Relevant bibliographies by topics / Incipient fault (IF)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Incipient fault (IF)'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Incipient fault (IF)"

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Deng, Yong, Yibing Shi, and Wei Zhang. "Diagnosis of Incipient Faults in Nonlinear Analog Circuits." Metrology and Measurement Systems 19, no. 2 (January 1, 2012): 203–18. http://dx.doi.org/10.2478/v10178-012-0018-7.

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Diagnosis of Incipient Faults in Nonlinear Analog Circuits Considering the problem to diagnose incipient faults in nonlinear analog circuits, a novel approach based on fractional correlation is proposed and the application of the subband Volterra series is used in this paper. Firstly, the subband Volterra series is calculated from the input and output sequences of the circuit under test (CUT). Then the fractional correlation functions between the fault-free case and the incipient faulty cases of the CUT are derived. Using the feature vectors extracted from the fractional correlation functions, the hidden Markov model (HMM) is trained. Finally, the well-trained HMM is used to accomplish the incipient fault diagnosis. The simulations illustrate the proposed method and show its effectiveness in the incipient fault recognition capability.
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Mharakurwa, Edwell T., G. N. Nyakoe, and A. O. Akumu. "Power Transformer Fault Severity Estimation Based on Dissolved Gas Analysis and Energy of Fault Formation Technique." Journal of Electrical and Computer Engineering 2019 (February 3, 2019): 1–10. http://dx.doi.org/10.1155/2019/9674054.

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Decision making on transformer insulation condition based on the evaluated incipient faults and aging stresses has been the norm for many asset managers. Despite being the extensively applied methodology in power transformer incipient fault detection, solely dissolved gas analysis (DGA) techniques cannot quantify the detected fault severity. Fault severity is the core property in transformer maintenance rankings. This paper presents a fuzzy logic methodology in determining transformer faults and severity through use of energy of fault formation of the evolved gasses during transformer faulting event. Additionally, the energy of fault formation is a temperature-dependent factor for all the associated evolved gases. Instead of using the energy-weighted DGA, the calculated total energy of related incipient fault is used for severity determination. Severity of faults detected by fuzzy logic-based key gas method is evaluated through the use of collected data from several in-service and faulty transformers. DGA results of oil samples drawn from transformers of different specifications and age are used to validate the model. Model results show that correctly detecting fault type and its severity determination based on total energy released during faults can enhance decision-making in prioritizing maintenance of faulty transformers.
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Zhang, Zhao, and Xiao He. "Fault-Structure-Based Active Fault Diagnosis: A Geometric Observer Approach." Energies 13, no. 17 (August 31, 2020): 4475. http://dx.doi.org/10.3390/en13174475.

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Fault diagnosis techniques can be classified into passive and active types. Passive approaches only utilize the original input and output signals of the system. Because of the small amplitudes, the characteristics of incipient faults are not fully represented in the data of the system, so it is difficult to detect incipient faults by passive fault diagnosis techniques. In contrast, active methods can design auxiliary signals for specific faults and inject them into the system to improve fault diagnosis performance. Therefore, active fault diagnosis techniques are utilized in this article to detect and isolate incipient faults based on the fault structure. A new framework based on observer approach for active fault diagnosis is proposed and the geometric approach based fault diagnosis observer is introduced to active fault diagnosis for the first time. Based on the dynamic equations of residuals, auxiliary signals are designed to enhance the diagnosis performance for incipient faults that have specific structures. In addition, the requirements that auxiliary signals need to meet are discussed. The proposed method can realize the seamless combination of active fault diagnosis and passive fault diagnosis. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed approach, and it is indicated that the proposed method significantly improves the accuracy of the diagnosis for incipient faults.
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Zhang, Ge, Qiong Yang, Guotong Li, Jiaxing Leng, and Long Wang. "A Satellite Incipient Fault Detection Method Based on Local Optimum Projection Vector and Kullback-Leibler Divergence." Applied Sciences 11, no. 2 (January 15, 2021): 797. http://dx.doi.org/10.3390/app11020797.

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Timely and effective detection of potential incipient faults in satellites plays an important role in improving their availability and extending their service life. In this paper, the problem of detecting incipient faults using projection vector (PV) and Kullback-Leibler (KL) divergence is studied in the context of detecting incipient faults in satellites. Under the assumption that the variables obey a multidimensional Gaussian distribution and using KL divergence to detect incipient faults, this paper models the optimum PV for detecting incipient faults as an optimization problem. It proves that the PVs obtained by principal component analysis (PCA) are not necessarily the optimum PV for detecting incipient faults. It then compares the on-line probability density function (PDF) with the reference PDF for detecting incipient faults on the local optimum PV. A numerical example and a real satellite fault case were used to assess the validity and superiority of the method proposed in this paper over conventional methods. Since the method takes into account the characteristics of the actual incipient faults, it is more adaptable to various possible incipient faults. Fault detection rates of three simulated faults and the real satellite fault are 98%, 84%, 93% and 92%, respectively.
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Chen, Hongtian, Bin Jiang, and Ningyun Lu. "Data-Driven Incipient Sensor Fault Estimation with Application in Inverter of High-Speed Railway." Mathematical Problems in Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/8937356.

