Coupling-Based Upper Limb 7-DOF Exoskeleton for Smooth Human Motion Tracking: ASC-EXO

The conventional sensor coupling of the sensor in an exoskeleton robot suffers from inaccurate human motion tracking due to irregular inertial loading on the sensor axis. It may cause sensor axis misalignment, performance degradation and damage the sensor permanently, especially in the multiple degrees of freedom (DOF) exoskeleton robot. In this paper, we proposed a novel design of an adaptive sensor coupling-based 7-DOF exoskeleton (ASC-EXO) for motion tracking and tele-manipulation. The adaptive sensor coupling mechanism has been designed based on the flexure structure cantilever with a slew ring, which suppresses irregular inertial loading on the sensor and is stiff enough along the sensor rotation in the z-axis and compliant on the other axes. The slew ring allows smooth joint rotation. Further, the sensor values are improved by a Butterworth filter. As a result, the adaptive sensor coupling suppressed the sensor noise 20 times more than the conventionally coupled sensor with the robot joint. Finally, we developed a prototype of the 7-DOF exoskeleton (ASC-EXO) using adaptive sensor coupling for smooth human motion tracking. The performance evaluation of adaptive sensor coupling and human motion tracking is presented. In addition, we demonstrated smooth control of the slave robot (KUKA) using the exoskeleton (ASC-EXO) as a master device.

Experiment and Analysis of Performance Degradation of CCD Image Sensors Induced by Proton Radiation with Different Energy

Experiment and Analysis of Performance Degradation of CCD Image Sensors Induced by Proton Radiation with Different Energy

A modified fusion model-based/data-driven model for sensor fault diagnosis and performance degradation estimation of aero-engine

Sensor fault diagnosis and performance degradation estimation (SFDPDE) plays a critical role in the operation and maintenance of aero-engines. In this study, a modified fusion model driven by sensor measurements is proposed to overcome the drawbacks of the single data-driven and single model-based methods. Two types of on-board models are established based on augmented state space equations, and a data-driven model based on an extreme learning machine (ELM) is constructed for residual correction of the on-board model. A bidirectional information transmission algorithm is designed in the SFDPDE framework in order to include the function coordination. The Kalman filter is employed as the optimal algorithm in the SFDPDE framework, containing a standardized sensor parameter selection process. The experimental results indicate that the proposed fusion model improves the accuracy of sensor fault diagnosis and reduces the mean square error of health parameter estimations, while the information sharing module expands the application scope of SFDPDE and improves its accuracy as well as stability.

Influence mechanism and recognition algorithm of CMOS active pixel sensor radiation damage on star sensor star map recognition

In order to analyze the causes of star sensor performance degradation and attitude measurement accuracy reduction in space radiation environment, the total ionizing dose effects on complementary metal oxide semiconductor (CMOS) star sensor performance are studied. By using an established outfield star sensor test system, the Orion Nebula and the zenith direction of the sky are imaged. Through the experimental processes of star map data acquisition, star point extraction and star map matching, the influence mechanisms of complementary metal oxide semiconductor active pixel sensor (CMOS APS) noises on background gray mean value of star map and number of identified stars are analyzed. A recognition algorithm for finding stars annihilated by radiated noise is proposed. Through theoretical derivation, the quantitative relationships between CMOS APS dark current noise, dark signal non-uniformity noise and photon response non-uniformity noise and star centroid positioning error are established. The γ radiation results show that the image gray-mean of the whole star map increases, the number of identified stars decreases, and the star point centroid positioning accuracy decreases, which seriously affect star map recognition of star sensor. This research provides a theoretical basis for the radiation-resistant reinforcement design of high precision star sensors.

