Sensor Reliability

In contemporary technological landscapes, sensors play a pivotal role in enabling diverse applications across industries, from healthcare to manufacturing. This paper undertakes a thorough investigation on system performance (reliability and availability of a system), focusing on the critical interplay between baseline performance, performance with integrated sensors and performance considering sensor reliability, recognizing the foundational importance of sensors in data-driven decision-making processes. The research employs a causation-based approach to systematically develop functional relations within the system. The failures identified of each component and functional relationships will then be analyzed using a simulation technique to understand the inherent performance of the engineering system. From here, a genetic algorithm is used to design a sensor set and tailor it for an engineering system, providing a foundation for conducting trade studies in the paper's subsequent sections. Through rigorous quantitative analysis and simulations, we compare the impacts of the performance of the sensor set design compared to the baseline performance. The paper then investigates the complexities of sensor reliability on overall system performance. Through advanced simulations, we elucidate the potential cascading effects that variations in sensor reliability can have on the system's performance. By exploring these ripple effects, we aim to provide a comprehensive understanding of how sensor reliability plays a crucial role in determining the success of complex systems. Beyond the immediate considerations of sensor characteristics, the paper analyses the maintenance aspects of sensors by performing a series of analyses to suggest maintenance aimed at improving the sensor and hence system reliability. Highlighting the relationship between sensor reliability and system performance, this section stresses the critical role of consistent maintenance practices in ensuring sustained data quality and system functionality. In conclusion, this paper aims to highlight the different perspectives that can be analyzed to understand the reality of system performance, considering facets such as sensor maintenance and reliability. It also aims to demonstrate various approaches that can be applied to engineering systems to uncover truths about sensor performance and reliability.

Upcoming emissions legislations and On-Board-Diagnostics (OBD) system requirements, both in the US and Europe, impose new challenges for particulate matter (PM) control strategies, especially with regard to upcoming EURO-VI regulations that are expected to limit the particulate number (PN) emissions. Indeed, the US-EPA Heavy-Duty-OBD regulations already require monitoring of diesel particulate filters (DPF) in 2010 for at least one engine series, with extension to all engine series by 2013. Due to the current absence of reliable in-line PM sensors to monitor DPF filtration efficiencies, manufacturers adopted alternative methods based on pressure drop measurements and semi-empirical models that require extensive calibration efforts; hence, driving upward the development costs. In order to meet upcoming OBD requirements and reduce unnecessary DPF regeneration frequencies, so as to minimize fuel consumption penalties, reliable sensors need to be integrated into the aftertreatment control environment. These next-generation sensors must be capable of performing actual real-time PM concentration measurements on a continuous basis within the exhaust stream. The primary objective of this study was to assess the capabilities, limitations and sensitivity of a newly developed inline PM sensor, with regard to its future application for OBD monitoring or control strategies of PM filter systems. The operation of the so called Pegasor Particulate Sensor (PPS) [1] is based on the escaping current principle. The instrument is capable of performing continuous PM measurements, directly from the exhaust stack, while providing a real-time signal with a resolution of 100 Hz. The sensor’s output signal can be calibrated to either measure the concentration of mass, surface or number of exhaust particles. Designed as a flow-through device, the PPS has a tungsten corona wire imposing an equal charge on particles that is subsequently measured from the outflowing particles via a built-in electrometer. The system does not involve collection or contact with particles in the exhaust stream, which is especially advantageous for long-term stability and operation without frequent maintenance; hence, best suited for in-use application. A comprehensive test matrix was developed to gain a more pronounced understanding of the sensor’s measurement technology by comparing it to other proven aerosol instruments, namely the Ultrafine Condensation Particle Counter (UCPC), Engine Exhaust Particle Sizer (EEPS™) spectrometer, Tapered Element Oscillating Microbalance (TEOM), and gravimetric PM. Test results demonstrated a stable and repeatable response over consecutive European Transient Cycle (ETC) and Federal Test Procedure (FTP) cycles, as well as over idle and constant load operation with coefficients of variation below 2%, which is a prerequisite for OBD algorithm implementation.

