Academic literature on the topic 'Fault diagnosis alarms'
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Journal articles on the topic "Fault diagnosis alarms"
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Wei, Lu, Zheng Qian, Yan Pei, and Jingyue Wang. "Wind Turbine Fault Diagnosis by the Approach of SCADA Alarms Analysis." Applied Sciences 12, no. 1 (December 22, 2021): 69. http://dx.doi.org/10.3390/app12010069.
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Wind farm operators are overwhelmed by a large amount of supervisory control and data acquisition (SCADA) alarms when faults occur. This paper presents an online root fault identification method for SCADA alarms to assist operators in wind turbine fault diagnosis. The proposed method is based on the similarity analysis between an unknown alarm vector and the feature vectors of known faults. The alarm vector is obtained from segmented alarm lists, which are filtered and simplified. The feature vector, which is a unique signature representing the occurrence of a fault, is extracted from the alarm lists belonging to the same fault. To mine the coupling correspondence between alarms and faults, we define the weights of the alarms in each fault. The similarities is measured by the weighted Euclidean distance and the weighted Hamming distance, respectively. One year of SCADA alarms and maintenance records are used to verify the proposed method. The results show that the performance of the weighted Hamming distance is better than that of the weighted Euclidean distance; 84.1% of alarm lists are labeled with the right root fault.
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Kim, Kyusung, and Alexander G. Parlos. "Reducing the Impact of False Alarms in Induction Motor Fault Diagnosis." Journal of Dynamic Systems, Measurement, and Control 125, no. 1 (March 1, 2003): 80–95. http://dx.doi.org/10.1115/1.1543550.
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Early detection and diagnosis of incipient faults is desirable for on-line condition assessment, product quality assurance, and improved operational efficiency of induction motors. At the same time, reducing the probability of false alarms increases the confidence of equipment owners in this new technology. In this paper, a model-based fault diagnosis system recently proposed by the authors for induction motors is experimentally compared for fault detection and false alarm performance with a more traditional signal-based motor fault estimator. In addition to the nameplate information required for the initial set-up, the proposed model-based fault diagnosis system uses measured motor terminal currents and voltages, and motor speed. The motor model embedded in the diagnosis system is empirically obtained using dynamic recurrent neural networks, and the resulting residuals are processed using wavelet packet decomposition. The effectiveness of the model-based diagnosis system in detecting the most widely encountered motor electrical and mechanical faults, while minimizing the impact of false alarms resulting from power supply and load variations, is demonstrated through extensive testing with staged motor faults. The model-based fault diagnosis system is scalable to motors of different power ratings and it has been successfully tested with fault data from 2.2kW,373kW, and 597kW induction motors.
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Ding, Wei, Qing Chen, Yuzhan Dong, and Ning Shao. "Fault Diagnosis Method of Intelligent Substation Protection System Based on Gradient Boosting Decision Tree." Applied Sciences 12, no. 18 (September 7, 2022): 8989. http://dx.doi.org/10.3390/app12188989.
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In order to improve the efficiency of the devices’ fault diagnosis of the protection systems of intelligent substation, a fault diagnosis method based on a gradient boosting decision tree (GBDT) was proposed. Using the integrated alarm information, the device self-checking information, the link information of generic object-oriented substation event (GOOSE) and sampled value (SV) and the sampling value information generated during the fault of the protection system, the fault feature information set is constructed. According to different fault characteristics, the protection system faults are classified into simple faults and complex faults to improve the diagnosis efficiency. Using GBDT training rules, a fault diagnosis model of protection system based on GBDT is established and fault diagnosis steps are given. This study takes a 110 kV intelligent substation in southern China as an example, to verify the effectiveness and accuracy of the proposed fault diagnosis method, and compared it with the existing methods in terms of the accuracy. The diagnostic accuracy in the case of false alarms and the case of multiple faults are verified. The results show that the method can meet the practical engineering application.
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Liu, Pan, Xing Ming Li, and Jian Wu. "A New Algorithm for the Fuzziness of Alarms in Network Faults Diagnosis." Applied Mechanics and Materials 198-199 (September 2012): 1539–44. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1539.
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The alarm correlation analysis based on fuzzy association rules mining is the popular and cutting-edge field of the network fault diagnosis research. In the application environment of alarms in communication networks, a new algorithm of the fuzziness of alarms which is called FKMA (Fuzzy K-Means of Alarms algorithm) is proposed .During the process of fuzziness, there are two methods of sorting the center. Simulations are carried out to the comparison of the two methods. The fuzziness of alarms is effectively realized. And fuzzy association rules mining are achieved. The advantages and efficiency of FKMA are demonstrated by experiments.
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Zhu, Zhi Jie, Jun Li, Jian Yong Liu, and Hong Cheng Jiang. "The Study of Intelligent Processing Frame to Alarms in Monitoring Center." Advanced Materials Research 614-615 (December 2012): 1008–12. http://dx.doi.org/10.4028/www.scientific.net/amr.614-615.1008.
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With the build of Monitoring Center in our country, a great of signals are uploaded from many substations which are located on every corner. When an abnormal or a grid fault in Power System happens, lots of signals come out, then many operators on duty can’t often react quickly and judge the fault accurately.Recently，more and more scholars begin studying intelligent alarm processing system. In this paper, by analyzing signals characters and SCADA network of Monitoring Center, an intelligent processing frame to alarms in monitoring center is provided, the frame includes three layers named signals foundation treatment layer, signals connecting and sharing layer, signals intelligent diagnosis layer. Now the frame has been implemented successfully in Monitoring Center of our company. At the same time, based on an virtual signals software, lots of devices alarms and grid faults are simulated, this intelligent processing system, which are built on the frame, always show the alarm tip quickly. Facts prove it attributes to grid fault judge for operators of Monitoring Center.
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Deng, Lingzhi, Yuqiang Cheng, and Yehui Shi. "Fault Detection and Diagnosis for Liquid Rocket Engines Based on Long Short-Term Memory and Generative Adversarial Networks." Aerospace 9, no. 8 (July 26, 2022): 399. http://dx.doi.org/10.3390/aerospace9080399.
