Measurement Noise Research Articles

Advancement in transformer fault diagnosis technology

The transformer plays a critical role in maintaining the stability and smooth operation of the entire power system, particularly in power transmission and distribution. The paper begins by providing an overview of traditional fault diagnosis methods for transformers, including dissolved gas analysis and vibration analysis techniques, elucidating their developmental trajectory. Building upon these traditional methods, numerous researchers have aimed to enhance and optimize them through intelligent technologies such as neural networks, machine learning, and support vector machines. These researchers have addressed common issues in traditional fault diagnosis methods, such as the low correlation between characteristic parameters and faults, ambiguous fault descriptions, and the complexity of feature analysis. However, due to the complexity of transformer structures and the uncertainties in operating environments, the collection and analysis of characteristic parameters becomes highly intricate. Researchers have further refined algorithms and feature values based on intelligent diagnostic algorithms for transformers. The goal is to improve diagnostic speed, mitigate the impact of measurement noise, and further advance the adaptability of artificial intelligence technology in the field of transformers. On the other hand, the excellent multi-parameter analysis capability of artificial intelligence technology is more suitable for transformer diagnostic techniques that involve the fusion of multiple information sources. Through the powerful data acquisition, processing, and decision-making capabilities provided by intelligent algorithms, it can comprehensively analyze non-electrical parameters such as oil and gas characteristics, vibration signals, temperature, along with electrical parameters like short-circuit reactance and load ratio. Moreover, it can automatically analyze the inherent relationship between faults and characteristic quantities and provide decision-making suggestions. This technique plays a pivotal role in ensuring transformer safety and power network security, emerging as a prominent direction in transformer fault diagnosis research.

Enhancement of DDST-MFAC for tracking performance by using dynamic data reconciliation

Abstract Model-free adaptive control (MFAC) stands out as an effective data-driven method for addressing nonlinear problems in industrial processes. To maintain good control performance, a data-driven set-point tuning (DDST) method is used to update the virtual set-point of the MFAC system. The DDST-based MFAC (i.e. DDST-MFAC) constantly approaches the target of the process through the nonlinear set-point tuning method. However, due to equipment errors and external interference, industrial sensors often suffer from measurement noise, which can have adverse effects on the control performance. In this article, an available dynamic data reconciliation (DDR) technique is adopted to improve the tracking performance of the DDST-MFAC, which suppresses the impact of process noise by using predicted information and measured data to achieve high-precision requirements for controlling nonlinear processes. Finally, considering both Gaussian and non-Gaussian distribution of measurement noise, the effectiveness of the proposed method was verified through the simulation of a nonlinear nonaffine plant. It is also applied to the steam-water heat exchange process, the control result is improved ultimately.

A Novel RUL Prediction Method for Rolling Bearings Based on Dynamic Control Chart and Adaptive incremental filtering

Abstract Degradation of rolling bearings typically consists of two stages: a stable stage (Stage I) characterized by stable fluctuations in the health indicator (HI), and a degradation stage (Stage II) where early damage leads to HI degradation, eventually reaching the failure threshold. Therefore, to achieve online RUL prediction for bearings, three aspects should be studied: 1) Degradation modeling; 2) Inter stage change point identification; 3) Online degradation state updating. Firstly, a two-stage degradation model is constructed by simultaneously considering inherent randomness, individual differences, and measurement errors. Then, a dynamic statistical process control (SPC) method is proposed to identify the change point from Stage I to Stage II. The SPC is designed to dynamically control limits based on the bearing's condition monitoring (CM) data to prevent false alarms. An adaptive incremental filtering (AIF) is proposed to update the degradation states by simultaneously considering the state increment and the dynamics of the system noise and measurement noise. The effectiveness of the proposed method is validated on 16004 bearing test data and XJTU-SY bearing data. Results show that the proposed method can accuracy identify the change point and improve the accuracy of the prediction result during stage II.

