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Journal articles on the topic 'Drift diagnosis'

Author: Grafiati
Published: 25 January 2025

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1

Danko, G., Cs Mczci, G. Zhan, J. Daemen, and P. Mousset-Jones. "Integral Robotic Mine Drift Roof Diagnosis." IFAC Proceedings Volumes 28, no. 17 (August 1995): 29–35. http://dx.doi.org/10.1016/s1474-6670(17)46741-3.

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2

Reidenberg, Marcus M. "Diagnosis Drift and Its Contribution to Polypharmacy." Clinical Pharmacology & Therapeutics 103, no. 4 (October 3, 2017): 556–57. http://dx.doi.org/10.1002/cpt.858.

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3

Cheung, Allen, and Anne S. Kiremidjian. "Development of a Rotation Algorithm for Earthquake Damage Diagnosis." Earthquake Spectra 30, no. 4 (November 2014): 1381–401. http://dx.doi.org/10.1193/012212eqs016m.

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In the attempt to develop simple damage detection algorithms that can be embedded in structural monitoring systems for rapid condition assessment following a large earthquake, a new algorithm is developed that characterizes structural damage, based on the residual drift following a strong motion. The residual drift is estimated from rotations computed from strong motion structural response acceleration measurements taken at key points on the structure. The Paulay and Priestley (1992) plastic hinge model is used to evaluate the residual drift, given rotation measurements at points on a single column. The algorithm is tested using data collected from a set of reinforced concrete single-column shaking tests, performed at the University of Nevada, Reno, and the University of California, Berkeley. Results from the tests indicate that the rotation algorithm can potentially be used for detecting and quantifying damage to single-column structures using one rotation measurement. Additional calibration and further testing of the algorithm will be necessary to reduce possible overestimation of the residual drift present on the column due to the simplicity of the column deformation model. Nevertheless, the results serve as an initial proof of concept that can be useful and very practical as a rapid damage estimation technique.
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4

Zhao, Zhibin, Jianfeng Xu, Yanlong Zang, and Ran Hu. "Adaptive Abnormal Oil Temperature Diagnosis Method of Transformer Based on Concept Drift." Applied Sciences 11, no. 14 (July 8, 2021): 6322. http://dx.doi.org/10.3390/app11146322.

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The diagnosis of abnormal transformer oil temperature is of great significance to guarantee the normal operation of the transformer. Due to concept drift, the oil temperature abnormal diagnosis of the oil-immersed main power transformer is usually unstable via the classic data mining method. Thus, this paper proposes an adaptive abnormal oil temperature diagnosis method (AAOTD) of the transformer based on concept drift. First, the bagging ensemble learning method was used to predict the oil temperature. Then, abnormal diagnosis was performed based on the difference between the predicted oil temperature and the actual measured oil temperature. At the same time, based on the concept drift detection strategy and Adaboost ensemble learning methods, adaptive update of the base classifier in the abnormal diagnosis model was realized. Experiments validated that the algorithm proposed in this paper can significantly reduce the influence of concept drift and has higher oil temperature prediction accuracy. Furthermore, since this method only utilizes the existing power grid data resources to realize abnormal oil temperature diagnosis without extra monitoring equipment, it is an economic and efficient solution for practical scenarios in the electric power industry.
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Rahpoe, N., M. Weber, A. V. Rozanov, K. Weigel, H. Bovensmann, J. P. Burrows, A. Laeng, et al. "Relative drifts and biases between six ozone limb satellite measurements from the last decade." Atmospheric Measurement Techniques 8, no. 10 (October 16, 2015): 4369–81. http://dx.doi.org/10.5194/amt-8-4369-2015.

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Abstract. As part of European Space Agency's (ESA) climate change initiative, high vertical resolution ozone profiles from three instruments all aboard ESA's Envisat (GOMOS, MIPAS, SCIAMACHY) and ESA's third party missions (OSIRIS, SMR, ACE-FTS) are to be combined in order to create an essential climate variable data record for the last decade. A prerequisite before combining data is the examination of differences and drifts between the data sets. In this paper, we present a detailed analysis of ozone profile differences based on pairwise collocated measurements, including the evolution of the differences with time. Such a diagnosis is helpful to identify strengths and weaknesses of each data set that may vary in time and introduce uncertainties in long-term trend estimates. The analysis reveals that the relative drift between the sensors is not statistically significant for most pairs of instruments. The relative drift values can be used to estimate the added uncertainty in physical trends. The added drift uncertainty is estimated at about 3 % decade−1 (1σ). Larger differences and variability in the differences are found in the lowermost stratosphere (below 20 km) and in the mesosphere.
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6

Bhateja, Vikrant, Rishendra Verma, Rini Mehrotra, and Shabana Urooj. "A Non-Linear Approach to ECG Signal Processing using Morphological Filters." International Journal of Measurement Technologies and Instrumentation Engineering 3, no. 3 (July 2013): 46–59. http://dx.doi.org/10.4018/ijmtie.2013070104.

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Analysis of the Electrocardiogram (ECG) signals is the pre-requisite for the clinical diagnosis of cardiovascular diseases. ECG signal is degraded by artifacts such as baseline drift and noises which appear during the acquisition phase. The effect of impulse and Gaussian noises is randomly distributed whereas baseline drift generally affects the baseline of the ECG signal; these artifacts induce interference in the diagnosis of cardio diseases. The influence of these artifacts on the ECG signals needs to be removed by suitable ECG signal processing scheme. This paper proposes combination of non linear morphological operators for the noise and baseline drift removal. Non flat structuring elements of varying dimensions are employed with morphological filtering to achieve low distortion as well as good noise removal. Simulation outcomes illustrate noteworthy improvement in baseline drift yielding lower values of MSE and PRD; on the other hand high signal to noise ratios depicts suppression of impulse and Gaussian noises.
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7

Pezheva, Madina Kh, Kazbek K. Umarov, Andrey V. Yakimov, and Sergey V. Redkin. "Assessment of the first food of hydrobionts in the ecosystems of small rivers of Kabardino-Balkaria (on the example of the Nalchik river)." Veterinariya, Zootekhniya i Biotekhnologiya 12/1, no. 121 (2023): 122–36. http://dx.doi.org/10.36871/vet.zoo.bio.202312114.

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The article provides information about the drift of the natural food base of fish. The species composition of hydrobionts is given, the role of drift as the first food for river fish species is shown, their role in the diagnosis of the current ecological state of the small Nalchik river is determined.
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8

Rank, Wendi. "Assessing for pronator drift." Nursing 43, no. 4 (April 2013): 66. http://dx.doi.org/10.1097/01.nurse.0000428333.01107.94.

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9

Langenegger, Elisabeth, and Christian Lanz. "Drift injury - postmortal lesions can lead to a false diagnosis." Anthropologischer Anzeiger 63, no. 1 (March 11, 2005): 103–6. http://dx.doi.org/10.1127/anthranz/63/2005/103.

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10

Kumar, Jambi Ratna Raja, and Prateeksha Chouksey. "Gas Sensor Array Drift in an E-Nose System: A Dataset for Machine Learning Applications." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6 (July 23, 2023): 167–71. http://dx.doi.org/10.17762/ijritcc.v11i6.7343.