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The development of health monitoring technology for liquid rocket engines (LREs) can effectively improve the safety and reliability of launch vehicles, which has important theoretical and engineering significance. Therefore, we propose a fault detection and diagnosis (FDD) method for a large LOX/kerosene rocket engine based on long short-term memory (LSTM) and generative adversarial networks (GANs). Specifically, we first modeled a large LOX/kerosene rocket engine using MATLAB/Simulink and simulated the engine’s normal and fault operation states involving various startup and steady-state stages utilizing fault injection. Second, we created an LSTM-GAN model trained with normal operating data using LSTM as the generator and a multilayer perceptron (MLP) as the discriminator. Third, the test data were input into the discriminator to obtain the discrimination results and realize fault detection. Finally, the test data were input into the generator to obtain the predicted samples and calculate the absolute error between the predicted and the real value of each parameter. Then the fault diagnosis index, standardized absolute error (SAE), was constructed. SAE was analyzed to realize fault diagnosis. The simulated results highlight that the proposed method effectively detects faults in the startup and steady-state processes, and diagnoses the faults in the steady-state process without missing an alarm or being affected by false alarms. Compared with the conventional redline cut-off system (RCS), adaptive threshold algorithm (ATA), and support vector machine (SVM), the fault detection process of LSTM-GAN is more concise and more timely.
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Zdiri, Mohamed Ali, Badii Bouzidi, and Hsan Hadj Abdallah. "Performance investigation of an advanced diagnostic method for SSTPI-fed IM drives under single and multiple open IGBT faults." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 2 (March 4, 2019): 616–41. http://dx.doi.org/10.1108/compel-04-2018-0181.
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Purpose This paper aims to analyze and investigate the performance of an improved fault detection and identification (FDI) method based on multiple criteria, applied to six-switch three-phase inverter (SSTPI)-fed induction motor (IM) drives under both single and multiple open insulated-gate bipolar transistors(IGBT) faults. Design/methodology/approach This paper proposes an advanced diagnostic method for both single and multiple open IGBT faults dedicated to SSTPI-fed IM drives considering five distinct faulty operating conditions as follows: a single IGBT open-circuit fault, a single-phase open-circuit fault, a non-crossed double fault in two different legs, a crossed double fault in two different legs and a three-IGBT open-circuit fault. This is achieved because of the introduction of a new diagnosis variable provided using the information of the slope of the current vector in (α-β) frame. The proposed FDI method is based on the synthesis and the analysis, under both healthy and faulty operations, of the behaviors of the introduced diagnosis variable, the three motor phase currents and their normalized average values. Doing so, the developed FDI method allows a best compromise of fast detection and precision localization of IGBT open-circuit fault of the inverter. Findings Simulation works, carried out considering the implementation of the direct rotor flux oriented control in an IM fed by the conventional SSTPI, have proved the high performance of the advanced FDI method in terms of fast fault detection associated with a high robustness against false alarms, against speed and load torque fast variations and against the oscillations of the DC-bus voltage in the case of both healthy and faulty operations. Research limitations/implications This work should be extended considering the validation of the obtained simulation results through experiments. Originality/value Different from other FDI methods, which suffer from a low diagnostic effectiveness for low load levels and false alarms during transient operation, this method offers the potentialities to overcome these drawbacks because of the introduction of the new diagnosis variable. This latter, combined with the information provided from the three motor phase currents and their normalized average values allow a more efficient detection and identification of IGBT open-circuit fault.
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You, Zhuan. "Fault Alarms and Power Performance in Hybrid Electric Vehicles Based on Hydraulic Technology." World Electric Vehicle Journal 14, no. 1 (January 10, 2023): 20. http://dx.doi.org/10.3390/wevj14010020.
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In order to improve the fault alarm effect on the power performance of hydraulic hybrid electric vehicles (HEV), this paper proposes a fault alarm method for hybrid electric vehicle power performance based on hydraulic technology, builds a hybrid electric vehicle power system model, uses hydraulic technology to extract the characteristic signals of key components, uses support vector mechanisms to build a hybrid electric vehicle classifier, and obtains the fault alarm results for dynamic performance based on hydraulic technology. The results show that the proposed method can improve real-time diagnosis and alarm for engine faults in HEV, and the fault can be diagnosed after 5 s of injection, thus ensuring the dynamic stability of HEV.
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Chin, Hsinyung, and Kourosh Danai. "A Method of Fault Signature Extraction for Improved Diagnosis." Journal of Dynamic Systems, Measurement, and Control 113, no. 4 (December 1, 1991): 634–38. http://dx.doi.org/10.1115/1.2896468.
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Efficient extraction of fault signatures from sensory data is a major concern in fault diagnosis. This paper introduces a self-tuning method of fault signature extraction that enhances fault detection, minimizes false alarms, improves diagnosability, and reduces fault signature variability. The proposed method uses a Flagging Unit to convert the processed measurements to binary vectors, and utilizes nonparametric pattern classification techniques to estimate the fault signatures. The performance of the Flagging Unit, which relies on its adaptation algorithms to optimize its performance based upon a sample batch of measurement-fault vectors, is demonstrated in simulation.
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Tian, Ying, Qiang Zou, and Jin Han. "Data-Driven Fault Diagnosis for Automotive PEMFC Systems Based on the Steady-State Identification." Energies 14, no. 7 (March 30, 2021): 1918. http://dx.doi.org/10.3390/en14071918.
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Data-driven diagnosis methods for faults of proton exchange membrane fuel cell (PEMFC) systems can diagnose faults through the state variable data collected during the operation of the PEMFC system. However, the state variable data collected from the PEMFC system during the stack switching between different operating points can easily cause false alarms, such that the practical value of the diagnosis system is reduced. To overcome this problem, a fault diagnosis method for PEMFC systems based on steady-state identification is proposed in this paper. The support vector data description (SVDD) and relevance vector machine (RVM) optimized by the artificial bee colony (ABC) are used for the steady-state identification and fault diagnosis. The density-based spatial clustering of applications with noise (DBSCAN) and linear least squares fitting (LLSF) are used to identify the abnormal data in datasets and estimate change rates of the system state variables respectively. The proposed method can automatically identify the state variable data collected from the PEMFC system during the stack switching between different operating points, so that the diagnosis accuracy can be improved and false alarms can be reduced. The proposed method has a certain practical value and can provide a reference for further study.
Dissertations / Theses on the topic "Fault diagnosis alarms"
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Al-Kharaz, Mohammed. "Analyse multivariée des alarmes de diagnostic en vue de la prédiction de la qualité des produits." Electronic Thesis or Diss., Aix-Marseille, 2021. http://theses.univ-amu.fr.lama.univ-amu.fr/211207_ALKHARAZ_559anw633vgnlp70s324svilo_TH.pdf.