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Incipient faults in high-speed railway have been rarely considered before developing into faults or failures. In this paper, a new data-driven incipient fault estimate (FE) methodology is proposed under multivariate statistics frame, which incorporates with Kullback-Leibler divergence (KLD) in information domain and neural network approximation in machine learning. By defining one sensitive fault indicator (SFI), the incipient fault amplitude can be precisely estimated. According to the experimental platform of China Railway High-speed 2 (CRH2), the proposed incipient FE algorithm is examined, and the more sensitivity and accuracy to tiny abnormality are demonstrated. Followed by the incipient FE results, several factors on FE performance are further analyzed.
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Wang, Manran, Li Guo, Jinhao Chen, Bingqi Lv, and Yang Yang. "Superresolution Reconstruction of Electrical Equipment Incipient Fault." Journal of Control Science and Engineering 2018 (August 7, 2018): 1–11. http://dx.doi.org/10.1155/2018/1630402.

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With the rapid development of industry and technology, the electrical power system becomes more complex and the electrical equipment becomes more diverse. Defective equipment is often the cause of industrial accidents and electrical injuries, which can result in serious injuries, such as electrocution, burns, and electrical shocks. In some cases, electrical equipment fault may result in death. However, in some special situation, some fault is very small even invisible, such as equipment aging, holes, and cracks, so the detection of these incipient faults is difficult or even impossible. These potential incipient faults become the biggest hidden danger in the electrical equipment and electricity power system. For these reasons, this paper proposes a superresolution reconstruction method for electrical equipment incipient fault to ensure complete detection in electrical equipment, which aims to guarantee the security of electrical power system operation and industry production. Experimental results show that this method can get a state-of-the-art reconstruction effect of incipient fault, so as to provide reliable fault detection of electrical power system.
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Shi, Huaitao, Jin Guo, Zhe Yuan, Zhenpeng Liu, Maxiao Hou, and Jie Sun. "Incipient Fault Detection of Rolling Element Bearings Based on Deep EMD-PCA Algorithm." Shock and Vibration 2020 (October 26, 2020): 1–17. http://dx.doi.org/10.1155/2020/8871433.

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Due to the relatively weak early fault characteristics of rolling bearings, the difficulty of early fault detection increases. For unsolving this problem, an incipient fault detection method based on deep empirical mode decomposition and principal component analysis (Deep EMD-PCA) is proposed. In this method, multiple data processing layers are created to extract weak incipient fault features, and EMD is used to decompose the vibration signal. This method establishes an accurate data mode, which can improve the incipient fault detection capability. It overcomes the difficulties of incipient fault detection, in which weak fault features can be extracted from the background of strong noise. From a theoretical point of view, this paper proves that the Deep EMD-PCA method can retain more variance information and has a good early fault detection ability. The experiment results indicate that the detection rate of Deep EMD-PCA is about 85%, and the failure detection delay time is almost zero. The incipient faults of rolling element bearings can be detected accurately and timely by Deep EMD-PCA. The method effectively improves the accuracy and timeliness of fault detection under actual working conditions and has good practical application value.
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Hoang, Ngoc-Bach, and Hee-Jun Kang. "Incipient wheel fault identification in mobile robots using neural networks and nonlinear least squares." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 3 (August 9, 2016): 446–58. http://dx.doi.org/10.1177/0954406215616650.

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In this paper, we present a novel method for fault identification in the case of an incipient wheel fault in mobile robots. First, a three-layer neural networks is established to estimate the deviation of the robot dynamics due to the process fault. The estimate of the faulty dynamic model is based on a combination of the nominal dynamic model and the neural network output. Then, by replacing the faulty dynamic model with its estimate value, the primary estimates of the wheel radius appear as the solutions of two quadratic equations. Next, a simple and efficient way to perform these primary estimate selections is proposed in order to eliminate undesired primary estimates. A recursive nonlinear least squares is applied in order to obtain a smooth estimate of the wheel radius. Two computer simulation examples using Matlab/Simulink show that the proposed method is very effective for incipient fault identification in the setting of both left and right wheel faults.
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Xia, Jingping, Bin Jiang, Ke Zhang, and Jinfa Xu. "Robust Fault Diagnosis Design for Linear Multiagent Systems with Incipient Faults." Mathematical Problems in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/436935.