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Gas sensor arrays are widely used in various applications such as environmental monitoring, industrial process control, and medical diagnosis. However, one of the main challenges in using gas sensor arrays is their tendency to drift over time, which can significantly affect their accuracy and reliability. In this research paper, we present a gas sensor array drift dataset that can be used to evaluate and develop drift compensation techniques. The dataset consists of measurements from an array of eight metal oxide gas sensors exposed to six different target gases at varying concentrations over several months. The paper also describes the experimental setup, data acquisition process, and preliminary dataset analysis. Our results show that the sensor array exhibits significant drift over time and that the drift patterns vary depending on the target gas and concentration. This dataset can provide a valuable resource for researchers and engineers working on gas sensor array applications and can help advance the development of more robust and accurate gas sensing systems.
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11

Cui, Rang, Tiancheng Ma, Wenjie Zhang, Min Zhang, Longkang Chang, Ziyuan Wang, Jingzehua Xu, Wei Wei, and Huiliang Cao. "A New Dual-Mass MEMS Gyroscope Fault Diagnosis Platform." Micromachines 14, no. 6 (May 31, 2023): 1177. http://dx.doi.org/10.3390/mi14061177.

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MEMS gyroscopes are one of the core components of inertial navigation systems. The maintenance of high reliability is critical for ensuring the stable operation of the gyroscope. Considering the production cost of gyroscopes and the inconvenience of obtaining a fault dataset, in this study, a self-feedback development framework is proposed, in which a dualmass MEMS gyroscope fault diagnosis platform is designed based on MATLAB/Simulink simulation, data feature extraction, and classification prediction algorithm and real data feedback verification. The platform integrates the dualmass MEMS gyroscope Simulink structure model and the measurement and control system, and reserves various algorithm interfaces for users to independently program, which can effectively identify and classify seven kinds of signals of the gyroscope: normal, bias, blocking, drift, multiplicity, cycle and internal fault. After feature extraction, six algorithms, ELM, SVM, KNN, NB, NN, and DTA, were respectively used for classification prediction. The ELM and SVM algorithms had the best effect, and the accuracy of the test set was up to 92.86%. Finally, the ELM algorithm is used to verify the actual drift fault dataset, and all of them are successfully identified.
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12

PULLEN, RICHARD L. "Neurologic assessment for pronator drift." Nursing 34, no. 3 (March 2004): 22. http://dx.doi.org/10.1097/00152193-200403000-00019.

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13

Wu, Zhiwu, Tianfu Huang, Chunguang Wang, Xiang Wu, and Yanzhao Tu. "Transformer Fault Diagnosis and Location Method Based on Fault Tree Analysis." Scalable Computing: Practice and Experience 25, no. 5 (August 1, 2024): 3587–93. http://dx.doi.org/10.12694/scpe.v25i5.3182.

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Fiber optical current transformer (FOCT) is widely used in power systems for fault diagnosis and analysis, which can improve its operational reliability. Construct fault modes and fault trees based on fault data of all fiber current transformers, and construct a fault feature space. Constructing a fault diagnosis expert system using fault trees and fault feature space clustering centers to achieve accurate diagnosis of fault types, patterns, and components. The proposed method was validated using fault data and case studies of all fiber current transformers in a regional power grid, and the results showed that: The on-site fault case is closest to the cluster center of drift deviation fault, so it belongs to drift deviation fault. Further extract the on-site maintenance report, which indicates that the operating temperature of the all fiber current transformer is relatively high. The diagnostic results of the fault diagnosis expert system for the faulty all fiber current transformer are consistent with the actual results on site, verifying the accuracy and reliability of this method.
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14

Straumann, D., D. S. Zee, and D. Solomon. "Three-Dimensional Kinematics of Ocular Drift in Humans With Cerebellar Atrophy." Journal of Neurophysiology 83, no. 3 (March 1, 2000): 1125–40. http://dx.doi.org/10.1152/jn.2000.83.3.1125.

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One of the signs of the cerebellar ocular motor syndrome is the inability to maintain horizontal and vertical fixation. Typically, in the presence of cerebellar atrophy, the eyes show horizontal gaze-evoked and vertical downbeat nystagmus. We investigated whether or not the cerebellar ocular motor syndrome also includes a torsional drift and, specifically, if it is independent from the drift in the horizontal-vertical plane. The existence of such a torsional drift would suggest that the cerebellum is critically involved in maintaining the eyes in Listing's plane. Eighteen patients with cerebellar atrophy (diagnosis confirmed by magnetic resonance imaging) were tested and compared with a group of normal subjects. Three-dimensional eye movements (horizontal, vertical, and torsional) during attempted fixations of targets at different horizontal and vertical eccentricities were recorded by dual search coils in a three-field magnetic frame. The overall ocular drift was composed of an upward drift that increased during lateral gaze, a horizontal centripetal drift that appeared during lateral gaze, and a torsional drift that depended on horizontal eye position. The vertical drift consisted of two subcomponents: a vertical gaze-evoked drift and a constant vertical velocity bias. The increase of upward drift velocity with eccentric horizontal gaze was caused by an increase of the vertical velocity bias; this component did not comply with Listing's law. The horizontal-eye-position–dependent torsional drift was intorsional in abduction and extorsional in adduction, which led to an additional violation of Listing's law. The existence of torsional drift that is eye-position–dependent suggests that the cerebellum is critically involved in the implementation of Listing's law, perhaps by mapping a tonic torsional signal that depends on the direction of the line of sight. The magnitude of this signal might reflect the difference in torsional eye position between the torsional resting position determined by the mechanics of the eye plant and the torsional position required by Listing's law.
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15

Inacio, Maurilio, Andre Lemos, and Walmir Caminhas. "Fault Diagnosis with Evolving Fuzzy Classifier Based on Clustering Algorithm and Drift Detection." Mathematical Problems in Engineering 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/368190.

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The emergence of complex machinery and equipment in several areas demands efficient fault diagnosis methods. Several fault diagnosis methods based on different theories and approaches have been proposed in the literature. According to the concept of intelligent maintenance, the application of intelligent systems to accomplish fault diagnosis from process historical data has been shown to be a promising approach. In problems involving complex nonstationary dynamic systems, an adaptive fault diagnosis system is required to cope with changes in the monitored process. In order to address fault diagnosis in this scenario, use of the so-called “evolving intelligent systems” is suggested. This paper proposes the application of an evolving fuzzy classifier for fault diagnosis based on a new approach that combines a recursive clustering algorithm and a drift detection method. In this approach, the clustering update depends not only on a similarity measure, but also on the monitoring changes in the input data flow. A merging cluster mechanism was incorporated into the algorithm to enable the removal of redundant clusters. Multivariate Gaussian memberships functions are employed in the fuzzy rules to avoid information loss if there is interaction between variables. The novel approach provides greater robustness to outliers and noise present in data from process sensors. The classifier is evaluated in fault diagnosis of a DC drive system. In the experiments, a DC drive system fault simulator was used to simulate normal operation and several faulty conditions. Outliers and noise were added to the simulated data to evaluate the robustness of the fault diagnosis model.
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16

Hu, Jie, Tengfei Huang, Jiaopeng Zhou, and Jiawei Zeng. "Electronic Systems Diagnosis Fault in Gasoline Engines Based on Multi-Information Fusion." Sensors 18, no. 9 (September 3, 2018): 2917. http://dx.doi.org/10.3390/s18092917.