Effect of hydrogen poisoning on p-gate AlGaN/GaN HEMTs

In this work, we investigate the degradation behavior and mechanism of p-gate AlGaN/GaN high-electron mobility transistors (HEMTs) for the first time under hydrogen (H2) atmosphere. The experimental results reveal significant decrease in drain-to-source current, negative drift in threshold voltage, increase in off-state gate leakage current, and deterioration of subthreshold swing in the p-gate AlGaN/GaN HEMT after H2 treatment. The degradation of the electrical parameters is considered to hydrogen poisoning phenomenon. Through secondary ion mass spectrometry and variable temperature photoluminescence spectroscopy, we observe the increase in hydrogen concentration in the p-GaN layer and the formation of electrically inactive Mg–H complexes after H2 treatment. As results, the effective hole concentration decreases and the trap density of the device increases, which are confirmed by Hall effect measurement and low-frequency noise analysis, respectively. The detrimental effect of hydrogen on p-gate AlGaN/GaN HEMTs can be attributed primarily to the compensation of Mg doping and the generation of defects.

Commuter Experience: An Assessment of Metro-Train Comfort Amidst Operational Vibroacoustic Conditions

This study investigates the impact of different passenger rolling stock structures and train configurations on vibrations and noise generated during operation. Vibroacoustic measurements were performed during acceleration, constant speed, and braking phases to analyze the effects using statistical analysis according to the relevant standards, revealing a statistically significant relationship between the equivalent noise level and vibration dose. In the context of the expanding Warsaw metro network, which is adding new lines and modern rolling stock, trends in the development of metro rolling stock were analyzed using five different types of metro vehicles, from the oldest to the newest designs. Vehicle performance was ranked in the context of ride comfort using standards and combined vibration and noise measures. The research results allowed for a collective comparative assessment of the construction of individual types of passenger rolling stock in terms of vibroacoustic phenomena, thanks to which it is possible to assess the impact of modern solutions and the justification for investing in modern rolling stock. Newer trains generally record significantly lower vibration and noise levels. The difference between the oldest and the most modern vehicle types is 57% for the vibration acceleration level (0.08 ms−2 RMS) and 66% for the noise level (2.2 dB LAeq).

Measuring Residual Stresses with Crack Compliance Methods: An Ill-Posed Inverse Problem with a Closed-Form Kernel

By means of relaxation methods, residual stresses can be obtained by introducing a progressive cut or a hole in a specimen and by measuring and elaborating the strains or displacements that are consequently produced. If the cut can be considered a controlled crack-like defect, by leveraging Bueckner’s superposition principle, the relaxed strains can be modeled through a weighted integral of the residual stress relieved by the cut. To evaluate residual stresses, an integral equation must be solved. From a practical point of view, the solution is usually based on a discretization technique that transforms the integral equation into a linear system of algebraic equations, whose solutions can be easily obtained, at least from a computational point of view. However, the linear system is often significantly ill-conditioned. In this paper, it is shown that its ill-conditioning is actually a consequence of a much deeper property of the underlying integral equation, which is reflected also in the discretized setting. In fact, the original problem is ill-posed. The ill-posedness is anything but a mathematical sophistry; indeed, it profoundly affects the properties of the discretized system too. In particular, it induces the so-called bias–variance tradeoff, a property that affects many experimental procedures, in which the analyst is forced to introduce some bias in order to obtain a solution that is not overwhelmed by measurement noise. In turn, unless it is backed up by sound and reasonable physical assumptions on some properties of the solution, the introduced bias is potentially infinite and impairs every uncertainty quantification technique. To support these topics, an illustrative numerical example using the crack compliance (also known as slitting) method is presented. The availability of the Linear Elastic Fracture Mechanics Weight Function for the problem allows for a completely analytical formulation of the original integral equation by which bias due to the numerical approximation of the physical model is prevented.