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Cette thèse s’intéresse à la prédiction de la qualité de produits et à l’amélioration de la performance des alarmes de diagnostics au sein d’une usine de semi-conducteurs. Pour cela, nous exploitons l’historique des alarmes collecté durant la production. Premièrement, nous proposons une approche de modélisation et d’estimation du risque de dégradation du produit final associé à chaque alarme déclenchée en fonction du comportement d’activation de celle-ci sur l’ensemble des produits durant la production. Deuxièmement, en utilisant les valeurs de risque estimées pour toute alarme, nous proposons une approche de prédiction de la qualité finale d’un lot de produits. Grâce à l’utilisation des techniques d’apprentissage automatique, cette approche modélise le lien entre les événements d’alarmes des processus et la qualité finale du lot. Dans la même veine, nous proposons une autre approche basée sur le traitement du texte d’évènement d’alarmes dans le but de prédire la qualité finale du produit. Cette approche présente une amélioration en termes de performances et en termes d’exploitation de plus d’information disponible dans le texte d’alarme. Enfin, nous proposons un cadre d’analyse des activations d’alarmes en présentant un ensemble d’outils d’évaluation de performances et plusieurs techniques de visualisation interactive plus adaptées pour la surveillance et l’évaluation des processus de fabrication de semi-conducteurs. Pour chacune des approches susmentionnées, l’efficacité est démontrée à l’aide d’un ensemble de données réelles obtenues à partir d’une usine de fabrication de semi-conducteursThis thesis addresses the prediction of product quality and improving the performance of diagnostic alarms in a semiconductor facility. For this purpose, we exploit the alarm history collected during production. First, we propose an approach to model and estimate the degradation risk of the final product associated with each alarm triggered according to its activation behavior on all products during production. Second, using the estimated risk values for any alarm, we propose an approach to predict the final quality of the product's lot. This approach models the link between process alarm events and the final quality of product lot through machine learning techniques. We also propose a new approach based on alarm event text processing to predict the final product quality. This approach improves performance and exploits more information available in the alarm text. Finally, we propose a framework for analyzing alarm activations through performance evaluation tools and several interactive visualization techniques that are more suitable for semiconductor manufacturing. These allow us to closely monitor alarms, evaluate performance, and improve the quality of products and event data collected in history. The effectiveness of each of the above approaches is demonstrated using a real data set obtained from a semiconductor manufacturing facility
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Trenchard, Andrew J. "Process plant alarm diagnosis using synthesised fault tree knowledge." Thesis, Loughborough University, 1990. https://dspace.lboro.ac.uk/2134/7258.
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The development of computer based tools, to assist process plant operators in their task of fault/alarm diagnosis, has received much attention over the last twenty five years. More recently, with the emergence of Artificial Intelligence (AI) technology, the research activity in this subject area has heightened. As a result, there are a great variety of fault diagnosis methodologies, using many different approaches to represent the fault propagation behaviour of process plant. These range in complexity from steady state quantitative models to more abstract definitions of the relationships between process alarms. Unfortunately, very few of the techniques have been tried and tested on process plant and even fewer have been judged to be commercial successes. One of the outstanding problems still remains the time and effort required to understand and model the fault propagation behaviour of each considered process. This thesis describes the development of an experimental knowledge based system (KBS) to diagnose process plant faults, as indicated by process variable alarms. In an attempt to minimise the modelling effort, the KBS has been designed to infer diagnoses using a fault tree representation of the process behaviour, generated using an existing fault tree synthesis package (FAULTFINDER). The process is described to FAULTFINDER as a configuration of unit models, derived from a standard model library or by tailoring existing models. The resultant alarm diagnosis methodology appears to work well for hard (non-rectifying) faults, but is likely to be less robust when attempting to diagnose intermittent faults and transient behaviour. The synthesised fault trees were found to contain the bulk of the information required for the diagnostic task, however, this needed to be augmented with extra information in certain circumstances.
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Leão, Fábio Bertequini. "Metodologia para análise e interpretação de alarmes em tempo real de sistemas de distribuição de energia elétrica /." Ilha Solteira : [s.n.], 2011. http://hdl.handle.net/11449/100333.
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Orientador: Jose Roberto Sanches MantovaniBanca: Rubén Augusto Romero Lázaro
Banca: Carlos Roberto Minussi
Banca: Oriane Magela Neto
Banca: Julio Cesar Stacchini de Souza
Resumo: Neste trabalho é proposta uma metodologia para a análise e interpretação de alarmes em tempo real em sistemas de distribuição de energia elétrica, considerando o diagnóstico em nível de subestações e redes. A metodologia busca superar as dificuldades e desvantagens dos métodos já propostos na literatura especializada para resolver o diagnóstico de faltas em sistemas de potência. O método proposto emprega um modelo matemático original bem como um novo algoritmo genético para efetuar o diagnóstico dos alarmes de maneira eficiente e rápida. O modelo matemático é dividido em duas partes fundamentais: (1) modelo de operação do sistema de proteção; e (2) modelo de Programação Binária Irrestrita (PBI). A parte (1) é composta por um conjunto de equações de estados esperados das funções de proteção dos relés do sistema, modeladas com base na lógica de operação de funções de proteção tais como sobrecorrente, diferencial e distância, bem como na filosofia de proteção de sistemas de potência. A parte (2) é estabelecida através de uma função objetivo formulada com base na teoria de cobertura parcimoniosa (parcimonious set covering theory), e busca a associação ou "match" entre os relatórios de alarmes informados pelo sistema SCADA (Supervisory Control and Data Acquisition) e os estados esperados das funções de proteção formuladas na parte (1) do modelo. O novo algoritmo genético proposto é empregado para minimizar o modelo de PBI e possui como característica a utilização de dois parâmetros de controle. O algoritmo possui taxas de recombinação e mutação automática e dinamicamente calibradas, baseadas na saturação da população corrente, possuindo uma imediata resposta à possível convergência prematura para ótimos locais. A metodologia desenvolvida para o diagnóstico... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: This work proposes a methodology for the analysis and interpretation of real-time alarms in electric power distribution systems in the substation level and network level. The methodology seeks to overcome the difficulties and disadvantages of the methods already proposed in the literature to solve the fault diagnosis in power systems. The proposed method employs a novel mathematical model and a genetic algorithm to carry out the diagnosis of alarms efficiently and quickly. The model is divided into two main parts: (1) a protection system operation model; and (2) Unconstrained Binary Programming (UBP) model. Part (1) provides a set of expected state equations of the protective relay functions established based on the protection operation logic such as overcurrent, differential and distance as well as the protection philosophy. Part (2) is established through an objective function formulated based on parsimonious set covering theory for associating the alarms reported by SCADA (Supervisory Control and Data Acquisition) system with the expected states of the protective relay functions. The novel genetic algorithm use only two control parameters and is employed to minimize the UBP model. In addition the algorithm has recombination and mutation rates automatically and dynamically calibrated based on the saturation of the current population and it presents an immediate response to possible premature convergence to local optima. The methodology developed for the diagnosis of substations is extended to distribution networks considering that the network has sufficient level of automation for remote monitoring of the primary feeders. In this way a new paradigm for protection of distribution networks developed based on Smart Grid concept is proposed. Extensive tests are performed with the methodology applied to distribution... (Complete abstract click electronic access below)
Doutor
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Oliveira, Aécio de Lima. "Processador inteligente de alarmes e modelos de programação matemática para diagnóstico de faltas em sistemas elétricos de potência." Universidade Federal de Santa Maria, 2016. http://repositorio.ufsm.br/handle/1/3699.