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The rapid development of electronic techniques in automobile has led to an increase of potential safety hazards, thus, a strong on-board diagnostic (OBD) system is desperately needed. To solve the problem of OBD insensitivity to manufacture errors or aging faults, the paper proposes a novel multi information fusion method. The diagnostic model is composed of a data fusion layer, feature fusion layer, and decision fusion layer. They are based on the back propagation (BP) neural network, support vector machine (SVM), and evidence theory, respectively. Algorithms are mainly focused on the reliability allocation of diagnostic results, which come from the data fusion layer and feature fusion layer. A fault simulator system was developed to simulate bias and drift faults of the intake pressure sensor. The real vehicle experiment was carried out to acquire data that are used to verify the availability of the method. Diagnostic results show that the multi-information fusion method improves diagnostic accuracy and reliability effectively. The study will be a promising approach for the diagnosis bias and drift fault of sensors in electronic control systems.
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17

Luo, Xiang Yan, Jun Bin Cao, and Jun Qing Cao. "Airborne Oxygen-Making System Drift Oxygen Sensor Characteristics for Fault Diagnosis Strategy." Applied Mechanics and Materials 347-350 (August 2013): 371–75. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.371.

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This paper focuses on airborne oxygen-making system shortcomings of oxygen sensor characteristic drift in, proposes a method of fault diagnosis. Oxygen sensor with a Wavelet packet analysis of feature extraction, based on wavelet neural network method to determine whether the sensor has failed, and sensor to detect hardware and software design are given.
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18

Pang, Fubin, Lihui Wang, Haifeng Wu, and Long Wan. "Online diagnosis algorithm for random drift error in fiber optic current sensor." Optical Fiber Technology 68 (January 2022): 102824. http://dx.doi.org/10.1016/j.yofte.2022.102824.

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19

Shan, Jiazeng, Henry T. Y. Yang, Weixing Shi, Daniel Bridges, and Paul K. Hansma. "Structural Damage Diagnosis Using Interstory Drift–Based Acceleration Feedback with Test Validation." Journal of Engineering Mechanics 139, no. 9 (September 2013): 1185–96. http://dx.doi.org/10.1061/(asce)em.1943-7889.0000531.

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20

Tran, Quang Thinh, and Sy Dzung Nguyen. "Bearing Fault Diagnosis Based on Measured Data Online Processing, Domain Fusion, and ANFIS." Computation 10, no. 9 (September 8, 2022): 157. http://dx.doi.org/10.3390/computation10090157.

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Processing noise online in sensors-based measurement data (SMD) and mitigating the effect of domain drift are always challenges. As a result, it negatively impacts the effectiveness and feasibility of data-driven model (DDM)-based mechanical-system fault identification (MFI). Here, we propose an online bearing fault diagnosis method named ANFIS-BFDM by using an adaptive neurofuzzy inference system (ANFIS). Reduction in the influence of domain drift between the source domain and target domain (DDSTD) is considered in both the data processing and fault identification. Online solutions for preprocessing SMD and exploiting the filtered data to label the target domain are presented in a fusion domain deriving from the source and target domains. First, in the offline phase, frequency-based splitting of SMD into different time series is performed to cancel the high-frequency region. An optimal data screening threshold (ODST) is distilled in the remaining low-frequency data to develop an impulse noise filter named FIN. An ANFIS then identifies the dynamic response of the bearing(s) via the filtered data. The FIN and ANFIS are finally exploited during the online phase to filter noise and recognize the object’s health status online. The survey results reflect the positive effects of the method, even if severe impulse noise appears in the databases.
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21

Nix, Cindy, Gael Cobraiville, Marie-Jia Gou, and Marianne Fillet. "Potential of Single Pulse and Multiplexed Drift-Tube Ion Mobility Spectrometry Coupled to Micropillar Array Column for Proteomics Studies." International Journal of Molecular Sciences 23, no. 14 (July 6, 2022): 7497. http://dx.doi.org/10.3390/ijms23147497.

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Proteomics is one of the most significant methodologies to better understand the molecular pathways involved in diseases and to improve their diagnosis, treatment and follow-up. The investigation of the proteome of complex organisms is challenging from an analytical point of view, because of the large number of proteins present in a wide range of concentrations. In this study, nanofluidic chromatography, using a micropillar array column, was coupled to drift-tube ion mobility and time-of-flight mass spectrometry to identify as many proteins as possible in a protein digest standard of HeLa cells. Several chromatographic parameters were optimized. The high interest of drift-tube ion mobility to increase the number of identifications and to separate isobaric coeluting peptides was demonstrated. Multiplexed drift-tube ion mobility spectrometry was also investigated, to increase the sensitivity in proteomics studies. This innovative proteomics platform will be useful for analyzing patient samples to better understand unresolved disorders.
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22

Al Balushi, Mitha, Fatima Al Maskari, Syed Javaid, and Amar Ahamd. "Drift in Depression Prevalence Disorder in Gulf Cooperation Council (GCC) Countries Over 30 Years." BJPsych Open 10, S1 (June 2024): S48—S49. http://dx.doi.org/10.1192/bjo.2024.175.

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AimsDepression disorder is a major public health problem and a serious medical illness which negatively affects people's daily life. The WHO's International Classification of Diseases (ICD–10) defines this set of disorders ranging from mild to moderate to severe. Estimated annual percentage change (EAPC) is a useful statistic that is used to measure trends in rates over time-period.The aim of this study was to compute the drift in depression prevalence disorder using the EAPC of the prevalence of depression disorder between 1990 to 2019 with corresponding 95% confidence intervals (95% CI) across the GCC countries.MethodsPrevalence of depression disorder data for the GCC countries were downloaded from “Our World in Data” https://ourworldindata.org/mental-health#depression. We computed the drift of depression over 30 years between the 6 GCC countries using the statistical software R.ResultsThe greatest decrease was seen for Bahrain which is (–5.2%) followed by Qatar (–3.2%) and United Arab Emirates (–3%). However, the largest increase was observed for Saudi Arabia (2.7%), followed by Kuwait (1.1%) and Oman (0.7%). The reduction in the prevalence of depression disorder seen in Bahrain, Qatar and United Arab Emirates shows a significant achievement in mental health diagnosis, prevention, and treatment.ConclusionHowever, further studies are required to better understand the drifts in the GCC countries. Furthermore, governmental funding for academic and research mental health programs is highly recommended.
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23

Mamedli, Siusan N., Marina A. Chekalova, and Liudmila A. Meshcheriakova. "Modern approaches to the diagnosis of malignant trophoblastic tumors." Journal of Modern Oncology 23, no. 2 (August 16, 2021): 345–48. http://dx.doi.org/10.26442/18151434.2021.2.200478.