Rapidly and sensitively evaluating the acid value (AV) of edible oils is significant to ensuring food quality and safety. Cesium lead bromide perovskite nanocrystals (CsPbBr3 NCs) are an effective candidate for AV detection; however, their instability restricts wide applications. Herein, CsPbBr3@ZIF-8 was prepared by confining and growing CsPbBr3 NCs in situ into zeolitic imidazolate framework-8 (ZIF-8) to improve the stability, and a fluorescence sensor was established to evaluate the AV of edible oils. The results present that CsPbBr3 NCs (below 5 nm) with excellent optical properties were confined and grown in situ in micropores and mesopores of ZIF-8. Meanwhile, CsPbBr3@ZIF-8 had better long-term storage, ultraviolet-irradiation, and water-exposure stabilities, compared with CsPbBr3 NCs. Given the fact that free fatty acids (the major contributor of AV) decrease the fluorescence of CsPbBr3 NCs, the fluorescence intensities of CsPbBr3@ZIF-8 were negative-linearly related to oil AV (R2 = 0.9902) in 0.04-6.00 mg of KOH/g with a 0.06 mg of KOH/g limit of detection. Besides, the practical AV recovery was 92-101% with an average relative standard deviation of 2%. Furthermore, the detection time was 20 min. The response mechanism revealed that free fatty acids could remove surface ligands and increase surface defects to prompt the aggregation of CsPbBr3 NCs and the formation of lattice fringe dislocations, inducing a decrease in the fluorescence. Thus, a stable, sensitive, reliable sensor was established to evaluate the AV of edible oils.

13.05 - Issues of Connectivity, Durability, and Reliability of Sensors and Their Applications

*The largest barrier to effective operation of tip clearance sensors in aero engines has been survivability and sensor accuracy within difficult thermal environments, especially in the turbine area. Thermal cycling, high thermal gradients and total time at temperature often contribute to premature sensor degradation and failure. This paper describes the results of testing a microwave tip clearance sensor designed to operate within either the compressor or turbine. The testing included resolution and linearity measurements along with thermal cycling of the cable in a high temperature furnace. I. Introduction HE measurement of tip clearance within gas turbine engines is normally performed within the context of providing information back to engine designers. It is desired that tip clearances be designed as close as possible, yet still not rub the case as the engine goes through its operating envelope. It has been documented that tip clearance pinch points occur on takeoff and landing as differences in thermal expansions between the case and rotor occur during the full output power condition at takeoff as well as the thermal soak back that occurs immediately after landing. 1 It is difficult to obtain actual tip clearance data in the field, due to the difficulty in getting tip clearances sensors to provide accurate and reliable data for an extended period of time. Therefore, design tip clearances can often be conservative, since it normally desirable to operate at a larger clearance then risk significant mechanical contact between the blades and case. Large tip clearances can have a substantial impact on efficiencies. It has been reported in the literature that a 0.001” the tip clearance can be closed can contribute as much as an additional 0.1% in efficiency improvement. 2 Normal operation of the engine typically causes clearances to increase over time as the blades and case coatings gradually erode due to the gas path or mechanical contact between the blades and case. If an active clearance control system could be designed able to adjust the tip clearances dynamically for the current engine condition, then the clearances could be optimized for the current location within the operating envelope as well as assist in maintaining constant engine performance in-between overhauls. A variety of active tip clearance control methods have been proposed including thermal techniques that control the clearance using compressor bleed air as well as active mechanical techniques that shift the rotor axially or use some type of segmented case to squeeze the case around the blades. New large commercial engine designs use compressor bleed air to actively control the tip clearance. Due to the inability to directly measure clearances in the engine, the tip clearance is scheduled using available data off of the engine combined with various models and algorithms. This technique also has some conservatism within it, which would benefit from actual clearance data to close the loop and thus optimize the control system. To date, the main types of sensors that are used for measuring tip clearance include capacitive, eddy current, optical, and microwave. Each of these different sensing principals has positives and negatives associated with them. The largest barrier to date in implementing a tip clearance sensor on a production engine has been sensor reliability, especially in the hottest areas of the engine. Sensor reliability in the turbine will typically be tens to hundreds of hours without some type of external active cooling, such as nitrogen or circulated water. Therefore, the ability to implement a sensor with long term reliability remains the most significant barrier to improved active clearance control performance. The microwave sensor described in this paper contains some unique characteristics, which make it highly suitable for use in the turbine environment. This paper will describe those characteristics as well as provide the results of performance testing showing the applicability of the sensor for use within an active clearance control system.

Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data.

Despite the wide range of applications for the polymer electrolyte membrane fuel cell (PEMFC), its reliability and durability are still major barriers for further commercialization. As a possible solution, PEMFC fault diagnosis has received much more attention in the last few decades. Due to the difficulty of developing an accurate PEMFC model incorporating various failure mode effects, data-driven approaches are widely used for diagnosis purposes. These methods depend largely on the quality of sensor measurements from the PEMFC. Therefore, it is necessary to investigate sensor reliability when performing PEMFC fault diagnosis.In this study, sensor reliability is investigated by proposing an identification technique to detect abnormal sensors during PEMFC operation. The identified abnormal sensors will be removed from the analysis in order to guarantee reliable diagnostic performance. Moreover, the effectiveness of the proposed technique is investigated using test data from a PEMFC system, where fuel cell flooding is observed. During the test, due to accumulation of liquid water inside the PEMFC, the humidity sensors will give misleading readings, and flooding cannot be identified correctly with inclusion of these humidity sensors in the analysis. With the proposed technique, the abnormal humidity measurements can be detected at an early stage. Results demonstrate that by removing the abnormal sensors, flooding can be identified with the remaining sensors, thus reliable health monitoring can be guaranteed during the PEMFC operation.

Ankle fractures can cause significant functional impairment in the short and long term. In recent years, gait analysis using inertial sensors has gained special relevance as a reliable measurement system. This study aimed to evaluate the differences in spatiotemporal gait parameters and clinical–functional measurements in patients with bimalleolar ankle fracture and healthy subjects, to study the correlation between the different variables, and to analyze the test–retest reliability of a single inertial sensor in our study population. Twenty-two subjects with bimalleolar ankle fracture six months after surgery and eleven healthy subjects were included in the study. Spatiotemporal parameters were analyzed with the G-WALK sensor. Functional scales and clinical measures were collected beforehand. In the ankle fracture group, the main differences were obtained in bilateral parameters (effect size: 0.61 ≤ d ≤ 0.80). Between-group differences were found in cadence, speed, stride length, and stride time (effect size: 1.61 ≤ d ≤ 1.82). Correlation was moderate (0.436 < r < 0.554) between spatiotemporal parameters and clinical–functional measures, explaining up to 46% of gait performance. Test–retest reliability scores were high to excellent (0.84 ≤ ICC ≤ 0.98), with the worst results in the gait phases. Our study population presents evident clinical–functional impairments 6 months after surgery. The G-WALK can be considered a reliable tool for clinical use in this population.

High reliability of piezoresistive pressure sensors by wafer to wafer direct bonding at room temperature