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Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorThis thesis proposes an Intelligent Alarm Processor for fault diagnosis in electrical power systems. The objective is to develop a methodology for automatic fault analysis using reported alarms from Supervisory Control and Data Acquisition (SCADA) to allow the use of diagnosis systems in large power systems. The proposal can be used in real-time decision support systems to assist control center‟s operators during the decision-making after unscheduled contingencies with relevant information to power system restoration. This work expects to contribute to the development of advanced alarm management logics that allow modifying the chronological sequence of reported alarms, event mapping and the generation of operating patterns of protection systems according to topology network. Still, mathematical programming models have been formulated as a parsimonious set covering problem to fault section estimation and identification of protective devices with improper operation. Among these models, it stands out the model that deals with integrated analysis of reported alarms, events and diagnosis that better explain the alarms. The proposed approach has been tested in different portions of the Southern Brazilian power system. The results show that alarm processing allows the practical implementation of intelligent diagnosis methods in existing supervisory systems. The proposed diagnosis methods show better performance and accurate solutions than other methods presented in literature.
Esta tese propõe um Processador Inteligente de Alarmes para diagnóstico de faltas em sistemas elétricos de potência. O objetivo é desenvolver uma metodologia para a análise automática de faltas a partir dos alarmes reportados no sistema de supervisão e aquisição de dados (SCADA) que possibilite o uso de métodos de diagnóstico em sistemas de potência de grande porte. Essa proposta pode ser empregada em sistemas de apoio à decisão em tempo real, que auxiliem operadores de centros de controle do sistema (COS) na tomada de decisão após desligamentos não programados, com informações pertinentes para o restabelecimento do sistema. O trabalho espera contribuir com o desenvolvimento de lógicas avançadas de gerenciamento de alarmes que possibilitem a reordenação cronológica dos alarmes reportados, o mapeamento dos eventos e a geração de padrões de funcionamento de sistemas de proteção de acordo à topologia da rede. Além disso, os modelos de programação matemática foram formulados como um problema de recobrimento de conjuntos parcimonioso, para estimação da seção em falta e identificação dos dispositivos de proteção com atuação indevida. Dentre esses modelos, destaca-se o modelo que analisa, de forma integrada, os alarmes reportados e determina os eventos e diagnósticos que melhor explicam os alarmes. A abordagem proposta foi testada em diferentes porções do sistema sul do sistema interligado nacional (SIN). Os resultados mostram que as rotinas desenvolvidas para o processamento de alarmes permite a implantação prática de métodos inteligentes de diagnóstico em sistemas supervisórios existentes. Os métodos propostos para diagnóstico de faltas mostraram desempenhos e precisão nos resultados superiores a outros métodos presentes na literatura.
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Leão, Fábio Bertequini [UNESP]. "Metodologia para análise e interpretação de alarmes em tempo real de sistemas de distribuição de energia elétrica." Universidade Estadual Paulista (UNESP), 2011. http://hdl.handle.net/11449/100333.
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leao_fb_dr_ilha.pdf: 4326970 bytes, checksum: 5e80d8b3eb8a0bff2c52ea28e2f0a451 (MD5)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Neste trabalho é proposta uma metodologia para a análise e interpretação de alarmes em tempo real em sistemas de distribuição de energia elétrica, considerando o diagnóstico em nível de subestações e redes. A metodologia busca superar as dificuldades e desvantagens dos métodos já propostos na literatura especializada para resolver o diagnóstico de faltas em sistemas de potência. O método proposto emprega um modelo matemático original bem como um novo algoritmo genético para efetuar o diagnóstico dos alarmes de maneira eficiente e rápida. O modelo matemático é dividido em duas partes fundamentais: (1) modelo de operação do sistema de proteção; e (2) modelo de Programação Binária Irrestrita (PBI). A parte (1) é composta por um conjunto de equações de estados esperados das funções de proteção dos relés do sistema, modeladas com base na lógica de operação de funções de proteção tais como sobrecorrente, diferencial e distância, bem como na filosofia de proteção de sistemas de potência. A parte (2) é estabelecida através de uma função objetivo formulada com base na teoria de cobertura parcimoniosa (parcimonious set covering theory), e busca a associação ou “match” entre os relatórios de alarmes informados pelo sistema SCADA (Supervisory Control and Data Acquisition) e os estados esperados das funções de proteção formuladas na parte (1) do modelo. O novo algoritmo genético proposto é empregado para minimizar o modelo de PBI e possui como característica a utilização de dois parâmetros de controle. O algoritmo possui taxas de recombinação e mutação automática e dinamicamente calibradas, baseadas na saturação da população corrente, possuindo uma imediata resposta à possível convergência prematura para ótimos locais. A metodologia desenvolvida para o diagnóstico...
This work proposes a methodology for the analysis and interpretation of real-time alarms in electric power distribution systems in the substation level and network level. The methodology seeks to overcome the difficulties and disadvantages of the methods already proposed in the literature to solve the fault diagnosis in power systems. The proposed method employs a novel mathematical model and a genetic algorithm to carry out the diagnosis of alarms efficiently and quickly. The model is divided into two main parts: (1) a protection system operation model; and (2) Unconstrained Binary Programming (UBP) model. Part (1) provides a set of expected state equations of the protective relay functions established based on the protection operation logic such as overcurrent, differential and distance as well as the protection philosophy. Part (2) is established through an objective function formulated based on parsimonious set covering theory for associating the alarms reported by SCADA (Supervisory Control and Data Acquisition) system with the expected states of the protective relay functions. The novel genetic algorithm use only two control parameters and is employed to minimize the UBP model. In addition the algorithm has recombination and mutation rates automatically and dynamically calibrated based on the saturation of the current population and it presents an immediate response to possible premature convergence to local optima. The methodology developed for the diagnosis of substations is extended to distribution networks considering that the network has sufficient level of automation for remote monitoring of the primary feeders. In this way a new paradigm for protection of distribution networks developed based on Smart Grid concept is proposed. Extensive tests are performed with the methodology applied to distribution... (Complete abstract click electronic access below)
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Sánchez, Vílchez José Manuel. "Cross-layer self-diagnosis for services over programmable networks." Thesis, Evry, Institut national des télécommunications, 2016. http://www.theses.fr/2016TELE0012/document.