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Malignant trophoblastic tumors (TO) include invasive and metastatic cystic drift, choriocarcinoma, TO of the placental bed, and epithelioid TO. They are rare, mainly in women of reproductive age, and most importantly, they are always associated with pregnancy. To date, the Blokhin National Medical Research Center of Oncology has accumulated a large and unique experience of modern diagnostics and treatment of patients with various forms of malignant TO. An obligatory stage of the examination is ultrasound diagnostics of the pelvic organs. In addition, performing an ultrasound examination during the treatment period, along with monitoring the level of chorionic gonadotropin, makes it possible to assess the effectiveness of treatment, diagnose tumor resistance and ascertain the onset of remission.
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24

Tinati, Mohammad Ali, and Behzad Mozaffary. "A Wavelet Packets Approach to Electrocardiograph Baseline Drift Cancellation." International Journal of Biomedical Imaging 2006 (2006): 1–9. http://dx.doi.org/10.1155/ijbi/2006/97157.

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Baseline wander elimination is considered a classical problem. In electrocardiography (ECG) signals, baseline drift can influence the accurate diagnosis of heart disease such as ischemia and arrhythmia. We present a wavelet-transform- (WT-) based search algorithm using the energy of the signal in different scales to isolate baseline wander from the ECG signal. The algorithm computes wavelet packet coefficients and then in each scale the energy of the signal is calculated. Comparison is made and the branch of the wavelet binary tree corresponding to higher energy wavelet spaces is chosen. This algorithm is tested using the data record from MIT/BIH database and excellent results are obtained.
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25

Chew, Christopher, and Supriya Mannepalli. "COVID-19: No Guaranteed Protection from Future Infection after the Initial Diagnosis." Case Reports in Infectious Diseases 2021 (March 30, 2021): 1–8. http://dx.doi.org/10.1155/2021/6617719.

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The world of microbiology is vast in nature, and viruses continue to be a subset containing a lot of unknowns. Initial infection with certain viruses, such as varicella zoster virus and measles, allows for development of lifelong immunity; on the other hand, the influenza virus requires yearly vaccination, which may not provide adequate immunity. This can be attributed to antigenic shift and drift, rendering previously made antibodies ineffective against new strains of influenza. This article describes six cases of patients who presented with mild acute respiratory symptoms and tested positive for COVID-19 virus. After recovering from initial illness and being asymptomatic for several months, they developed recurrence of acute respiratory symptoms and, again, tested positive for COVID-19 virus, in more severe form than initial presentation. In the current state of the world, COVID-19 has created a lot of unknowns in the medical community, including patient presentation and treatment. COVID-19 research is evolving daily, but many questions remained unanswered. “Will a sufficient antibody response be created by the human body in those infected with COVID-19 and how long will that immunity last?” “Will antigenic drift occur quickly allowing the virus to evade previously made antibodies?” During initial surveillance of the COVID-19 virus, we were expecting development of an immune response comparable to SARS-CoV-1 and MERS-CoV, given the viral similarities. Unfortunately, based on our observations, this may not necessarily be true and will be further discussed in the presented article.
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Ye, Zhenyi, Yuan Liu, and Qiliang Li. "Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods." Sensors 21, no. 22 (November 16, 2021): 7620. http://dx.doi.org/10.3390/s21227620.

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Machine learning methods enable the electronic nose (E-Nose) for precise odor identification with both qualitative and quantitative analysis. Advanced machine learning methods are crucial for the E-Nose to gain high performance and strengthen its capability in many applications, including robotics, food engineering, environment monitoring, and medical diagnosis. Recently, many machine learning techniques have been studied, developed, and integrated into feature extraction, modeling, and gas sensor drift compensation. The purpose of feature extraction is to keep robust pattern information in raw signals while removing redundancy and noise. With the extracted feature, a proper modeling method can effectively use the information for prediction. In addition, drift compensation is adopted to relieve the model accuracy degradation due to the gas sensor drifting. These recent advances have significantly promoted the prediction accuracy and stability of the E-Nose. This review is engaged to provide a summary of recent progress in advanced machine learning methods in E-Nose technologies and give an insight into new research directions in feature extraction, modeling, and sensor drift compensation.
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27

Liang, Guo Zhuang, Su Fang Sun, and Jing Xia Wei. "An Approach Based on Wavelet Transform to Remove the Noises of ECG." Advanced Materials Research 562-564 (August 2012): 1899–902. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.1899.

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In the acquisition process of ECG, noise, which mainly consists of power line interference baseline drift and the EMG interference, often exists due to the instrument, the human body and other aspects. This noise mixed with the ECG, will causes ECG distortion, which makes the whole ECG waveform blurred, and impacts the subsequent signal processing and analysis. In this paper, Coif4 wavelet is used to make the ECG decomposed by 8 scale; at the same time, the wavelet decomposition and reconstruction method is used to remove baseline drift, and then the improved wavelet threshold method is used to remove power line interference and the EMG interference waveform to obtain accurate geocentric, providing a more accurate basis for the medical diagnosis.
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Joo, Jihoon E., Mark Clendenning, Ee Ming Wong, Christophe Rosty, Khalid Mahmood, Peter Georgeson, Ingrid M. Winship, et al. "DNA Methylation Signatures and the Contribution of Age-Associated Methylomic Drift to Carcinogenesis in Early-Onset Colorectal Cancer." Cancers 13, no. 11 (May 25, 2021): 2589. http://dx.doi.org/10.3390/cancers13112589.

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We investigated aberrant DNA methylation (DNAm) changes and the contribution of ageing-associated methylomic drift and age acceleration to early-onset colorectal cancer (EOCRC) carcinogenesis. Genome-wide DNAm profiling using the Infinium HM450K on 97 EOCRC tumour and 54 normal colonic mucosa samples was compared with: (1) intermediate-onset CRC (IOCRC; diagnosed between 50–70 years; 343 tumour and 35 normal); and (2) late-onset CRC (LOCRC; >70 years; 318 tumour and 40 normal). CpGs associated with age-related methylation drift were identified using a public dataset of 231 normal mucosa samples from people without CRC. DNAm-age was estimated using epiTOC2. Common to all three age-of-onset groups, 88,385 (20% of all CpGs) CpGs were differentially methylated between tumour and normal mucosa. We identified 234 differentially methylated genes that were unique to the EOCRC group; 13 of these DMRs/genes were replicated in EOCRC compared with LOCRCs from TCGA. In normal mucosa from people without CRC, we identified 28,154 CpGs that undergo ageing-related DNAm drift, and of those, 65% were aberrantly methylated in EOCRC tumours. Based on the mitotic-based DNAm clock epiTOC2, we identified age acceleration in normal mucosa of people with EOCRC compared with normal mucosa from the IOCRC, LOCRC groups (p = 3.7 × 10−16) and young people without CRC (p = 5.8 × 10−6). EOCRC acquires unique DNAm alterations at 234 loci. CpGs associated with ageing-associated drift were widely affected in EOCRC without needing the decades-long accrual of DNAm drift as commonly seen in intermediate- and late-onset CRCs. Accelerated ageing in normal mucosa from people with EOCRC potentially underlies the earlier age of diagnosis in CRC carcinogenesis.
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29

Yam, Jason C. S., Gabriela S. L. Chong, Patrick K. W. Wu, Ursula S. F. Wong, Clement W. N. Chan, and Simon T. C. Ko. "Predictive Factors Affecting the Short Term and Long Term Exodrift in Patients with Intermittent Exotropia after Bilateral Rectus Muscle Recession and Its Effect on Surgical Outcome." BioMed Research International 2014 (2014): 1–4. http://dx.doi.org/10.1155/2014/482093.