We present studies on the long-term reliability of interferometric fiber-optic current sensors (FOCS) for use in electric power transmission systems. Accelerated ageing tests are performed on crucial optical sensor components and a three-phase sensor system is subjected to an extended field trial. The sensor components under test include the sensor's superluminescent light emitting diode light source, the integrated-optic phase modulator and various passive components such as fiber couplers, fiber polarizers, polarization-maintaining fiber connectors, and fiber coatings. The components are exposed to accelerated ageing conditions for extended periods of time, i.e., temperature cycling (between -25 °C and 65 °C for up to 15000 cycles), constant high temperature at dry conditions (up to 115 °C for up to 20000 h), and damp heat (85 % relative humidity at 85 °C for up to 7900 h). Crucial component parameters such as the source wavelength and polarization extinction ratios are repeatedly measured as a function of temperature at defined intervals during the ageing periods and examined for potential drift of component failures. The field trial is carried out for a three-phase FOCS system integrated into 420 kV double-chamber circuit breakers over a period of more than three years. The sensor signals are compared to the signals of conventional current transformers. In addition, the evolution of various operational parameters such as the light source power is continuously recorded. The results prove a high degree of reliability of modern FOCS systems.

Background: Power distribution Internet of Things is a smart service system in which traditional distribution networks comprehensively apply modern computing technologies, such as cloud computing, mobile Internet of Things, and big data to realize the interconnection of all things in the power system and human-computer interaction. In the power distribution Internet of Things, there are many types of main power distribution equipment, and each type of power distribution equipment is connected with multiple sensor devices. These sensor devices play an important role in the smooth operation of the power distribution Internet of Things, so it is necessary to build a reliability evaluation system for these sensor devices. Objectives: The objective was to evaluate the reliability of the sensor device connected to the main equipment in the power distribution Internet of Things. Methods: Firstly, the G1-CV method (entropy weight method-coefficient of variation method) is used to construct the initial evaluation index system, and the comprehensive weight of each index is determined and ranked. Then the improved TriMap algorithm (ITriMap) is used to reduce the high-dimensional complex index system. Results: Simulation verification results show that the algorithm has a smaller reconstruction error than traditional dimensionality reduction algorithms such as t-SNE, Largr-Vias, UMAP, etc. The reduced index can scientifically and objectively measure the main equipment sensors of the power distribution network. Conclusion: An index reduction method based on the ITriMap algorithm is proposed to evaluate the reliability of sensors. Experimental results show that this algorithm greatly reduces the reconstruction error compared to other traditional methods. Discussion: In the future, on the basis of this article, we intend to construct a sensor reliability evaluation model for the main equipment of the power distribution Internet of Things using the 5- dimensional features after dimensionality reduction as input.

Polymer electrolyte membrane fuel cell fault diagnosis and sensor abnormality identification using sensor selection method

Multi-radar data fusion techniques are important for self-driving vehicles to better perceive the environment. In the process of obtaining targets location and constructing environmental models, multi-radar data fusion can be regarded as a homogeneous time-series data fusion problem which is commonly solved by weighted fusion methods. However, when most sensors data are biased, especially when the data are all larger or smaller than the accurate data at the same time, the weights of sensors defined by sensor consistency and sensor stability can not reflect the real reliability of sensors. Thus, in this study, the correlation change between different observed parameters of one sensor, which is calculated based on the shape-based distance, together with the sensor consistency and sensor stability is leveraged to judge the reliability of the sensors and calculate the weights of the sensors. An experimental study is carried out and the Nuscenes dataset is used where a multi-radar system consists one LIDAR and two millimeter wave radars. In the situation that most sensors data are biased, the relative error and the root-mean-square error of the method proposed in this study are significantly lower than that of the arithmetic averaging method, the trimmed mean method and the weighted fusion method which only considers the sensor consistency and stability. Besides, under other situations, the fusion accuracy is also improved.

Review of the current issues of connectivity, durability and reliability of sensors and their solutions

The reliability of sensor is an important index to determine the outdoor operation efficiency of power sensor, and the reliability detection of power sensor is an important means to ensure the reliability of network equipment. Based on the analysis of the thermal characteristics and thermal behavior of the battery, this paper studies the current detection methods of the low-temperature battery of the power sensor, and proposes a reliability test method of the power sensor considering the influence of outdoor airflow. Through multi-physical field simulation and analysis, the key parameters of relevant experiments are obtained, and the key test parameters are given.