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Les réseaux actuels servent millions de clients mobiles et ils se caractérisent par équipement hétérogène et protocoles de transport et de gestion hétérogènes, et des outils de gestion verticaux, qui sont très difficiles à intégrer dans leur infrastructure. La gestion de pannes est loin d’être automatisée et intelligent, ou un 40 % des alarmes sont redondantes et seulement un 1 ou 2% des alarmes sont corrélées au plus dans un centre opérationnel. Ça indique qu’il y a un débordement significatif des alarmes vers les adminis-trateurs humains, a comme conséquence un haut OPEX vue la nécessité d’embaucher de personnel expert pour accomplir les tâches de gestion de pannes. Comme conclusion, le niveau actuel d’automatisation dans les tâches de gestion de pannes dans réseaux télécoms n’est pas adéquat du tout pour adresser les réseaux programmables, lesquels promettent la programmation des ressources et la flexibilité afin de réduire le time-to-market des nouveaux services. L’automatisation de la gestion des pannes devient de plus en plus nécessaire avec l’arrivée des réseaux programmables, SDN (Software-Defined Networking), NFV (Network Functions Virtualization) et le Cloud. En effet, ces paradigmes accélèrent la convergence entre les domaines des réseaux et la IT, laquelle accélère de plus en plus la transformation des réseaux télécoms actuels en menant à repenser les opérations de gestion de réseau et des services, en particulier les opérations de gestion de fautes. Cette thèse envisage l’application des principes d’autoréparation en infrastructures basées sur SDN et NFV, en focalisant sur l’autodiagnostic comme facilitateur principal des principes d’autoréparation. Le coeur de cette thèse c’est la conception d’une approche de diagnostic qui soit capable de diagnostiquer de manière continuée les services dynamiques virtualisés et leurs dépendances des ressources virtuels (VNFs et liens virtuels) mais aussi les dépendances de ceux ressources virtuels de la infrastructure physique en-dessous, en prenant en compte la mobilité, la dynamicite, le partage de ressources à l’infrastructure en-dessousCurrent networks serve billions of mobile customer devices. They encompass heterogeneous equipment, transport and manage-ment protocols, and vertical management tools, which are very difficult and costly to integrate. Fault management operations are far from being automated and intelligent, where around 40% of alarms are redundant only around 1-2% of alarms are correlated at most in a medium-size operational center. This indicates that there is a significant alarm overflow for human administrators, which inherently derives in high OPEX due to the increasingly need to employ high-skilled people to perform fault management tasks. In conclusion, the current level of automation in fault management tasks in Telcos networks is not at all adequate for programmable networks, which promise a high degree of programmability and flexibility to reduce the time-to-market. Automation on fault management is more necessary with the advent of programmable networks, led by with SDN (Software-Defined Networking), NFV (Network Functions Virtualization) and the Cloud. Indeed, the arise of those paradigms accelerates the convergence between networks and IT realms, which as consequence, is accelerating faster and faster the transformation of cur-rent networks leading to rethink network and service management and operations, in particular fault management operations. This thesis envisages the application of self-healing principles in SDN and NFV combined infrastructures, by focusing on self-diagnosis tasks as main enabler of self-healing. The core of thesis is to devise a self-diagnosis approach able to diagnose at run-time the dynamic virtualized networking services and their dependencies from the virtualized resources (VNFs and virtual links) but also the dependencies of those virtualized resources from the underlying network infrastructure, taking into account the mobility, dynamicity, and sharing of resources in the underlying infrastructure
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Toller, Marcelo Brondani. "Proposta de um sistema híbrido composto por redes neurais artificiais e algorítmos genéticos para o tratamento de alarmes e o diagnóstico de faltas em sistemas elétricos de potência." Universidade Federal de Santa Maria, 2011. http://repositorio.ufsm.br/handle/1/8490.
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This work proposes a hybrid system for alarm processing and fault diagnosis in
electrical networks which use two methods of computational intelligence: Generalized
Regression Neural Network and Genetic Algorithms. The neural network has the function of
processing the set of received alarms and present as a response the characteristic(s) event(s),
using for this, an elaborated knowledge based on the functional diagrams for protection and
interviews with operators. Six modules were implemented for different neural components of
a test system, according to their protection schemes. The output of these modules is used as
input to the GA which has to do a combined analysis along with its database and provide the
operator with the main protective components involved in the incident, as well as the probable
causes of defects and actions to be taken in order to return the system in the shortest possible
time and greater safety. For average inserted random errors of 0%, 7,73%, 15,46% and
23,19% in the received alarms, the system was able to diagnoses correctly in 100%, 93,60%,
74,26% and 48,07% of the cases respectively. It was found that the genetic algorithm
improved the results obtained by neural network with good capability of generalization and
condition to present multiple solutions, and the response time of the hybrid system was
acceptable to the under consideration problem.O presente trabalho propõe um sistema híbrido para processamento de alarmes e diagnóstico de faltas em redes elétricas com a utilização de dois métodos de inteligência computacional: Generalized Regression Neural Network e Algoritmos Genéticos. A rede neural tem a função de processar o conjunto de alarmes reportados e apresentar como resposta o evento(s) característico(s), utilizando-se, para isso, de um conhecimento elaborado com base nos diagramas funcionais da proteção e entrevista com operadores. Foram implementados seis módulos neurais para diferentes componentes de um sistema teste, de acordo com os seus respectivos esquemas de proteção. A saída destes módulos é utilizada como entrada para o AG que deve fazer uma análise combinatória juntamente com sua base de dados e apresentar ao operador os principais componentes de proteção envolvidos na incidência, bem como as prováveis causas do defeito e ações a serem tomadas de forma a restabelecer o sistema no menor tempo possível e com maior segurança. Para erros aleatórios médios inseridos de 0%, 7,73%, 15,46% e 23,19% nos alarmes reportados, o sistema se mostrou capaz de diagnosticar corretamente em respectivamente 100%, 93,60%, 74,26% e 48,07% dos casos. Verificou-se que o algoritmo genético melhorou os resultados obtidos pela rede neural, apresentando boa capacidade de generalização e condições de apresentação de múltiplas soluções, sendo o tempo de resposta do sistema híbrido aceitável para o problema tratado.