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Purpose. To determine the predictive factors that affect short term and long term postoperative drift in intermittent exotropia after bilateral lateral rectus recession and to evaluate its effect on surgical outcome.Methods. Retrospective review of 203 patients with diagnosis of intermittent exotropia, who had surgical corrections with more than 3 years of followup. Different preoperative parameters were obtained and evaluated using Pearson’s correlation analysis.Results. The proportion of exodrift increased from 62% at 6 weeks to 84% at 3 years postoperatively. The postoperative drift was4.3±8.1 PD at 6 weeks,5.8±8.4 PD at 6 months,7.2 ± 8.3 PD at 1 year,7.4 ± 8.4 PD at 2 years, and7.7 ± 8.5 PD at 3 years. Preoperative deviation and initial overcorrection were significant factors affecting the postoperative drift at 3 years (r=0.177,P=0.011,r=-0.349, andP<0.001, resp.).Conclusions. Postoperative exodrift along three years occurs in a majority of patients after bilateral lateral rectus recession for intermittent exotropia. The long term surgical success is significantly affected by this postoperative exodrift. A larger preoperative deviation and a larger initial overcorrection are associated with a larger early and late postoperative exodrift.
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Wang, Lihui, Kai Zhao, Wenpeng Zhang, Jian Liu, and Fubin Pang. "Intelligent fault diagnosis algorithm for fiber optic current transformer." International Journal of Applied Electromagnetics and Mechanics 64, no. 1-4 (December 10, 2020): 3–10. http://dx.doi.org/10.3233/jae-209301.

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Affected by environmental factors, the performance of fiber optic current transformer (FOCT) will deteriorate over a long period of time. Intelligent fault diagnosis algorithm of Long-Short Term Memory (LSTM) combing with Support Vector Machine (SVM) is an effective way to deal with FOCT failures. According to the characteristics of LSTM, a signal prediction model in FOCT based on LSTM is proposed by analyzing the historical data. The residual signal can be obtained by the prediction signal and the observed signal. Set the residual threshold to determine whether the FOCT has fault. With the residual signal characteristics, a fault diagnosis model based on SVM is established. By analyzing the residual signal and extracting features, the diagnostic network can realize the pattern recognition and system fault diagnosis. Experiments demonstrate that the drift deviation fault, the ratio deviation fault and the fixed deviation fault can be diagnosed with an accuracy of 94.5%.
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Skurzok, Magdalena, and Aleksander Khreptak. "Efficiency analysis and promising applications of silicon drift detectors." Bio-Algorithms and Med-Systems 19, no. 1 (December 31, 2023): 74–79. http://dx.doi.org/10.5604/01.3001.0054.1936.

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Silicon drift detectors (SDDs) stand as a groundbreaking technology with a diverse range of applications, particularly in the fields of physics and medical imaging. This paper provides an analysis of the performance of SDDs as detectors for X-ray radiation measurement, shedding light on their exceptional capabilities and potential in medical imaging. Compared to conventional detectors, SDDs have several notable advantages. Their high efficiency in capturing X-rays allows them to provide outstanding sensitivity and accuracy in detecting even low-energy X-rays. In addition, SDDs exhibit significantly low electronic-noise levels, contributing to better signal-to- -noise ratio and better data quality. Furthermore, their high resolution enables exact spatial localization of radiation sources, which is essential for accurate diagnosis. This research is devoted to the evaluation of efficiency and potential application of SDDs in X-ray spectroscopy, with particular emphasis on their application in medical imaging. We focus on evaluating the performance characteristics of SDDs, such as their linearity, stability and sensitivity in detecting X-rays. The aim is to highlight the suitability of SDDs for a wide range of applications.
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Gudagunti, Fleming Dackson, Logeeshan Velmanickam, Dharmakeerthi Nawarathna, and Ivan T. Lima. "Nucleotide Identification in DNA Using Dielectrophoresis Spectroscopy." Micromachines 11, no. 1 (December 28, 2019): 39. http://dx.doi.org/10.3390/mi11010039.

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We show that negative dielectrophoresis (DEP) spectroscopy is an effective transduction mechanism of a biosensor for the detection of single nucleotide polymorphism (SNP) in a short DNA strand. We observed a frequency dependence of the negative DEP force applied by interdigitated electrodes to polystyrene microspheres (PM) with respect to changes in both the last and the second-to-last nucleotides of a single-strand DNA bound to the PM. The drift velocity of PM functionalized to single-strand DNA, which is proportional to the DEP force, was measured at the frequency range from 0.5 MHz to 2 MHz. The drift velocity was calculated using a custom-made automated software using real time image processing technique. This technology for SNP genotyping has the potential to be used in the diagnosis and the identification of genetic variants associated with diseases.
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Huang, Kaifeng, Zegong Liu, and Dan Huang. "Fault Diagnosis for Methane Sensors using Generalized Regression Neural Network." International Journal of Online Engineering (iJOE) 12, no. 03 (March 31, 2016): 42. http://dx.doi.org/10.3991/ijoe.v12i03.5443.

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To identify the hang, collision and drift faults of methane sensors, this paper presents a fault diagnosis method for methane sensors using multi-sensor information fusion. A methane concentration monitoring approximation model with multi-sensor information fusion is established based on generalized regression neural network (GRNN).The output of the neural network is compared with the measured value of the sensor to be diagnosed to obtain the variation curve of the residual error signal. Through the analysis of the variation tendency of the residual error signal, the fault status of a methane sensor could be determined based on a reasonable threshold. Through simulation comparison is applied between the two models of GRNN and BP neural network; verify the GRNN model is much more precise in the approximation of methane concentrations. Fault diagnosis for methane sensors using generalized regression neural network is effective and more efficient.
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Miller, Eric Tyler, Lorna Kwan, Sandy Liu, and Isla Garraway. "Evaluating PSA nadir drift in high-risk and metastatic prostate cancer." Journal of Clinical Oncology 34, no. 2_suppl (January 10, 2016): 310. http://dx.doi.org/10.1200/jco.2016.34.2_suppl.310.

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310 Background: Prostate cancer (PC) is a heterogeneous, clinically disparate, and often unpredictable disease whereby nearly 3% of patients present with bone metastatic PC (BMPC). Virtually all patients diagnosed with stage M1 disease fail to achieve complete remission and rapidly progress to castration-resistant PC (CRPC) and death (28% 5-year survival). Androgen deprivation therapy (ADT), the current standard of care for M1 disease, does not induce durable remissions. Improved understanding of tumor biology associated with lethal progression is desperately needed to guide clinical trials. Methods: We assembled a diverse, clinically annotated biorepository with diagnostic biopsy tissue from 428 patients with high-risk M0 and M1 disease. Retrospective analysis was performed on M0 (n = 157) and M1 (n = 112) patients stratified by treatment sequence. Clinical and pathological variables analyzed included age at diagnosis, race, primary Gleason sum, tumor burden, initial treatment received, PSA at time of biopsy, PSA velocity and length of follow-up. Primary endpoints included PSA nadir per treatment round, time from biopsy to CRPC, and death. Results: Compared to M0 cases, M1 patients were older, displayed significantly higher PSAs at the time of biopsy, and were more likely to display primary Gleason pattern 5. Patients with stage M1 disease had significantly worse survival, shorter time to CRPC, and median PSA nadir following each treatment round never fell below 2ng/ml. Median PSA nadirs associated with each treatment round demonstrated marked upward drift in the M1 cohort when compared to the M0 cohort. Conclusions: The presence of distant metastases is a major obstacle in achieving durable remissions for very high-risk PC. Our data confirms that ADT has limited effect on controlling cancer progression in M1 patients. PSA nadir drift in M1 compared to M0 patients may indicate intrinsic tumor resistance to currently available therapies. Multimodal profiling of M1 primary tumors may reveal actionable targets for initiation of combination treatment regimens at diagnosis.
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Xiao, Hongjun, Yiqi Liu, and Daoping Huang. "Semiadaptive Fault Diagnosis via Variational Bayesian Mixture Factor Analysis with Application to Wastewater Treatment." Journal of Control Science and Engineering 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/2034826.