Performance Investigation of Carbon Nanotube Based Temperature Compensated Piezoresistive Pressure Sensor

In silicon-based piezoresistive pressure sensor, the accuracy of the sensor is affected mainly by thermal drift and the sensitivity of the sensor varies with the rise in temperature. Here, the temperature effects on the desired representation of the sensor are analysed. Use of smart material Carbon nanotubes (CNT) and a few effective temperature compensation techniques are presented in this study to reduce the temperature effect on the accuracy of the sensor. Resistive compensation employed extra piezoresistors with Negative Temperature Coefficient of Resistivity (TCR) for temperature compensation. The attainment of the desired compensation techniques is highly compatible with the MEMS device fabrication. The compensated pressure sensor is supremacy for pressure measurement with temperature variations. Though various techniques have been suggested and put into actuality with successful attainment, the techniques featuring easy implementation and perfect compatibility with existing schemes are still blooming demanded to design a piezoresistive pressure sensor with perfect comprehensive performance. In this paper, CNT piezoresistive material has been employed as sensing elements for pressure sensor and compared with silicon in terms of output voltage and sensor performance degradation at higher temperature. Pressure sensors using CNT and silicon piezo resistive sensing materials were simulated on silicon (100) diaphragm by ANYSIS. Based on simulation results, silicon and CNT both pressure sensor also shows better results at near room temperature. With the increasing temperature it is observed that silicon pressure output underestimated by 23%.

Reliability evaluation method based on dynamic fault diagnosis results: A case study of a seabed mud lifting system

In this paper, a reliability evaluation method based on dynamic fault diagnosis results is proposed. The feasibility of the method is verified by taking the seabed mud lifting system as an example. The method considers the influence of the degradation of sensors and system equipment on the diagnostic results. The problem of overdiagnosis in static diagnosis network is avoided. Fault monitoring stage to locate the fault of the mud lifting system, reliability assessment stage to use dynamic Bayesian reverse diagnosis and forward reasoning to calculate the reliability of the whole system. The reliability of the system with and without sensor degradation is analyzed. The effects of common cause failure, multi-state degradation, redundant design and sensor performance degradation on system reliability are analyzed. The influence of the failure of different comphonents on the reliability of the system is studied. The sensitivity of the system is analyzed and the importance sequence of different components in the system is given.

Novel Virtual Sample Generation Using Target-Relevant Autoencoder for Small Data-Based Soft Sensor

In the complex industrial processes, the process data have the characteristics of imbalance and are incomplete due to the difficult-to-measure key variables, leading to the performance degradation of soft sensors. In order to deal with this problem, a novel noise injection virtual sample generation approach based on target-relevant autoencoder (NITAE-VSG) is proposed in this article. Different from the conventional VSG methods, the presented NITAE-VSG model can generate useful input and output virtual samples directly. To increase the diversity of virtual samples, the Gaussian noise (G_noise) is added into the features extracted from the target-relevant autoencoder (TAE) hidden layer. The generated virtual samples are combined with the original small samples to enhance the soft sensor model performance. To verify the reliable feasibility and superior effectiveness of the presented NITAE-VSG, two actual industrial cases: an industrial process of purified terephthalic acid shortened as PTA and a production system of ethylene are carried out. The results of simulations demonstrate that the presented NITAE-VSG can generate superior virtual samples to some other virtual sample generation (VSG) methods and can effectively improve the soft sensor modeling accuracy.

Effect of Total Ionizing Dose Damage on 8-Transistor CMOS Star Sensor Performance

The effects of total ionizing dose (TID) radiation from 60Co gamma-rays on an 8-transistor global shutter exposure complementary metal-oxide semiconductor image sensor (CIS) within a star sensor is presented to analyze the sources of star sensor performance degradation and the decrease of attitude measurement accuracy. The dark current, dark signal non-uniformity, and photon response non-uniformity versus the TID are investigated. The signal-to-noise ratio, star diagonal distance accuracy, and star point centroid positioning accuracy of the star sensor versus the TID are also analyzed. By establishing the correlation between space radiation, CIS noise, and star sensor performance parameters, the transfer mechanism of CIS parameter degradation to star sensor parameter degradation is revealed.