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The design of a robust fault estimation observer is studied for linear multiagent systems subject to incipient faults. By considering the fact that incipient faults are in low-frequency domain, the fault estimation of such faults is proposed for discrete-time multiagent systems based on finite-frequency technique. Moreover, using the decomposition design, an equivalent conclusion is given. Simulation results of a numerical example are presented to demonstrate the effectiveness of the proposed techniques.
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Li, Fu Cai, Jin Chen, Gui Cai Zhang, and Zheng Jia He. "Wavelet Transform Domain Filter and Its Application in Incipient Fault Prognosis." Key Engineering Materials 293-294 (September 2005): 127–34. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.127.

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Noise is the biggest obstacle that makes the incipient fault prognosis results uncorrected. According to the theories of correlation analysis and threshold de-noising by wavelets, wavelet transform domain filter (WTDF) is constructed. WTDF is an iterative process. By selecting the process parameters adaptively, WTDF can de-noise signal efficiently. More important, the faint component in the signal will become stronger. WTDF method is used to analyze the signals collected from a bearing that has incipient unbalance and misalignment faults. Results show that WTDF is effective for bearing incipient fault prognosis.
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Dissertations / Theses on the topic "Incipient fault (IF)"

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Jaafari, Mousavi Mir Rasoul. "Underground distribution cable incipient fault diagnosis system." Texas A&M University, 2005. http://hdl.handle.net/1969.1/4675.

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This dissertation presents a methodology for an efficient, non-destructive, and online incipient fault diagnosis system (IFDS) to detect underground cable incipient faults before they become catastrophic. The system provides vital information to help the operator with the decision-making process regarding the condition assessment of the underground cable. It incorporates advanced digital signal processing and pattern recognition methods to classify recorded data into designated classes. Additionally, the IFDS utilizes novel detection methodologies to detect when the cable is near failure. The classification functionality is achieved through employing an ensemble of rule-based and supervised classifiers. The Support Vector Machines, designed and used as a supervised classifier, was found to perform superior. In addition to the normalized energy features computed from wavelet packet analysis, two new features, namely Horizontal Severity Index, and Vertical Severity Index are defined and used in the classification problem. The detection functionality of the IFDS is achieved through incorporating a temporal severity measure and a detection method. The novel severity measure is based on the temporal analysis of arrival times of incipient abnormalities, which gives rise to a numeric index called the Global Severity Index (GSI). This index portrays the progressive degradation path of underground cable as catastrophic failure time approaches. The detection approach utilizes the numerical modeling capabilities of SOM as well as statistical change detection techniques. The natural logarithm of the chronologically ordered minimum modeling errors, computed from exposing feature vectors to a trained SOM, is used as the detection index. Three modified change detection algorithms, namely Cumulative Sum, Exponentially Weighted Moving Averages, and Generalized Likelihood Ratio, are introduced and applied to this application. These algorithms determine the change point or near failure time of cable from the instantaneous values of the detection index. Performance studies using field recorded data were conducted at three warning levels to assess the capability of the IFDS in predicting the faults that actually occurred in the monitored underground cable. The IFDS presents a high classification rate and satisfactory detection capability at each warning level. Specifically, it demonstrates that at least one detection technique successfully provides an early warning that a fault is imminent.
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Zhou, Wei. "Incipient Bearing Fault Detection for Electric Machines Using Stator Current Noise Cancellation." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19706.

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The objective of this research is to develop a bearing fault detection scheme for electric machines via stator current. A new method, called the stator current noise cancellation method, is proposed to separate bearing fault-related components in the stator current. This method is based on the concept of viewing all bearing-unrelated components as noise and defining the bearing detection problem as a low signal-to-noise ratio (SNR) problem. In this method, a noise cancellation algorithm based on Wiener filtering is employed to solve the problem. Furthermore, a statistical method is proposed to process the data of noise-cancelled stator current, which enables bearing conditions to be evaluated solely based on stator current measurements. A detailed theoretical analysis of the proposed methods is presented. Several online tests are also performed in this research to validate the proposed methods. It is shown in this work that a bearing fault can be detected by measuring the variation of the RMS of noise-cancelled stator current by using statistical methods such as the Statistical Process Control. In contrast to most existing current monitoring techniques, the detection methods proposed in this research are designed to detect generalized-roughness bearing faults. In addition, the information about machine parameters and bearing dimensions are not required in the implementation.
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Zhang, Xiaoxia. "Incipient anomaly detection and estimation for complex system health monitoring." Electronic Thesis or Diss., université Paris-Saclay, 2020. http://www.theses.fr/2020UPASG025.