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Mainly due to the hostile environment in wastewater plants (WWTPs), the reliability of sensors with respect to important qualities is often poor. In this work, we present the design of a semiadaptive fault diagnosis method based on the variational Bayesian mixture factor analysis (VBMFA) to support process monitoring. The proposed method is capable of capturing strong nonlinearity and the significant dynamic feature of WWTPs that seriously limit the application of conventional multivariate statistical methods for fault diagnosis implementation. The performance of proposed method is validated through a simulation study of a wastewater plant. Results have demonstrated that the proposed strategy can significantly improve the ability of fault diagnosis under fault-free scenario, accurately detect the abrupt change and drift fault, and even localize the root cause of corresponding fault properly.
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Bax, Carmen, Stefano Prudenza, Giulia Gaspari, Laura Capelli, Fabio Grizzi, and Gianluigi Taverna. "Drift compensation on electronic nose data for non-invasive diagnosis of prostate cancer by urine analysis." iScience 25, no. 1 (January 2022): 103622. http://dx.doi.org/10.1016/j.isci.2021.103622.

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Tijms, Betty M., Sandra D. Mulder, Pieter Jelle Visser, Wiesje M. van der Flier, Philip Scheltens, and Charlotte E. Teunissen. "CSF AMYLOID BETA 1-42 LEVELS OBTAINED OVER 15 YEARS SHOW A DIAGNOSIS-DEPENDENT UPWARD DRIFT." Alzheimer's & Dementia 13, no. 7 (July 2017): P198—P199. http://dx.doi.org/10.1016/j.jalz.2017.07.060.

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Fuhrmann, A., I. A. Kane, M. A. Clare, R. A. Ferguson, E. Schomacker, E. Bonamini, and F. A. Contreras. "Hybrid turbidite-drift channel complexes: An integrated multiscale model." Geology 48, no. 6 (March 18, 2020): 562–68. http://dx.doi.org/10.1130/g47179.1.

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Abstract The interaction of deep-marine bottom currents with episodic, unsteady sediment gravity flows affects global sediment transport, forms climate archives, and controls the evolution of continental slopes. Despite their importance, contradictory hypotheses for reconstructing past flow regimes have arisen from a paucity of studies and the lack of direct monitoring of such hybrid systems. Here, we address this controversy by analyzing deposits, high-resolution seafloor data, and near-bed current measurements from two sites where eastward-flowing gravity flows interact(ed) with northward-flowing bottom currents. Extensive seismic and core data from offshore Tanzania reveal a 1650-m-thick asymmetric hybrid channel levee-drift system, deposited over a period of ∼20 m.y. (Upper Cretaceous to Paleocene). High-resolution modern seafloor data from offshore Mozambique reveal similar asymmetric channel geometries, which are related to northward-flowing near-bed currents with measured velocities of up to 1.4 m/s. Higher sediment accumulation occurs on the downstream flank of channel margins (with respect to bottom currents), with inhibited deposition or scouring on the upstream flank (where velocities are highest). Toes of the drift deposits, consisting of thick laminated muddy siltstone, which progressively step back into the channel axis over time, result in an interfingering relationship with the sandstone-dominated channel fill. Bottom-current flow directions contrast with those of previous models, which lacked direct current measurements or paleoflow indicators. We finally show how large-scale depositional architecture is built through the temporally variable coupling of these two globally important sediment transport processes. Our findings enable more-robust reconstructions of past oceanic circulation and diagnosis of ancient hybrid turbidite-drift systems.
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Ng, Selina Ka-wai, Eric Yat-tung Chan, and Shuk-yu Leung. "Comparison of Calibration Drift in Transcutaneous Carbon Dioxide Monitoring Devices for Overnight Level 4 Sleep Study in Hong Kong Children." Pediatric Respirology and Critical Care Medicine 8, no. 2 (April 2024): 25–32. http://dx.doi.org/10.4103/prcm.prcm_21_23.

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Abstract Background: Level 4 sleep study with transcutaneous carbon dioxide (TcCO2) monitoring is a simple, non-invasive method to investigate sleep-related hypoventilation. However, calibration drift in the TcCO2 device weakens its reliability. Materials and Methods: We conducted a retrospective study of 61 patients from <1 to 20 years of age in our paediatric unit, who were assigned one of the two models of TcCO2 machines (SenTec Digital Monitoring System and Tina Radiometer Copenhagen TCM4 Transcutaneous Blood Gas Monitor) for performing the Level 4 sleep study, using capillary blood gas carbon dioxide (pCO2) level at the first and ninth hours as a reference. Results: For the 9-h sleep study, there was no change in the attachment site, membrane, or sensor. The TcCO2–pCO2 difference at the ninth hour in the former model was 0.03 ± 0.61 kPa (0.26 ± 4.59 mm Hg), which was favourable in comparison to the latter (–0.45 ± 1.25 kPa or –3.38 ± 9.38 mm Hg), with P = 0.014; the TcCO2–pCO2 difference between monitors A and B at the ninth hour compared to the first hour did not differ substantially from the former (P = 0.160), but a statistically significant difference was noted in the latter model (P = 0.037). Both findings indicated calibration drift and hence less accurate TcCO2 readings in the latter model. Conclusion: In overnight extended use, calibration drift might affect the diagnosis and management of sleep-related hypoventilation.
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Cheng, Chi-Cheng, Yih-Tun Tseng, Cheng-Da Wu, and Der-Lin Wang. "Fault Diagnosis of a High-Speed Cam-Driven Pin Assembly System." Advances in Materials Science and Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/5917408.

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A cam-driven mechanical system applied for pin assembly of connectors of electrical devices is studied in this paper. Three cooperative cams are involved in the tasks of approaching, cutting, insertion, and restoring. In order to meet the demanded productivity growth, the operation speed tends to be elevated. However, high running speeds usually cause deficiencies of pin dropping and inaccurate positioning. Diagnosis is therefore conducted to explore their physical reasons so that modification of future mechanical design can be made. Frequency responses of experimental measurements show greater natural frequency and system stiffness caused by nonlinear dynamics for higher operation speed. It also appears that the clamping force is reduced and drift of the locked pin’s location is induced for higher running speed. In addition, separation of the fixture system induced by contact oscillation generates clearance larger than the thickness of the pin. Based on the mathematical models obtained from the technique of system identification, deeper insight of the mechanical system and future system improvement can be highly expected.
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Bucci, Ovidio M., Gennaro Bellizzi, Sandra Costanzo, Lorenzo Crocco, Giuseppe Di Massa, and Rosa Scapaticci. "Experimental Characterization of Spurious Signals in Magnetic Nanoparticles Enhanced Microwave Imaging of Cancer." Sensors 21, no. 8 (April 16, 2021): 2820. http://dx.doi.org/10.3390/s21082820.