Convergence analysis of state estimation for UAV formation under GPS‐denied environments

AbstractIn this article, we focus on the state estimation and formation control problem for a multi‐UAV system, where only parts of UAVs have GPS measurements, that is, the anchors, and the other UAVs measure their relative positions with anchors. With a second‐order discrete‐time model of each UAV in the formation, we analyze the convergence of state estimation (including its expectation and covariance) for commonly used consensus‐based formation control protocols, under the effect of control and measurement noise. More specifically, based on the discrete‐time stability analysis, we obtain the sufficient and necessary conditions for the expectation stability of the formation control. Using the fixed point theorem in positive symmetric matrix subspace and observability analysis, we rigorously prove that the covariance of estimation is bounded if and only if the anchor node set is globally reachable. Based on our analysis, the formation control parameters and system topology can be designed to satisfy sufficient conditions, such that the desired formation pattern can be achieved for the multi‐UAV dynamic system under GPS‐denied environments. Simulation results corroborate the effectiveness of our theoretical results.

Laboratory measurement of treadmill noise under several speeds, runners, and floor coverings

Laboratory measurement of treadmill noise under several speeds, runners, and floor coverings

Extremely low-vibration experiments with resolutions nanometer range supported on air tables may also require a low-noise environment

The paper reports key results from an extensive diagnostic study examining a room's vibration and acoustic noise environments and their correlation to measurement noise. Vibrations of unknown origin were contaminating research being performed on highly vibration-sensitive apparatus in the basement of a recently completed engineering building. The research involved extensive single‐molecule microscopy requiring resolutions down to 1 nm carried out on an optical table supported on pneumatic legs. Researchers reported that the experimental noise was reported to be of random amplitude less than 40 nm. The problem did not appear to correlate with obvious building or personnel activities. The design vibration and noise criteria for the new space had been VC-E and NC-40, respectively. At the time of the study, the ambient floor vibrations were several classes better; however, the acoustic environment was slightly worse. FRF correlation was calculated between combinations of the tabletop and floor vibrations and airborne sound. The best correlation was between tabletop vibration and airborne sound. The supply air was a significant part of the problem, but the ceiling offered very little attenuation from above. Some of the sound was being generated above the ceiling by HVAC components other than the supply air.

Reduction of sheets requirement for Slow Printing Operations at Noise Measurement Procedure in ECMA-74 Annex C

Reduction of sheets requirement for Slow Printing Operations at Noise Measurement Procedure in ECMA-74 Annex C

Airborne acoustic assessment of impact-resistant fitness flooring systems

Fitness facilities in residential and mixed-used commercial buildings introduce important acoustic design challenges that must be considered in a building. Cardio activities such as running on treadmills, group classes for high intensity training, and weight training areas introduce high levels of unwanted noise and vibration into a building structure. Isolated floor constructions are generally coordinated and implemented during design but are not practical when buildings are completed and occupied. Impact-resistant fitness flooring systems are typically explored and installed as an alternate approach to reduce the transmission of impact noise and vibration. Non-standardized field airborne noise measurements of weight drops were conducted on an existing gym and on four possible impact-resistant fitness flooring upgrades to assess the reduction of impact noise levels in the offices located below the gym. The measurements were also compared to the HVAC background noise levels to observe the increase above background conditions and provide a subjective evaluation. The results of the measurements show that impact-resistant fitness flooring systems provide a noticeable reduction of impact noise, compared to standard fitness flooring systems. The results also show that impact-resistant fitness flooring systems that include specialty engineered shock pads as a base layer provide a significant reduction of impact noise levels.