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La détection et le diagnostic des défauts naissants pour les systèmes d’ingénierie ou industriels multivariés à bruit élevé sont abordés dans ce travail de thèse par l’intermédiare d’une approche statistique non paramétrique ’globale’.Un défaut naissant induit un changement anormal dans les valeurs mesurées de la variable du système. Cependant, un tel changement est faible, et tend à ne pas causer de changements évidents dans les paramètres des distributions des signaux du système. En particulier dans un environnement bruité, les caractéristiques de ces défaults faible peuvent être masquées par le bruit et rend celui-ci difficile à évaluer. Dans une telle situation, l’utilisation de méthodes paramétriques traditionnelles pour la détection échouent. Pour faire face à ces difficultés et effectuer la détection et le diagnostic des défauts, une approche’globale’ qui peut prendre en compte la signature totale des défauts est nécessaire. La détection de défauts naissants peut être obtenue par la mesure des différences entre les distributions avant et après l’apparition du défaut. Certaines méthodes basées sur la distribution (dites ’globales’) ont été proposées, mais les performances de détection de ces approches existantes dans un environnement à haut niveau de bruit devraient être améliorées. Dans ce contexte, la divergence de Jensen-Shannon est considérée comme un indicateur de défaut ’global’ pour effectuer la détection et le diagnostic de défaut naissant dans un environnement à haut niveau de bruit. Ses performances de détection pour de petites variations anormales noyées dans le bruit sont validés en simulation. En outre, le problème de l’estimation des défauts est également étudié dans ce travail. Un modèle théorique d’estimation de la sévérité des défauts à parti dépend de la valeur de la divergence pour des conditions Gaussiennes est établi. La précision du modèle d’estimation est évaluée sur des modèles numériques par le biais de simulations. Ensuite, l’approche statistique ’globale’ est mise en oeuvre pour à deux applications dans le domaine de l’ingénierie. La première concerne la détection de fissures naissantes dans un matériau conducteur. La divergence de Jensen-Shannon combinée à l’analyse en composantes indépendantes et à la décomposition on ondelettes a été appliquée à la détection et à la caractérisation de fissures mineures dans des structures conductrices avec des perturbations bruit sur la base de signaux d’impédance expérimentaux. La deuxième application concerne le diagnostic de défauts naissants dans un processus non linéaire multivarié avec un bruit élevé. Le ’Tennessee Eastman Process’ (TEP) est un processus non linéaire multivarié typique pour lequel nous avons appliqué, la divergence de Jensen-Shannon combinée à l’analyse en composantes principales à noyau (ACPN) est pour étudier la détection de défauts naissants dont les difficultés de sont largement décrites dans la littérature
Incipient fault detection and diagnosis in engineering and multivariate industrial systems with a high-level noise are addressed in this Ph.D. thesis by a ’global’ non-parametric statistical approach. An incipient fault is supposed to induce an abnormal change in the measured value of the system variable. However, such change is weak, and it tends not to cause obvious changes in the signal distribution’s parameters. Especially in high noise level environment, the weak fault feature can be masked by the noise and becomes unpredictable. In such a condition, using traditional parametric-based methods generally fails in the fault detection. To cope with incipient fault detection and diagnosis, a ’global’ approach that can consider the total faults signature is needed. The incipient fault detection can be obtained by measuring the differences between the signal distributions before and after the fault occurrence. Some distribution-based ’global’ methods have been proposed, however, the detection capabilities of these existed approaches in high noise level environment should be improved. In this context, Jensen-Shannon divergence is considered a ’global’ fault indicator to deal with the incipient fault detection and diagnosis in a high noise level environment. Its detection performance for small abnormal variations hidden in noise is validated through simulation. In addition, the fault estimation problem is also considered in this work. A theoretical fault severity estimation model depending on the divergence value for the Gaussian condition is derived. The accuracy of the estimation model is evaluated on numerical models through simulations. Then, the ’global’ statistical approach is applied to two applications in engineering. The first one relates to non- destruction incipient cracks detection. The Jensen-Shannon divergence combined with Noisy Independent Component Analysis and Wavelet analysis was applied for detection and characterization of minor cracks in conductive structures with high-level perturbations based on experimental impedance signals. The second application addresses the incipient fault diagnosis in a multivariate non-linear process with a high-level noise. Tennessee Eastman Process (TEP) is one typical multivariate non-linear process, the Jensen-Shannon divergence in the Kernel Principal Component Analysis (KPCA) is developed for coping with incipient fault detection in this process
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Bade, Rajesh Kumar. "Analysis of incipient fault signatures in inductive loads energized by a common voltage bus." Texas A&M University, 2004. http://hdl.handle.net/1969.1/3095.