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Hounkonnou, Carole. "Auto-diagnostic actif dans les réseaux de télécommunications." Phd thesis, Université Rennes 1, 2013. http://tel.archives-ouvertes.fr/tel-00932834.
Full textAbstract:
Les réseaux de télécommunications deviennent de plus en plus complexes, notamment de par la multiplicité des technologies mises en œuvre, leur couverture géographique grandissante, la croissance du trafic en quantité et en variété, mais aussi de par l'évolution des services fournis par les opérateurs. Tout ceci contribue à rendre la gestion de ces réseaux de plus en plus lourde, complexe, génératrice d'erreurs et donc coûteuse pour les opérateurs. On place derrière le terme " réseaux autonome " l'ensemble des solutions visant à rendre la gestion de ce réseau plus autonome. L'objectif de cette thèse est de contribuer à la réalisation de certaines fonctions autonomiques dans les réseaux de télécommunications. Nous proposons une stratégie pour automatiser la gestion des pannes tout en couvrant les différents segments du réseau et les services de bout en bout déployés au-dessus. Il s'agit d'une approche basée modèle qui adresse les deux difficultés du diagnostic basé modèle à savoir : a) la façon d'obtenir un tel modèle, adapté à un réseau donné à un moment donné, en particulier si l'on souhaite capturer plusieurs couches réseau et segments et b) comment raisonner sur un modèle potentiellement énorme, si l'on veut gérer un réseau national par exemple. Pour répondre à la première difficulté, nous proposons un nouveau concept : l'auto-modélisation qui consiste d'abord à construire les différentes familles de modèles génériques, puis à identifier à la volée les instances de ces modèles qui sont déployées dans le réseau géré. La seconde difficulté est adressée grâce à un moteur d'auto-diagnostic actif, basé sur le formalisme des réseaux Bayésiens et qui consiste à raisonner sur un fragment du modèle du réseau qui est augmenté progressivement en utilisant la capacité d'auto-modélisation: des observations sont collectées et des tests réalisés jusqu'à ce que les fautes soient localisées avec une certitude suffisante. Cette approche de diagnostic actif a été expérimentée pour réaliser une gestion multi-couches et multi-segments des alarmes dans un réseau IMS.
Books on the topic "Fault diagnosis alarms"
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O'Callaghan, Peter. Demonstration of combination of expert system paradigms for telecommunications network alarm correlation and fault diagnosis. (s.l: The Author), 1996.
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Park, Min Young. Evaluation of a fuzzy-expert system for fault diagnosis in power systems: Using an object-oriented hybrid solution for real-time power alarm processing. Poole: Bournemouth University, 2001.
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Trenchard, Andrew John. Process plant alarm diagnosis using fault tree knowledge. 1990.
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Trenchard, Andrew John. Process plant alarm diagnosis using fault tree knowledge. 1990.
Find full textBook chapters on the topic "Fault diagnosis alarms"
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Ashmole, P. H. "Power System Alarm Analysis and Fault Diagnosis Using Expert Systems." In Failsafe Control Systems, 207–16. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-009-0429-3_15.
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Fritzen, Paulo Cícero, Ghendy Cardoso, João Montagner Zauk, Adriano Peres de Morais, Ubiratan H. Bezerra, and Joaquim A. P. M. Beck. "Integrated Use of Artificial Neural Networks and Genetic Algorithms for Problems of Alarm Processing and Fault Diagnosis in Power Systems." In Intelligent Information and Database Systems, 370–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12145-6_38.
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Chakkor, Saad, Mostafa Baghouri, and Abderrahmane Hajraoui. "Fault Severity Sensing for Intelligent Remote Diagnosis in Electrical Induction Machines." In Applications of Artificial Neural Networks for Nonlinear Data, 180–206. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4042-8.ch008.
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Electrical induction machines are widely used in the modern wind power production. As their repair cost is important and since their down-time leads to significant income loss, increasing their reliability and optimizing their proactive maintenance process are critical tasks. Many diagnosis systems have been proposed to resolve this issue. However, these systems are failing to recognize accurately the type and the severity level of detected faults in real time. In this chapter, a remote automated control approach applied for electrical induction machines has been suggested as an appropriate solution. It combines developed Fast-ESPRIT method, fault classification algorithm, and fuzzy inference system interconnected with vibration sensors, which are located on various wind turbine components. Furthermore, a new fault severity indicator has been formulated and evaluated to avoid false alarms. Study findings with computer simulation in Matlab prove the satisfactory robustness and performance of the proposed technique in fault classification and diagnosis.
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Petze, John. "Analytics, Alarms, Analysis, Fault Detection and Diagnostics." In Automated Diagnostics and Analytics for Buildings, 127–35. River Publishers, 2021. http://dx.doi.org/10.1201/9781003151906-12.
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El-Far, Gomaa Zaki. "Design of Robust Approach for Failure Detection in Dynamic Control Systems." In Recent Algorithms and Applications in Swarm Intelligence Research, 237–59. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2479-5.ch013.
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This paper presents a robust instrument fault detection (IFD) scheme based on modified immune mechanism based evolutionary algorithm (MIMEA) that determines on line the optimal control actions, detects faults quickly in the control process, and reconfigures the controller structure. To ensure the capability of the proposed MIMEA, repeating cycles of crossover, mutation, and clonally selection are included through the sampling time. This increases the ability of the proposed algorithm to reach the global optimum performance and optimize the controller parameters through a few generations. A fault diagnosis logic system is created based on the proposed algorithm, nonlinear decision functions, and its derivatives with respect to time. Threshold limits are implied to improve the system dynamics and sensitivity of the IFD scheme to the faults. The proposed algorithm is able to reconfigure the control law safely in all the situations. The presented false alarm rates are also clearly indicated. To illustrate the performance of the proposed MIMEA, it is applied successfully to tune and optimize the controller parameters of the nonlinear nuclear power reactor such that a robust behavior is obtained. Simulation results show the effectiveness of the proposed IFD scheme based MIMEA in detecting and isolating the dynamic system faults.