Noise exposure assessment of over-track buildings induced by train operations

The noise generated by trains running beneath these buildings significantly impacts residents and has garnered growing attention. To evaluate the influence of trains running under buildings on internal noise levels, this study conducted subjective questionnaire surveys and field measurements for noise and vibrations in two buildings located in Wuhan and Shenzhen, China. The subjective annoyance of noise was investigated through a survey conducted with a total of 152 participants. The measurement points were positioned at the 2nd-floor level in close proximity to the building’s support structures, as well as on the various floors. The contribution of airborne noise and structure-borne noise to the acoustic environment was identified, elucidating their respective impacts on the overall soundscape. Furthermore, an investigation was conducted to examine the correlation between structure-borne noise and floor vibrations, which was then compared with the guidelines provided by FTA (Federal Transit Administration). The results found that the indoor noise within the over-track building, resulting from train operations, mainly stems from structure-borne noise characterized by a frequency-dependent correlation among floor vibrations. This phenomenon is predominantly observed below 200 Hz with prominent peaks specifically at 40 Hz. The utilization of the FTA (Federal Transit Administration) recommended formula for predicting structure-borne noise levels based on velocity levels may result in an underestimation of the noise impact, particularly with regard to noise energy above 100 Hz. These findings hold significant implications for the design and construction of similar over-track buildings.

A novel concurrent learning-based fixed-time convergent visual depth observer for weakly persistently exciting perspective dynamical systems

The problem of synthesizing a fixed-time depth observer based on monocular image feedback is considered in this study. The key challenge lies in the fact that the perspective dynamical system used to model visual motion is typically characterized as being weakly persistently exciting, which complicates observer synthesis. Furthermore, the key to achieving the task objective (safe obstacle avoidance, for instance) lies in the synthesis of a depth observer that achieves rapid convergence of the (static) obstacle depth estimate to the ground truth in a known fixed-time. To address these challenges, and in contrast with prior schemes that rely on a motion-restrictive persistency of excitation (PE) condition for ensuring exponential convergence, a novel adaptive observer framework is considered in this study that incorporates a concurrent learning (CL) term for ensuring fixed-time observer convergence. In particular, the use of concurrent learning allows for the synthesis of a relaxed finite-time excitation condition that relies on historical data recorded over a dynamic sliding window in the recent past that the proposed observer relies on to ensure fixed-time convergence. Thus, a continuous-time reduced-order observer formulation is presented that relies on camera motion data to achieve fixed-time convergence to a uniform ultimate bound for a suitably large choice of the observer gains. Experimental results are used to demonstrate the efficacy of the proposed scheme in the presence of significant measurement noise. A performance comparison study is also undertaken to demonstrate superior performance of the proposed scheme compared to leading alternative designs. Finally, the practical applicability of the proposed scheme is verified by incorporating the proposed scheme within a reactive navigation scheme to accomplish obstacle avoidance. By incorporating a suitably informative CL term within the observer framework, the proposed scheme eliminates the need to rely on a difficult-to-verify PE condition, thus rendering it more suitable for practical applications like visual target-tracking and visual servo control.

Identifying cell states in single-cell RNA-seq data at statistically maximal resolution.

Single-cell RNA sequencing (scRNA-seq) has become a popular experimental method to study variation of gene expression within a population of cells. However, obtaining an accurate picture of the diversity of distinct gene expression states that are present in a given dataset is highly challenging because the sparsity of the scRNA-seq data and its inhomogeneous measurement noise properties. Although a vast number of different methods is applied in the literature for clustering cells into subsets with 'similar' expression profiles, these methods generally lack rigorously specified objectives, involve multiple complex layers of normalization, filtering, feature selection, dimensionality-reduction, employ ad hoc measures of distance or similarity between cells, often ignore the known measurement noise properties of scRNA-seq measurements, and include a large number of tunable parameters. Consequently, it is virtually impossible to assign concrete biophysical meaning to the clusterings that result from these methods. Here we address the following problem: Given raw unique molecule identifier (UMI) counts of an scRNA-seq dataset, partition the cells into subsets such that the gene expression states of the cells in each subset are statistically indistinguishable, and each subset corresponds to a distinct gene expression state. That is, we aim to partition cells so as to maximally reduce the complexity of the dataset without removing any of its meaningful structure. We show that, given the known measurement noise structure of scRNA-seq data, this problem is mathematically well-defined and derive its unique solution from first principles. We have implemented this solution in a tool called Cellstates which operates directly on the raw data and automatically determines the optimal partition and cluster number, with zero tunable parameters. We show that, on synthetic datasets, Cellstates almost perfectly recovers optimal partitions. On real data, Cellstates robustly identifies subtle substructure within groups of cells that are traditionally annotated as a common cell type. Moreover, we show that the diversity of gene expression states that Cellstates identifies systematically depends on the tissue of origin and not on technical features of the experiments such as the total number of cells and total UMI count per cell. In addition to the Cellstates tool we also provide a small toolbox of software to place the identified cellstates into a hierarchical tree of higher-order clusters, to identify the most important differentially expressed genes at each branch of this hierarchy, and to visualize these results.