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Recent research has demonstrated the use of electrical signature analysis (ESA), that is, the use of induction motor currents and voltages, for early detection of motor faults in the form of embedded algorithms. In the event of multiple motors energized by a common voltage bus, the cost of installing and maintaining fault monitoring and detection devices on each motor may be avoided, by using bus level aggregate electrical measurements to assess the health of the entire population of motors. In this research an approach for detecting commonly encountered induction motor mechanical faults from bus level aggregate electrical measurements is investigated. A mechanical fault indicator is computed processing the raw electrical measurements through a series of signal processing algorithms. Inference of an incipient fault is made by the percentage relative change of the fault indicator from the “healthy” baseline, thus defining a Fault Indicator Change (FIC). To investigate the posed research problem, healthy and faulty motors with broken rotor bar faults are simulated using a detailed transient motor model. The FIC based on aggregate electrical measurements is studied through simulations of different motor banks containing the same faulty motor. The degradation in the FIC when using aggregate measurements, as compared to using individual motor measurements, is investigated. For a given motor bank configuration, the variation in FIC with increasing number of faulty motors is also studied. In addition to simulation studies experimental results from a two-motor setup are analyzed. The FIC and degradation in the FIC in the case of load eccentricity fault, and a combination of shaft looseness and bearing damage is studied through staged fault experiments in the laboratory setup. In this research, the viability of using bus level aggregate electrical measurements for detecting incipient faults in motors energized by a common voltage bus is demonstrated. The proposed approach is limited in that as the power rating fraction of faulty motors to healthy motors in a given configuration decreases, it becomes far more difficult to detect the presence of incipient faults at very early stages.
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Harmouche, Jinane. "Statistical Incipient Fault Detection and Diagnosis with Kullback-Leibler Divergence : from Theory to Applications." Thesis, Supélec, 2014. http://www.theses.fr/2014SUPL0022/document.

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Les travaux de cette thèse portent sur la détection et le diagnostic des défauts naissants dans les systèmes d’ingénierie et industriels, par des approches statistiques non-paramétriques. Un défaut naissant est censé provoquer comme tout défaut un changement anormal dans les mesures des variables du système. Ce changement est imperceptible mais aussi imprévisible dû à l’important rapport signal-sur défaut, et le faible rapport défaut-sur-bruit caractérisant le défaut naissant. La détection et l’identification d’un changement général nécessite une approche globale qui prend en compte la totalité de la signature des défauts. Dans ce cadre, la divergence de Kullback-Leibler est proposée comme indicateur général de défauts, sensible aux petites variations anormales cachées dans les variations du bruit. Une approche d’analyse spectrale globale est également proposée pour le diagnostic de défauts ayant une signature fréquentielle. L’application de l’approche statistique globale est illustrée sur deux études différentes. La première concerne la détection et la caractérisation, par courants de Foucault, des fissures dans les structures conductrices. La deuxième application concerne le diagnostic des défauts de roulements dans les machines électriques tournantes. En outre, ce travail traite le problème d’estimation de l’amplitude des défauts naissants. Une analyse théorique menée dans le cadre d’une modélisation par analyse en composantes principales, conduit à un modèle analytique de la divergence ne dépendant que des paramètres du défaut
This phD dissertation deals with the detection and diagnosis of incipient faults in engineering and industrial systems by non-parametric statistical approaches. An incipient fault is supposed to provoke an abnormal change in the measurements of the system variables. However, this change is imperceptible and also unpredictable due to the large signal-to-fault ratio and the low fault-to-noise ratio characterizing the incipient fault. The detection and identification of a global change require a ’global’ approach that takes into account the total faults signature. In this context, the Kullback-Leibler divergence is considered to be a ’global’ fault indicator, which is recommended sensitive to abnormal small variations hidden in noise. A ’global’ spectral analysis approach is also proposed for the diagnosis of faults with a frequency signature. The ’global’ statistical approach is proved on two application studies. The first one concerns the detection and characterization of minor cracks in conductive structures. The second application concerns the diagnosis of bearing faults in electrical rotating machines. In addition, the fault estimation problem is addressed in this work. A theoretical study is conducted to obtain an analytical model of the KL divergence, from which an estimate of the amplitude of the incipient fault is derived
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Bole, Brian McCaslyn. "Load allocation for optimal risk management in systems with incipient failure modes." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50394.