Conference papers on the topic "Fault diagnosis alarms"
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Li, Wenfei, and Rama K. Yedavalli. "Dynamic Threshold Method Based Aircraft Engine Sensor Fault Diagnosis." In ASME 2008 Dynamic Systems and Control Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/dscc2008-2262.
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It is challenging to have a good fault diagnostic scheme that can distinguish between model uncertainties and occurrence of faults, which helps in reducing false alarms and missed detections. In this paper, a dynamic threshold algorithm is developed for aircraft engine sensor fault diagnosis that accommodates parametric uncertainties. Using the robustness analysis of parametric uncertain systems, we generate upper-and-lower bound trajectories for the dynamic threshold. The extent of parametric uncertainties is assumed to be such that the perturbed eigenvalues reside in a set of distinct circular regions. Dedicated observer scheme is used for engine sensor fault diagnosis design. The residuals are errors between estimated state variables from a bank of Kalman filters. With this design approach, the residual crossing the upper-and-lower bounds of the dynamic threshold indicates the occurrence of fault. Application to an aircraft gas turbine engine Component Level Model clearly illustrates the improved performance of the proposed method.
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Liu, Chongchong, Guohua Wu, Congsong Yang, Yunwen Li, and Qian Wu. "A Fault Diagnosis Method Based on Signed Directed Graph and Correlation Analysis for Nuclear Power Plants." In 2020 International Conference on Nuclear Engineering collocated with the ASME 2020 Power Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/icone2020-16120.
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Abstract Fault detection and diagnosis (FDD) provides safety alarms and diagnostic functions for a nuclear power plant (NPP), which comprises large and complex systems. NPP has a large number of parameters which make it difficult achieve FDD. Now many diagnosis methods have lack of better explanation for faults and quantitative analysis. Therefore, to overcome the “black box” of FDD based on data-driven methods, this paper adopts signed directed graph (SDG) in knowledge graph for FDD. It can express the cause and effect of accidents through knowledge maps. At same time, this paper uses correlation analysis to conduct a quantitative analysis between parameters and faults. It this paper, SDG is used to explain the reason of faults. In order to quickly achieve FDD, this paper introduces a quantitative analysis method. It combines expert system and correlation analysis method to analyze the weight of each parameter. On this basis, matrix reasoning is used to achieve the FDD, and the reason is shown in SDG model inference. This paper takes loss of coolant accident as the case study, the case shows that the proposed method is superior to the conventional SDG method and can diagnose the faults timely.
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Kim, Kyusung, and Dinkar Mylaraswamy. "Fault Diagnosis and Prognosis of Gas Turbine Engines Based on Qualitative Modeling." In ASME Turbo Expo 2006: Power for Land, Sea, and Air. ASMEDC, 2006. http://dx.doi.org/10.1115/gt2006-91210.
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This paper presents the development of a new fault diagnosis and prognosis algorithm based on qualitative modeling, which provides improved fault isolation. A fault diagnosis and prognosis algorithm is developed to detect and identify faults in the startup components of turbine engines while the faults are still in progress. Such diagnosis and prognosis will make it possible to take proper action before the system breaks down. The evidence associated with startup component failure is based on the aggregation of three dynamic events occurring in different time windows. These events are observable from speed at peak EGT (exhaust gas temperature), peak EGT, and start time. Discrete event modeling observes the unsynchronized occurrence of events. The algorithm was tested with data collected from the field; test results were obtained for twenty-nine engines, including six engines with failed startup components. The developed fault prognosis system successfully predicts failure for all six cases. In the earliest case, alarms triggered sixteen flights before the startup component breakdown and five flights in advance for the latest case.
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Lang, Haoxiang, Ying Wang, and Clarence W. de Silva. "Fault Diagnosis of an Industrial Machine Through Neuro-Fuzzy Sensor Fusion." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-42323.
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In this paper a neuro-fuzzy approach of multi-sensor fusion is developed for a fault diagnosis system. The approach is validated by applying it to a machine called the Iron Butcher, which is used in industry for the removal of heads in fish prior to further processing for canning. An important goal of this approach developed in this paper is to make an accurate decision of the machine condition by fusing information from different sensors. Specifically, sound, vibration and vision measurements are acquired from the machine using a microphone, an accelerometer and a digital CCD camera, respectively. Next, the sound and vibration signals are transformed into the frequency domain using Fast Fourier Transform (FFT). A feature vector from the FFT frequency spectra is defined and extracted from the acquired information. Also, a feature based vision tracking approach—the Scale Invariant Feature Transform (SIFT)—is applied to the vision data to track the object of interest (fish) in a robust manner. In the diagnosis process, a candidate fish is detected and tracked. Sound, vibration and vision features are extracted as inputs for the neuro-fuzzy fault diagnosis system. A four-layer neural network including a fuzzy hidden layer is developed to analyze and diagnose any existing faults. By training the neural network with sample data for typical faults, six crucial faults in the fish cutting machine are detected precisely. In this manner, alarms to warn about impending faults may be generated as well during the machine operation. Developed approaches are validated using computer simulations and physical experimentation using the industrial machine.
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de Lima Oliveira, Aecio, Patrick Escalante Farias, Olinto Cesar Bassi de Araujo, Adriano Peres de Morais, and Ghendy Cardoso. "Treatment of alarms applied to fault diagnosis in power systems." In 2014 49th International Universities Power Engineering Conference (UPEC). IEEE, 2014. http://dx.doi.org/10.1109/upec.2014.6934735.
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Cohen, Joseph, Baoyang Jiang, and Jun Ni. "Fault Diagnosis of Timed Event Systems: An Exploration of Machine Learning Methods." In ASME 2020 15th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/msec2020-8360.
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Abstract Especially common in discrete manufacturing, timed event systems often require a high degree of synchronization for healthy operation. Discrete event system methods have been used as mathematical tools to detect known faults, but do not scale well for problems with extensive variability in the normal class. A hybridized discrete event and data-driven method is suggested to supplement fault diagnosis in the case where failure patterns are not known in advance. A unique fault diagnosis framework consisting of signal data from programmable logic controllers, a Timed Petri Net of the normal process behavior, and machine learning algorithms is presented to improve fault diagnosis of timed event systems. Various supervised and unsupervised machine learning algorithms are explored as the methodology is implemented to a case study in semiconductor manufacturing. State-of-the-art classifiers such as artificial neural networks, support vector machines, and random forests are implemented and compared for handling multi-fault diagnosis using programmable logic controller signal data. For unsupervised learning, classifiers based on principal component analysis utilizing major and minor principal components are compared for anomaly detection. The rule-based extreme random forest classifier achieves the highest validation accuracy of 98% for multi-fault classification. Likewise, the unsupervised learning approach shows similar success, yielding anomaly detection rates of 98% with false alarms under 3%. The industrial feasibility of this method is notable, with the results achieved with a training set 99% smaller than the supervised learning classifiers.