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Magnetic nanoparticles enhanced microwave imaging relies on the capability of modulating the response of such nanocomponents at microwaves by means of a (low frequency) polarizing magnetic field. In medical imaging, this capability allows for the detection and imaging of tumors loaded with nanoparticles. As the useful signal is the one which arises from nanoparticles, it is crucial to remove sources of undesired disturbance to enable the diagnosis of early-stage tumors. In particular, spurious signals arise from instrumental drift, as well as from the unavoidable interaction between the polarizing field and the imaging system. In this paper, we experimentally assess and characterize such spurious effects in order to set the optimal working conditions for magnetic nanoparticles enhanced microwave imaging of cancer. To this end, simple test devices, which include all components typically comprised in a microwave imaging system, have been realized and exploited. The experiment’s results allow us to derive design formulas and guidelines useful for limiting the impact of unwanted magnetic effects, as well as that relative to the instrumental drift on the signal generated by the magnetic nanoparticles-loaded tumor.
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Gao, Dianzhu, Jun Peng, Yunyou Lu, Rui Zhang, Yingze Yang, and Zhiwu Huang. "Sensor Fault Diagnosis of Locomotive Electro-Pneumatic Brake Using an Adaptive Unscented Kalman Filter." Journal of Sensors 2021 (December 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/5407817.

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Normal operation of the pressure sensor is important for the safe operation of the locomotive electro-pneumatic brake system. Sensor fault diagnosis technology facilitates detection of sensor health. However, the strong nonlinearity and variable process noise of the brake system make the sensor fault diagnosis become challenging. In this paper, an adaptive unscented Kalman filter- (UKF-) based fault diagnosis strategy is proposed, aimed at detecting bias faults and drift faults of the equalizing reservoir pressure sensor in the brake system. Firstly, an adaptive UKF based on the Sage-Husa method is applied to accurately estimate the pressure transients in the equalizing reservoir of the brake system. Then, the residual is generated between the estimated pressure by the UKF and the measured pressure by the sensor. Afterwards, the Sequential Probability Ratio Test is used to evaluate the residual so that the incipient and gradual sensor faults can be diagnosed. An experimental prototype platform for diagnosis of the equalizing reservoir pressure control system is constructed to validate the proposed method.
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Rahpoe, N., M. Weber, A. V. Rozanov, K. Weigel, H. Bovensmann, J. P. Burrows, A. Laeng, et al. "Relative drifts and biases between six ozone limb satellite measurements from the last decade." Atmospheric Measurement Techniques Discussions 8, no. 4 (April 10, 2015): 3697–728. http://dx.doi.org/10.5194/amtd-8-3697-2015.

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Abstract. As part of ESA's climate change initiative high vertical resolution ozone profiles from three instruments all aboard ESA's Envisat (GOMOS, MIPAS, SCIAMACHY) in combination with ESA's third party missions (OSIRIS, SMR, ACE-FTS) are to be combined in order to create an essential climate variable data record for the last decade. A prerequisite before combining data is the examination of differences and drifts between the datasets. In this paper, we present a detailed analysis of ozone profile differences based on pairwise collocated measuerements, including the evolution of the differences with time. Such a diagnosis is helpful to identify strengths and weaknesses of each data set that may vary in time and introduce uncertainties in long-term trend estimates. Main results of this paper indicate that the 6 instruments perform well in the stratosphere particularly between 20 and 40 km with a mean relative difference of ±5% (middle latitudes) to ±10% (tropics). Larger differences and variability in the differences are found in the upper troposphere lower stratosphere region and in the mesosphere. The analysis reveals that the relative drift between the sensors is not statistically significant for most pairs of instruments.
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Yang, Liman, Lianming Su, Yixuan Wang, Haifeng Jiang, Xueyao Yang, Yunhua Li, Dongkai Shen, and Na Wang. "Metal Roof Fault Diagnosis Method Based on RBF-SVM." Complexity 2020 (December 3, 2020): 1–12. http://dx.doi.org/10.1155/2020/9645817.

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Metal roof enclosure system is an important part of steel structure construction. In recent years, it has been widely used in large-scale public or industrial buildings such as stadiums, airport terminals, and convention centers. Affected by bad weather, various types of accidents on metal roofs frequently occurred, causing huge property losses and adverse effects. Because of wide span, long service life and hidden fault of metal roof, the manual inspection of metal roof has low efficiency, poor real-time performance, and it is difficult to find hidden faults. On the basis of summarizing the working principle of metal roof and cause of accidents, this paper classifies the fault types of metal roofs in detail and establishes a metal roof monitoring and fault diagnosis system using distributed multisource heterogeneous sensors and Zigbee wireless sensor networks. Monitoring data from strain gauge, laser ranging sensor, and ultrasonic ranging sensor is utilized comprehensively. By extracting time domain feature, the data trend characteristics and correlation characteristics are analyzed and fused to eliminate erroneous data and find superficial faults such as sensor drift and network interruption. Aiming to the hidden faults including plastic deformation and bolt looseness, an SVM fault diagnosis algorithm based on RBF kernel function is designed and applied to diagnose metal roof faults. The experimental results show that the RBF-SVM algorithm can achieve high classification accuracy.
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Kermenov, Renat, Giacomo Nabissi, Sauro Longhi, and Andrea Bonci. "Anomaly Detection and Concept Drift Adaptation for Dynamic Systems: A General Method with Practical Implementation Using an Industrial Collaborative Robot." Sensors 23, no. 6 (March 20, 2023): 3260. http://dx.doi.org/10.3390/s23063260.

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Industrial collaborative robots (cobots) are known for their ability to operate in dynamic environments to perform many different tasks (since they can be easily reprogrammed). Due to their features, they are largely used in flexible manufacturing processes. Since fault diagnosis methods are generally applied to systems where the working conditions are bounded, problems arise when defining condition monitoring architecture, in terms of setting absolute criteria for fault analysis and interpreting the meanings of detected values since working conditions may vary. The same cobot can be easily programmed to accomplish more than three or four tasks in a single working day. The extreme versatility of their use complicates the definition of strategies for detecting abnormal behavior. This is because any variation in working conditions can result in a different distribution of the acquired data stream. This phenomenon can be viewed as concept drift (CD). CD is defined as the change in data distribution that occurs in dynamically changing and nonstationary systems. Therefore, in this work, we propose an unsupervised anomaly detection (UAD) method that is capable of operating under CD. This solution aims to identify data changes coming from different working conditions (the concept drift) or a system degradation (failure) and, at the same time, can distinguish between the two cases. Additionally, once a concept drift is detected, the model can be adapted to the new conditions, thereby avoiding misinterpretation of the data. This paper concludes with a proof of concept (POC) that tests the proposed method on an industrial collaborative robot.
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46

Kafili, D., J. Bashford, and S. Erigadoo. "P062 Agreement between arterial and capillary CO2 level in patients undergoing sleep study for suspected hypoventilation." SLEEP Advances 3, Supplement_1 (October 1, 2022): A50—A51. http://dx.doi.org/10.1093/sleepadvances/zpac029.134.