Spacecraft attitude determination from landmarks on space objects

In this paper, an attitude determination methodology for a space vehicle is presented. The proposed method uses the line-of-sight vectors, connecting landmarks captured in the images of an on-board camera to the center of perspective, as directional references. It solves the problem of landmark identification, in the event of warping of camera images, by using a reference catalog of well-mapped distinctive features of planets or large space objects. The knowledge of the orbital position of the camera is further utilized to reduce the search space within the catalog of landmarks. Post landmark identification, if more than one vector directions are available, the attitude corresponding to the rotational transformation of the reference frame to the camera fixed frame are extracted from the set of reference vectors and their corresponding observed vectors. The absolute three axis attitude updates so obtained can either supplement other sensors or serve as a back-up option in case of failure or performance degradation of conventional attitude sensors on-board a spacecraft at its end-of-life.

The Potential of Commercial Sensors for Spatially Dense Short-term Air Quality Monitoring Based on Multiple Short-term Evaluations of 30 Sensor Nodes in Urban Areas in Korea

ABSTRACT Recently, highly spatially dense air quality monitoring networks using low-cost sensors have been attempted worldwide. However, the quality of data from these sensor networks remains to be validated. This study assessed the potential of low-cost sensors for spatially dense air quality monitoring. Thirty sets of air quality sensor nodes for CO, NO2, O3, PM2.5, and PM10 were custom-built to evaluate their consistency in measurement, both among the sensor nodes and between the sensor node and instruments that use Federal Reference/Equivalent Methods (FRMs/FEMs) in the real atmosphere under two distinctly differing meteorological conditions (summer and winter) in Seoul and Busan, Korea. We found that commercially available low-cost sensors possess great potential as monitors for short-term air quality studies in urban areas, at least for one-month periods, given that (1) the self-consistency among the 30 sensors was high (R2 > 0.93), (2) the consistency between the sensors and the FRM/FEM instruments was reasonably high (R2 > 0.87 overall for the periods of comparison ), and (3) the consistency both among the sensors and between the sensors and the FRM/FEM instruments remained stable throughout the summer and the winter. However, vigorous data post-processing is needed to obtain reliable air quality data. For longer-term or temporally discontinuous monitoring, several issues must be addressed, including the limited lifetime of sensors, the degradation in sensor performance over time, and the long warm-up times for gaseous pollutant sensors. The O3 sensors required minimal post-processing correction, and the particulate matter and CO sensors agreed well with the FEM instruments after appropriate scale correction, but the NO2 sensors required additional efforts to correct for the effects of meteorological conditions and interfering materials. Overall, our results suggest that when investigating spatiotemporally heterogeneous distributions of air pollutants in various urban environments, a three-dimensional sensor network can be a useful tool for short-term monitoring, as long as data are corrected properly.

Performance and Reliability Degradation of CMOS Image Sensors in Back-Side Illuminated Configuration

We present a systematic characterization of wafer-level reliability dedicated test structures in Back-Side-Illuminated CMOS Image Sensors. Noise and electrical measurements performed at different steps of the fabrication process flow, definitely demonstrate that the wafer flipping/bonding/thinning and VIA opening proper of the Back-Side-Illuminated configuration cause the creation of oxide donor-like border traps. Respect to conventional Front-Side-Illuminated CMOS Image Sensors, the presence of these traps causes degradation of the transistors electrical performance, altering the oxide electric field and shifting the flat-band voltage, and strongly degrades also reliability. Results from Time-Dependent Dielectric Breakdown and Negative Bias Temperature Instability measurements outline the impact of those border traps on the lifetime prediction.

CMOS图像传感器辐射损伤导致星敏感器性能退化机理

In order to analyze the reasons for star sensor performance degradation and the decrease of attitude measurement accuracy, performance degradation mechanisms of star sensor caused by Complementary Metal Oxide Semiconductor (CMOS) image sensor radiation damage were mainly studied. By establishing the correlation among the spatial radiation, radiation sensitive parameters of CMOS image sensor and the performance parameters of star sensor, the transmission mechanism from CMOS image sensor device parameters degradation to star sensor system parameters degradation was revealed. The 60Co-γ irradiation test showed that the decrease of signal-to-noise ratio led to the decrease of star sensor detection sensitivity after irradiation, and the signal-to-noise ratio was the bridge between the CMOS image sensor and the star sensor system. The proton irradiation test showed that when the irradiation fluence was more than 3.68×1010 p/cm2, the star point centroid could not be extracted correctly. The results lay a certain foundation for the research of star sensor attitude measurement error and correction technology, and also provide some theoretical basis for the design of high-precision star sensor.