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Camporeale, S., L. Dambrosio, A. Milella, M. Mastrovito, and B. Fortunato. "Fault Diagnosis of Combined Cycle Gas Turbine Components Using Feed Forward Neural Networks." In ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/gt2003-38742.
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A diagnostic tool based on Feed Forward Neural Networks (FFNN) is proposed to detect the origin of performance degradation in a Combined Cycle Gas Turbine (CCGT) power plant. In such a plant, due the connection of the steam cycle to the gas turbine, any deterioration of gas turbine components affects not only the gas turbine itself but also the steam cycle. At the same time, fouling of the heat recovery boiler may cause the increase of the turbine back-pressure, reducing the gas turbine performance. Therefore, measurements taken from the steam cycle can be included in the fault variable set, used for detecting faults in the gas turbine. The interconnection of the two parts of the CCGT power plant is shown through the fingerprints of selected component fault models for a power plant composed of a heavy-duty gas turbine and a steam plant with a single pressure recovery boiler. The diagnostic tool is composed of two FFNN stages: the first network stage is addressed to pre-process fault data in order to evaluate the influence of the single fault variable on the single fault condition. The second FFNN stage detects the fault conditions. Tests with simulated data show that the the diagnostic tool is able to recognize single faults of both the gas turbine and the steam plant, with a high rate of success, in case of full fault intensity, even in presence of uncertainties in measurements. In case of partial fault intensity, faults concerning gas turbine components and the superheater, are well recognized, while false alarms occur for the other steam plant component faults, in presence of uncertainties in data. Finally, some combinations of faults, belonging either to the gas turbine or the steam plant, have been examined for testing the diagnostic tool on double fault detection. In this case, the network is applied twice. In the first step the amount of the fault parameters that originate the primary fault are estimated. In the second step, the diagnostic tool curtails the contribution of the main fault to the fault parameters, and the diagnostic process is reiterated. In the examined fault combinations, the diagnostic tool was able to detect at least one of the two faults in about 60% of the cases, even in presence of uncertainty in measurements and partial fault intensity.
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Parlos, Alexander G., Kyusung Kim, and Raj M. Bharadwaj. "Sensorless Early Detection of Mechanical Faults: Developments in Smart Rotating Machines." In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/vib-21750.
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Abstract Practical early fault detection and diagnosis systems must exhibit high level of detection accuracy and while exhibiting acceptably low false alarm rates. Such designs must have applicability to a large class of machines, require installation of no additional sensors, and require minimal detailed information regarding the specific machine design. Electromechanical systems, such as electric motors driving dynamic loads like pumps and compressors, often develop incipient failures that result in downtime. There is a large number of such failure modes, with a large majority being of mechanical nature. The precise signatures of these failure modes depend on numerous machine-specific factors, including variations in the electric power supply and driven load. In this paper the development and experimental demonstration of a sensorless, detection and diagnosis system is presented for incipient machine faults. The developed fault detection and diagnosis system uses recent developments in dynamic recurrent neural networks in implementing an empirical model-based approach, and multi-resolution signal processing for extracting fault information from transient signals. The signals used by the system are only the multi-phase motor current and voltage sensors, whereas the transient mechanical speed is estimated from these measurements using a recently developed speed filter. The effectiveness of the fault diagnosis system is demonstrated by detecting stator, rotor and bearing failures at early stages of development and during different levels of deterioration. Experimental test results from small machines, 2.2 kW, and large machines, 373 kW and 597 kW, are presented demonstrating the effectiveness of the proposed approach. Furthermore, the ability of the diagnosis system to discriminate between false alarms and actual incipient failure conditions is demonstrated.
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Scheianu, Dorin, and Phillip A. Farrington. "Development of a Companion Set of Charts—Soft Sensor and Directional Moving Range—for Fault Monitoring, Detection and Diagnosis With Application to Gas Turbine Engines." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-50962.
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Gas turbines monitoring for fault detection and diagnosis is long desired to be embedded within control systems. Yet the general approach is to have alarms and shut downs when critical parameters exceed certain limits, and fault diagnosis is initiated on the behalf of experienced professionals and testing apparatus during scheduled maintenance time. Statistical methods for monitoring univariate and multivariate processes have been developed and publicized in the research literature. A gas turbine can be treated as a complex multivariate process with parameters depending both on control variables imposed by operator and on independent ambient parameters. The authors propose a set of companion charts that can be implemented on line and allows continuous monitoring both for fault amplitude — represented by a newly introduced soft sensor — and for process variability in the direction of interest. The control limits are introduced using multivariate statistical theory. The set of charts was applied at Wood Group LIT in a test cell, for monitoring process variability and for diagnosis and characterization of engine faults during tests. A second application is used for early detection of faults at the current serviced fleet of turbines.
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Tse, Peter W., and Ling S. He. "Can Wavelet Transforms Used for Data Compression Equally Suitable for the Use of Machine Fault Diagnosis?" In ASME 2001 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/detc2001/vib-21647.
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Abstract Wavelet transforms are capable of separating the raw vibro-acoustic signals into different frequency and time bands. They have exhibited potentials in the detection of fault related impulsive signals by using their multi-resolution time-frequency analyses. To ensure the design of wavelet transforms is simple and the processing is not time intensive, discrete type of wavelet transforms (DWTs) become popular as they are composed of low-pass and high-pass digital filters only, making them easier to implement and processing faster. Recently, a number of publications have applied the similar type of DWTs commonly used for data compression (dyadic type of DWTs) in vibration based machine fault diagnosis. However, the results are not satisfactory. The main reasons are the poor resolution provided by DWTs and the inappropriate design of digital filters causing undesirable frequency aliasings. Without taking care of these problems, they may lead to false alarms in fault diagnosis. In this paper, we present a new family of DWTs, which mainly consists of a series of Butterworth filter banks. They are capable of providing sufficient resolutions in different time and frequency ranges, and minimizing the effect of frequency aliasing. The results have shown that the new types of DWTs are promising in solving the problems and tailor-made for machine fault diagnosis. With the help of the new DWTs, the faults that exhibit non-linear and non-stationary signals can be detected easier and the diagnosis becomes more reliable.