The distributed fault diagnosis for nonlinear complex dynamical networks based on [formula omitted]-detection criterion

The actuator fault diagnosis problem of nonlinear complex dynamical networks with stochastic noise is investigated. Firstly, a local estimation based on a priori knowledge and measurement output is designed to estimate state for each node. A sufficient condition is derived such that the estimation error is stable and the estimation gain matrix is obtained in fault-free case by applying the stability theory. Secondly, considering additive actuator fault in presence of process and measurement noises, the residual signal based on estimation error is designed to detect faults and locate them. Thirdly, the threshold is acquired by applying the covariance matrix of estimation error. A fault detection and isolation logic is designed via the σ-detection criterion. Finally, two different examples are designed to verify the effectiveness of the designed actuator fault detection and isolation scheme.

A Real-time Performance Improvement Method for Composite Time Scale

Abstract The composite time scale (CTS) provides a stable, accurate and reliable time scale for modern society. The improvement of CTS’s real-time performance will improve its stability, which strengths related applications’ performance. Aiming at this goal, a method achieved by determining the optimal calculation interval and accelerating adjustment stage is proposed in this paper. The determinants of the CTS’s calculation interval (characteristics of the clock ensemble, the measurement noise, the time and frequency synchronization system’s noise and the auxiliary output generator noise floor) are studied and the optimal calculation interval is obtained. We also investigate the effect of ensemble algorithm’s initial parameters on the CTS’s adjustment stage. A strategy to get the reasonable initial parameters of ensemble algorithm is designed. The results show that the adjustment stage can be finished rapidly or even can be shorten to zero with reasonable initial parameters. On this basis, we experimentally generate a distributed CTS with a calculation interval of 500 s and its stability outperforms those of the member clocks when the averaging time is longer than ~ 1700 s. The experimental result proves that the CTS’s real-time performance is significantly improved.

Track vertical irregularity estimation for railway bridges using a novel and lightweight deep-learning architecture

A comprehensive methodology is presented for the estimation of track vertical irregularities of railway bridges from vehicle responses, based on a novel and lightweight multi-layer-perceptron (MLP) deep learning architecture. Firstly, a vehicle–track–bridge interaction (VTBI) model is established for the generation of the datasets of deep learning networks. Secondly, the lightweight deep learning architecture is meticulously designed to identify the track's vertical irregularities. Then, the effectiveness of the proposed technique is validated through examples of a single-span simple bridge and a three-span bridge. Further, the sensitivity of the present method against various factors is investigated, including different combinations of vehicle responses, measurement noise, vehicle speed and different classes of track irregularity. It is confirmed that the identified track irregularities of the railway bridges, regardless of irregularity class and noise level, are in excellent agreement with the ground-truth ones. The increase in vehicle speed to some extent reduces the estimation accuracy of the irregularity. The proposed method has higher identification accuracy and efficiency for longer irregularity sequences of multi-span railway bridges.