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The development and implementation challenges associated with a proposed load allocation paradigm for fault risk assessment and system health management based on uncertain fault diagnostic and failure prognostic information are investigated. Health management actions are formulated in terms of a value associated with improving system reliability, and a cost associated with inducing deviations from a system's nominal performance. Three simulated case study systems are considered to highlight some of the fundamental challenges of formulating and solving an optimization on the space of available supervisory control actions in the described health management architecture. Repeated simulation studies on the three case-study systems are used to illustrate an empirical approach for tuning the conservatism of health management policies by way of adjusting risk assessment metrics in the proposed health management paradigm. The implementation and testing of a real-world prognostic system is presented to illustrate model development challenges not directly addressed in the analysis of the simulated case study systems. Real-time battery charge depletion prediction for a small unmanned aerial vehicle is considered in the real-world case study. An architecture for offline testing of prognostics and decision making algorithms is explained to facilitate empirical tuning of risk assessment metrics and health management policies, as was demonstrated for the three simulated case study systems.
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Wang, Zhenyuan. "Artificial Intelligence Applications in the Diagnosis of Power Transformer Incipient Faults." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/28594.

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This dissertation is a systematic study of artificial intelligence (AI) applications for the diagnosis of power transformer incipient fault. The AI techniques include artificial neural networks (ANN, or briefly neural networks - NN), expert systems, fuzzy systems and multivariate regression. The fault diagnosis is based on dissolved gas-in-oil analysis (DGA). A literature review showed that the conventional fault diagnosis methods, i.e. the ratio methods (Rogers, Dornenburg and IEC) and the key gas method, have limitations such as the "no decision" problem. Various AI techniques may help solve the problems and present a better solution. Based on the IEC 599 standard and industrial experiences, a knowledge-based inference engine for fault detection was developed. Using historical transformer failure data from an industrial partner, a multi-layer perceptron (MLP) modular neural network was identified as the best choice among several neural network architectures. Subsequently, the concept of a hybrid diagnosis was proposed and implemented, resulting in a combined neural network and expert system tool (the ANNEPS system) for power transformer incipient diagnosis. The abnormal condition screening process, as well as the principle and algorithms of combining the outputs of knowledge based and neural network based diagnosis, were proposed and implemented in the ANNEPS. Methods of fuzzy logic based transformer oil/paper insulation condition assessment, and estimation of oil sampling interval and maintenance recommendations, were also proposed and implemented. Several methods of power transformer incipient fault location were investigated, and a 7Ã 21Ã 5 MLP network was identified as the best choice. Several methods for on-load tap changer (OLTC) coking diagnosis were also investigated, and a MLP based modular network was identified as the best choice. Logistic regression analysis was identified as a good auditor in neural network input pattern selection processes. The above results can help developing better power transformer maintenance strategies, and serve as the basis of on-line DGA transformer monitors.
Ph. D.
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Avram, Remus C. "A UNIFIED NONLINEAR ADAPTIVE APPROACH FOR THE FAULT DIAGNOSIS OF AIRCRAFT ENGINES." Wright State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=wright1332784433.

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Almalki, Mishrari Metab. "CLASSIFICATION OF HIGH IMPEDANCE FAULTS, INCIPIENT FAULTS AND CIRCUIT BREAKER RESTRIKES DURING CAPACITOR BANK DE-ENERGIZATION IN RADIAL DISTRIBUTION FEEDERS." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1524.

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Monitoring of abnormal events in a distribution feeder by using a single technique is a challenging task. Many abnormal events can cause unsafe operation, including a high impedance fault (HIF) caused by a downed conductor touch ground surface, an incipient fault (IF) caused by partial breakdown to a cable insulation, and a circuit breaker (CB) malfunction due to capacitor bank de-energization to cause current restrikes. These abnormal events are not detectable by conventional protection schemes. In this dissertation, a new technique to identify distribution feeder events is proposed based on the complex Morlet wavelet (CMW) and on a decision tree (DT) classifier. First, the event is detected using CMW. Subsequently, a DT using event signatures classifies the event as normal operation, continuous and non-continuous arcing events (C.A.E. and N.C.A.E.). Additional information from the supervisory control and data acquisition (SCADA) can be used to precisely identify the event. The proposed method is meticulously tested on the IEEE 13- and IEEE 34-bus systems and has shown to correctly classify those events. Furthermore, the proposed method is capable of detecting very high impedance incipient faults (IFs) and CB restrikes at the substation level with relatively short detection time. The proposed method uses only current measurements at a low sampling rate of 1440 Hz yielding an improvement of existing methods that require much higher sampling rates.
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Cooke, Thomas Arthur. "High Impedance Arc Fault Detection in a Manhole Environment." Digital Commons @ East Tennessee State University, 2010. https://dc.etsu.edu/etd/1767.