Online sensor performance monitoring and fault detection for discrete linear parameter-varying systems

This paper proposes a fault detection algorithm to identify online sensor performance degradation and failure, where the sensor faults are characterised by variations of the sensor measurement noise covariance matrix. That is, the proposed algorithm estimates the slowly-varying sensor measurement noise covariance and detects the abrupt and/or intermittent change of sensor measurement noise covariance. A memory-based technique is used to detect the abrupt (or intermittent) change of sensor noise covariance matrix. The memory-based technique is adopted due to its simplicity and online applicability. The proposed algorithm originally is designed for discrete linear time-varying (DLTV) systems and applied to discrete linear parameter-varying (DLPV) systems. Simulation results show that the proposed algorithm is capable of estimating the slowly-varying and detecting the abrupt (or intermittent) change of sensor measurement noise covariance for multiple-input and multiple-output discrete linear parameter-varying systems, where the scheduling parameters lie within a compact set. Furthermore, the proposed estimation algorithm shows a reasonable rate of convergence.

Online sensor ageing detection using a modified adaptive filter

This paper proposes a fault detection algorithm to detect online sensor performance degradation and failure due to sensor ageing, where the sensor faults due to ageing are characterised by slow variations of the sensor measurement noise covariance matrix. The proposed algorithm utilises the information about the quality of weighted innovation sequence to estimate the slowly-varying sensor noise covariance. The iterative manner of the proposed algorithm leads to significant reduction of the computational load, reduced sensitivity to initial conditions and improved estimation accuracy, making it suitable for online applications. Simulation results show that the proposed algorithm is capable of estimating the slowly-varying sensor noise covariance for multiple-input and multiple-output systems with noise covariance varying linearly, exponentially, or linearly with sinusoid fluctuation. Furthermore, the proposed estimation algorithm shows a reasonable rate of convergence, better estimation accuracy and less computation load in contrast to published literature.

ALERA

The successful deployment of autonomous real-time systems is contingent on their ability to recover from performance degradation of sensors, actuators, and other electro-mechanical subsystems with low latency. In this article, we introduce ALERA, a novel framework for real-time control law adaptation in nonlinear control systems assisted by system state encodings that generate an error signal when the code properties are violated in the presence of failures. The fundamental contributions of this methodology are twofold—first, we show that the time-domain error signal contains perturbed system parameters’ diagnostic information that can be used for quick control law adaptation to failure conditions and second, this quick adaptation is performed via reinforcement learning algorithms that relearn the control law of the perturbed system from a starting condition dictated by the diagnostic information, thus achieving significantly faster recovery. The fast (up to 80X faster than traditional reinforcement learning paradigms) performance recovery enabled by ALERA is demonstrated on an inverted pendulum balancing problem, a brake-by-wire system, and a self-balancing robot.

Mixed ICC/ℋ∞ control for systems with sensors aging

A faulty sensor may lead to degraded system performance, system instability or even a fatal accident. On the other hand, the increasing need for safety and reliability has motivated the development of fault-tolerant control techniques. In this work, the sensor performance degradation due to its aging is modelled by the increment of sensor measurement noise covariance. The main contribution of this paper is the characterisation of the control synthesis conditions using parametrised linear matrix inequalities (PLMIs) for a multi-objective gain-scheduled noisy output-feedback controller that minimises the output cost on performance with satisfactory system stability, performance and control input covariance constraints ( constraints on the control inputs) in the presence of sensor aging. The closed-loop system stability and performance, in terms of mixed / performances, relative improvement, numerical complexity, computation time, and initial conditions response, are studied, and a numerical example is used to illustrate the effectiveness of the proposed control scheme. The synthesised controller guarantees not only the stability but also the closed-loop mixed / performances, and it is feasible for real-time applications.