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Abstract Background Measurement of arterial CO2 (PaCO2) is the gold standard for diagnosis of sleep hypoventilation during polysomnography. However, arterial PaCO2 measurement is a labour-intensive and painful. Previous audit in our unit demonstrated ABGs are often delayed or omitted when requested. Continuous monitoring of transcutaneous carbon dioxide (TcCO2) is often used as a surrogate marker of PaCO2, however caution should be applied in interpreting TcCO2 due to the “sensor drift” phenomenon. Arterialized capillary blood gas (CBG) measurement has been described in various settings and is used in many respiratory units. Concordance with ABG and TcCO2, however, is poorly described in the literature. A retrospective review was performed to assess concordance of arterialized capillary CO2 level (PcCO2) with TcCO2 measured during routine polysomnography. Methods This is a retrospective audit of patients with provisional diagnosis of sleep-related hypoventilation syndrome who attended Sunshine Coast University Hospital Sleep Disorders Centre for overnight sleep studies. Continuous TcCO2 was monitored and arterialized PcCO2 measured at the outset and completion of study. CO2 results will be compared to assess concordance. Where an ABG was performed simultaneously with a CBG the concordance will be reported. Progress to Date Full results to follow. It is anticipated that 30-40 patients will be included. Few ABGs were performed. Anticipated outcome Initial evaluation suggests PcCO2 may be a viable alternative to ABG based on concordance with limited ABG data. PcCO2 may be superior to TcCO2 due to the common phenomenon of TcCO2 “drift”. A prospective study, utilising ABG, CBG and TcCO2 is needed.
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Fan, Chao, Cheng Li, Yanfeng Peng, Yiping Shen, Guanghui Cao, and Sai Li. "Fault Diagnosis of Vibration Sensors Based on Triage Loss Function-Improved XGBoost." Electronics 12, no. 21 (October 29, 2023): 4442. http://dx.doi.org/10.3390/electronics12214442.

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Vibration sensors are prone to bias, drift, and other failures. To avoid misjudgments in state monitoring systems and potential safety accidents caused by vibration sensor failures, it is significant to diagnose the faults of vibration sensors. Existing methods for vibration sensor fault diagnosis are primarily based on Deep Learning, but Extreme Gradient Boosting stands out due to its excellent interpretability, and compared to other ensemble learning algorithms, it boasts superior accuracy and efficiency. Therefore, a vibration sensor fault diagnosis method based on Extreme Gradient Boosting is proposed to diagnose seven common types of faults in vibration sensors. To prevent the model from being overwhelmed by simple negative cases during training, a new loss function named Triage Loss is designed to improve the classifier’s performance. The vibration sensor fault diagnosis has confirmed the efficacy and practicality of the suggested approach. The experimental results indicate that the training of the model done using Triage Loss outperforms the training model done using the default loss function, with a maximum improvement of 5.4% accuracy, 5.45% in the F1-score, and 9.87% in the mean Average Precision under different fault rates.
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Blesa, Joaquim, Joseba Quevedo, Vicenç Puig, Fatiha Nejjari, Raul Zaragoza, and Alejandro Rolán. "Fault Diagnosis and Prognosis of a Brushless DC motor using a Model-based Approach." PHM Society European Conference 5, no. 1 (July 22, 2020): 9. http://dx.doi.org/10.36001/phme.2020.v5i1.1257.

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This paper proposes a model-based fault diagnosis and prognosis approach applied to brushless DC motors (BLDC). The objective is an early detection of mechanical and electrical faults in BLDC motors operating under a variety of operating conditions. The proposed model-based method is based on the evaluation of a set of residuals that are computed taking into account analytical redundancy relations. Fault diagnosis consist of two steps: First, checking if at least one of the residuals is inconsistent with the normal operation of the system. And, second, evaluating the set of the residuals that are inconsistent to determine which fault is present in the system. Fault prognosis consists of the same two steps but instead of considering current inconsistencies evaluates drift deviations from nominal operation to predict futures residual inconsistencies and therefore predict future fault detections and diagnosis. A description of various kinds of mechanical and electrical faults that can occur in a BLDC motor is presented. The performance of the proposed method is illustrated through simulation experiments.
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Elorza, Iker, Iker Arrizabalaga, Aritz Zubizarreta, Héctor Martín-Aguilar, Aron Pujana-Arrese, and Carlos Calleja. "A Sensor Data Processing Algorithm for Wind Turbine Hydraulic Pitch System Diagnosis." Energies 15, no. 1 (December 21, 2021): 33. http://dx.doi.org/10.3390/en15010033.

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Modern wind turbines depend on their blade pitch systems for start-ups, shutdowns, and power control. Pitch system failures have, therefore, a considerable impact on their operation and integrity. Hydraulic pitch systems are very common, due to their flexibility, maintainability, and cost; hence, the relevance of diagnostic algorithms specifically targeted at them. We propose one such algorithm based on sensor data available to the vast majority of turbine controllers, which we process to fit a model of the hydraulic pitch system to obtain significant indicators of the presence of the critical failure modes. This algorithm differs from state-of-the-art, model-based algorithms in that it does not numerically time-integrate the model equations in parallel with the physical turbine, which is demanding in terms of in situ computation (or, alternatively, data transmission) and is highly susceptible to drift. Our algorithm requires only a modest amount of local sensor data processing, which can be asynchronous and intermittent, to produce negligible quantities of data to be transmitted for remote storage and analysis. In order to validate our algorithm, we use synthetic data generated with state-of-the-art aeroelastic and hydraulic simulation software. The results suggest that a diagnosis of the critical wind turbine hydraulic pitch system failure modes based on our algorithm is viable.
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Wang, Xi, Yangming Zhou, Zhikai Zhao, Xiujuan Feng, Zhi Wang, and Mingzhi Jiao. "Advanced Algorithms for Low Dimensional Metal Oxides-Based Electronic Nose Application: A Review." Crystals 13, no. 4 (April 3, 2023): 615. http://dx.doi.org/10.3390/cryst13040615.

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Low-dimensional metal oxides-based electronic noses have been applied in various fields, such as food quality, environmental assessment, coal mine risk prediction, and disease diagnosis. However, the applications of these electronic noses are limited for conditions such as precise safety monitoring because electronic nose systems have problems such as poor recognition ability of mixed gas signals and sensor drift caused by environmental factors. Advanced algorithms, including classical gas recognition algorithms and neural network-based algorithms, can be good solutions for the key problems. Classical gas recognition methods, such as support vector machines, have been widely applied in electronic nose systems in the past. These methods can provide satisfactory results if the features are selected properly and the types of mixed gas are under five. In many situations, this can be challenging due to the drift of sensor signals. In recent years, neural networks have undergone revolutionary changes in the field of electronic noses, especially convolutional neural networks and recurrent neural networks. This paper reviews the principles and performances of typical gas recognition methods of the electronic nose up to now and compares and analyzes the classical gas recognition methods and the neural network-based gas recognition methods. This work can provide guidance for research in related fields.
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