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The scope of this thesis was to develop a prototype high-impedance arc detection system that a utility worker could use as an early warning system while working in a manhole environment. As part of this system sensors and algorithms were developed to increase the sensitivity of detecting an arc while ignoring loads that can give false positive signatures for arcing. The latest technology was used to repeat measurements performed in previous research from decades ago that lacked in sampling speed and amplitude resolution. Several types of arcs were produced and analyzed so to establish a library of various waveform and frequency signatures. The system was constructed as a development unit and is currently gathering information in the field. Data being collected will be analyzed so future revisions will give higher confidence levels of arc detection. Other future plans involve designing a more compact and portable unit.
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Books on the topic "Incipient fault (IF)"

1

Anderson, W. E. Final report: Technical contributions to the development of incipient fault detection, location instrumentation. Gaithersburg, Md: U.S. Department of Commerce, National Bureau of Standards, 1986.

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Anderson, W. E. Final report: Technical contributions to the development of incipient fault detection, location instrumentation. Gaithersburg, Md: U.S. Department of Commerce, National Bureau of Standards, 1986.

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Methodologies of using neural network and fuzzy logic technologies for motor incipient fault detection. Singapore: World Scientific, 1997.

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4

United States. National Aeronautics and Space Administration., ed. Final report of incipient fault detection study for advanced spacecraft systems. [Austin, Texas]: Tracor Applied Sciences, Inc., 1988.

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United States. National Aeronautics and Space Administration., ed. Advanced power system protection and incipient fault detection and protection of spaceborne power systems. College Station, Texas: [Washington, DC, 1989.

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Book chapters on the topic "Incipient fault (IF)"

1

Li, Le-yao, Xin-min Wang, and Ling-xia Mu. "Incipient Fault Detection for a Hypersonic Scramjet Vehicle." In Proceedings of the First Symposium on Aviation Maintenance and Management-Volume I, 31–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54236-7_4.

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Wagh, Nandkumar, and Dinesh Deshpande. "Transformer Incipient Fault Diagnosis Using Artificial Neural Network." In Communications in Computer and Information Science, 453–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25734-6_74.

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Whearty, James J., Thomas K. Rockwell, and Gary H. Girty. "Incipient Pulverization at Shallow Burial Depths Along the San Jacinto Fault, Southern California." In Fault Zone Dynamic Processes, 1–20. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119156895.ch1.

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Nakhaeinejad, Mohsen, Jaewon Choi, and Michael D. Bryant. "Model-Based Diagnostics and Fault Assessment of Induction Motors with Incipient Faults." In Rotating Machinery, Structural Health Monitoring, Shock and Vibration, Volume 5, 439–49. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9428-8_37.

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Pandey, Shashikant, P. Sateesh Kumar, M. Amarnath, Teki Tanay Kumar, and Paladugu Rakesh. "Incipient Fault Detection in Roller Bearing Using Ultrasonic Diagnostic Technique." In Lecture Notes in Mechanical Engineering, 243–51. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5151-2_23.

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Li, Fucai, Jin Chen, Gui Cai Zhang, and Zhengjia He. "Wavelet Transform Domain Filter and Its Application in Incipient Fault Prognosis." In Damage Assessment of Structures VI, 127–34. Stafa: Trans Tech Publications Ltd., 2005. http://dx.doi.org/10.4028/0-87849-976-8.127.

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Lee, Tsair-Fwu, Ming-Yuan Cho, Chin-Shiuh Shieh, Hong-Jen Lee, and Fu-Min Fang. "Particle Swarm Optimization-Based SVM for Incipient Fault Classification of Power Transformers." In Lecture Notes in Computer Science, 84–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11875604_11.

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Lee, Tsair-Fwu, Ming-Yuan Cho, Chin-Shiuh Shieh, Hong-Jen Lee, and Fu-Min Fang. "Diagnosis of Incipient Fault of Power Transformers Using SVM with Clonal Selection Algorithms Optimization." In Lecture Notes in Computer Science, 580–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11875604_65.

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Cai, Shihao, Jianfeng Qu, Zeping Wang, and Chunyan Li. "Feature Extraction of Rolling Bearing Incipient Fault Using an Improved SCA-Based UBSS Method." In Advances in Intelligent Systems and Computing, 994–1002. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00214-5_122.

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Gomez, Maria Jesus, Cristina Castejon, and Juan Carlos Garcia-Prada. "Incipient Fault Detection in Bearings Through the use of WPT Energy and Neural Networks." In Lecture Notes in Mechanical Engineering, 63–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39348-8_4.