Программный комплекс для дифференциальной оценки риска здоровью населения от воздействия шума железнодорожного и автомобильного транспорта

Objective: in the modern world, the number of vehicles on the roads is growing exponentially, and the infrastructure either remains unchanged or is changing, but it is not happening as fast as we would like [1]. Long-term effects of noise exposure, such as disorders of the cardiovascular system (high blood pressure, myocardial infarction, coronary heart disease, etc.), the nervous system (sleep disorder, cognitive impairment, vegetative vascular dystonia), diseases of the ear and mastoid process (conductive and sensorineural hearing loss), depression and other mental disorders [2, 3, 4], are more related to indicators that sum up the noise exposure over a long time. Both when conducting tests by accredited specialists and when choosing housing for buyers, you have to rely on an analytical or practical way to assess the noise pollution of a room. In order to provide a person with a simple and objective way to assess noise pollution, a software package has been developed that allows assessing the impact of noise pollution from transport on public health. Methods: quantitative risk assessment was carried out in accordance with the methodological recommendations of MP 2.1.10.0059-12 “Assessment of public health risk from exposure to traffic noise”. Results: in order to automate calculations, unify the evaluation of results and reduce time costs, the authors propose a software package for differential assessment of public health risks from exposure to rail and road transport. The end product is a user-friendly, cross-platform application for operational risk assessment in the field for employees of test centers who measure noise from known sources of automobile, railway or combined traf fic flows. Practical importance: convenience and optimization of risk processing by noise measurements by employees of noise measurement laboratories using the application.

Robust pedestrian multi-object tracking in the intelligent bus environment

Abstract Pedestrian multi-object tracking algorithms aim to maintain identity information of pedestrians by comparing the similarity between trajectories and detections, predicting pedestrian motion trajectories. However, within the context of intelligent bus, challenges arise due to factors such as passenger growth and vehicle vibrations, rendering existing pedestrian multi-object tracking algorithms less accurate. Therefore, this paper proposes an intelligent bus terminal robust pedestrian multi-object tracking algorithm named pure motion (PM), which can consistently and stably track pedestrians. The proposed algorithm employs several key strategies. Firstly, it optimizes trajectory prediction by adapting the aspect ratio of the prediction box based on pedestrian movement, automatically adjusting its shape, and selecting velocity weight coefficients according to different tracking targets. Secondly, it decomposes the homography matrix to acquire motion components and correct predicted results under motion conditions. Subsequently, the algorithm leverages the similarity between detection results and trajectories to retain high-confidence detections, eliminating low-confidence ones associated with background, thereby reducing false negatives and enhancing trajectory coherence. Futhermore, the introduction of detection confidence into trajectory updates to enhances the precision of measurement noise. The proposed algorithm underwent testing in intelligent bus driving scenarios, including turns, waiting for traffic lights, emergency braking, and approaching bus stops. The tracking accuracy on the MOT17-13-val dataset reaches 81.8. The results demonstrate that PM significantly improves the robustness of pedestrian multi-object tracking algorithms in the environment of intelligent bus.

Random coordinate descent: A simple alternative for optimizing parameterized quantum circuits

Variational quantum algorithms rely on the optimization of parameterized quantum circuits in noisy settings. The commonly used back-propagation procedure in classical machine learning is not directly applicable in this setting due to the collapse of quantum states after measurements. Thus, gradient estimations constitute a significant overhead in a gradient-based optimization of such quantum circuits. This paper introduces a random coordinate descent algorithm as a practical and easy-to-implement alternative to the full gradient descent algorithm. This algorithm only requires one partial derivative at each iteration. Motivated by the behavior of measurement noise in the practical optimization of parameterized quantum circuits, this paper presents an optimization problem setting that is amenable to analysis. Under this setting, the random coordinate descent algorithm exhibits the same level of stochastic stability as the full gradient approach, making it as resilient to noise. The complexity of the random coordinate descent method is generally no worse than that of the gradient descent and can be much better for various quantum optimization problems with anisotropic Lipschitz constants. Theoretical analysis and extensive numerical experiments validate our findings. Published by the American Physical Society 2024