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Conference papers on the topic "Incipient fault (IF)"

1

Zhao, Zhen, Fuli Wang, Mingxing Jia, and Shu Wang. "Probabilistic fault prediction of incipient fault." In 2010 Chinese Control and Decision Conference (CCDC). IEEE, 2010. http://dx.doi.org/10.1109/ccdc.2010.5498474.

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Chunhong, Xu, Li Juan, Zhang Peng, Mu Li-Jun, and Si Xiao-Sheng. "ESO-based fault diagnosis and fault-tolerant for incipient actuator faults." In 2013 25th Chinese Control and Decision Conference (CCDC). IEEE, 2013. http://dx.doi.org/10.1109/ccdc.2013.6561718.

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Loboda, Igor, Sergey Yepifanov, and Yakov Feldshteyn. "An Integrated Approach to Gas Turbine Monitoring and Diagnostics." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-51449.

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This paper presents an investigation of a conventional gas turbine diagnostic process and its generalization. A usual sequence of diagnostic actions consists of two stages: monitoring (fault detection) and subsequent proper diagnosis (fault identification). Such an approach neither implies fault identification nor uses the information about incipient faults unless the engine is recognized as faulty. In previous investigations for engine steady state operation conditions we addressed diagnostics problems without their relation with the monitoring process. Fault classes were given by samples of patterns generated by a static gas turbine performance model. This fault simulation took into account faults of varying severity including incipient ones. A diagnostic algorithm employed artificial neural networks to identify an actual fault. In the present paper we consider the monitoring and diagnosis as joint processes extending our previous approach over both of them. It is proposed to form two classes for the monitoring using the above-mentioned classes constructed for the diagnosis. A two-shaft industrial gas turbine has been chosen to test the proposed integrated approach to monitoring and diagnosis. A general recommendation following from the presented investigation is to identify faults simultaneously with fault detection. This permits accumulating preliminary diagnoses before the engine faulty condition is detected and a rapid final diagnosis after the fault detection.
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Menon, Sunil, O¨nder Uluyol, Kyusung Kim, and Emmanuel O. Nwadiogbu. "Incipient Fault Detection and Diagnosis in Turbine Engines Using Hidden Markov Models." In ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38589.

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Incipient fault detection and diagnosis in turbine engines is key to effective maintenance and improved availability of systems dependent on these engines. In this paper, we present a novel method for incipient fault detection and diagnosis using Hidden Markov Models (HMMs). In particular, we focus on engine faults that are manifest in transient operating conditions such as engine startup and acceleration. HMMs are stochastic signal models that are effective in modeling transient signals. They are developed with engine data collected under nominal operating conditions. Engine data representing different fault conditions are used to develop the fault HMMs; a separate model is developed for each of the faults. Once the nominal and fault HMMs are developed, new engine data collected from the engine are evaluated against the HMMs and a determination is made whether a fault is indicated. Here, we demonstrate our HMM-based fault detection and diagnosis approach on engine speed profiles taken from a real engine. Further, the effectiveness of the HMM-based approach is compared with a neural-network-based approach and a method based on using principal component analysis in conjunction with a neural network approach.
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Parlangeli, G., D. Pacella, and M. L. Corradini. "Fault identification and accommodation for incipient and abrupt faults." In 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434480.

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Escobet, T., V. Puig, J. Quevedo, and D. Garcia. "A methodology for incipient fault detection." In 2014 IEEE Conference on Control Applications (CCA). IEEE, 2014. http://dx.doi.org/10.1109/cca.2014.6981336.

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Yang, Shuming, Xiaoyu Wen, and Xiaofei Zhang. "Test Configuration for Incipient Fault Detection." In 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016). Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/icence-16.2016.86.

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Parlangeli, G., D. Pacella, and M. L. Corradini. "Unambiguous fault identification and accommodation for incipient and abrupt faults." In 2007 Mediterranean Conference on Control & Automation. IEEE, 2007. http://dx.doi.org/10.1109/med.2007.4433863.

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Fair, Martene, and Stephen L. Campbell. "Active incipient fault detection with more than two simultaneous faults." In 2009 IEEE International Conference on Systems, Man and Cybernetics - SMC. IEEE, 2009. http://dx.doi.org/10.1109/icsmc.2009.5346202.

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Zhang, Xin, Yingping Sang, Jiusun Zeng, and Zhenwei Huang. "Incipient fault detection based on empirical likelihood." In 2014 26th Chinese Control And Decision Conference (CCDC). IEEE, 2014. http://dx.doi.org/10.1109/ccdc.2014.6852726.

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