Relevant bibliographies by topics / Plant diagnosis

Academic literature on the topic 'Plant diagnosis'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Plant diagnosis"

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Guarro, S. "Nuclear power plant diagnosis." Annals of Nuclear Energy 14, no. 6 (January 1987): 325. http://dx.doi.org/10.1016/0306-4549(87)90135-6.

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Perez, Ana Picallo, Perez Iribarren E, Apaolaza A, and Sala J. M. "Thermoeconomic Approach to the Diagnosis of A DHW Microcogeneration Plant." Modern Environmental Science and Engineering 2, no. 08 (August 2016): 507–13. http://dx.doi.org/10.15341/mese(2333-2581)/08.02.2016/001.

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O'SHIMA, Eiji. "Plant management and failure diagnosis." Journal of the Japan Society for Precision Engineering 57, no. 3 (1991): 413–17. http://dx.doi.org/10.2493/jjspe.57.413.

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TAMURA, Ken. "Mecanical Diagnosis in Chemical Plant." Journal of the Society of Materials Science, Japan 66, no. 2 (2017): 193. http://dx.doi.org/10.2472/jsms.66.193.

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Miller, S. A., and R. R. Martin. "Molecular Diagnosis of Plant Disease*." Annual Review of Phytopathology 26, no. 1 (September 1988): 409–32. http://dx.doi.org/10.1146/annurev.py.26.090188.002205.

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Vevelstad, Merete, Unni Johansen, An-Magritt Haneborg, Marianne Madland Hagesæther, and åse Marit Leere øiestad. "Plant toxin poisoning–a disguised diagnosis?" Toxicologie Analytique et Clinique 34, no. 3 (September 2022): S63. http://dx.doi.org/10.1016/j.toxac.2022.06.082.

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Groot, Jan E. "Foliage Plant Diseases: Diagnosis and Control." HortTechnology 8, no. 4 (October 1998): 621c. http://dx.doi.org/10.21273/horttech.8.4.621b.

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Poll, Hans Günther, José Carlos Zanutto, and Walter Ponge-Ferreira. "Hydraulic Power Plant Machine Dynamic Diagnosis." Shock and Vibration 13, no. 4-5 (2006): 409–27. http://dx.doi.org/10.1155/2006/203834.

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A method how to perform an entire structural and hydraulic diagnosis of prototype Francis power machines is presented and discussed in this report. Machine diagnosis of Francis units consists on a proper evaluation of acquired mechanical, thermal and hydraulic data obtained in different operating conditions of several rotary and non rotary machine components. Many different physical quantities of a Francis machine such as pressure, strains, vibration related data, water flow, air flow, position of regulating devices and displacements are measured in a synchronized way so that a relation of cause an effect can be developed for each operating condition and help one to understand all phenomena that are involved with such kind of machine. This amount of data needs to be adequately post processed in order to allow correct interpretation of the machine dynamics and finally these data must be compared with the expected calculated data not only to fine tuning the calculation methods but also to accomplish fully understanding of the influence of the water passages on such machines. The way how the power plant owner has to operate its Francis machines, many times also determined by a central dispatcher, has a high influence on the fatigue life time of the machine components. The diagnostic method presented in this report helps one to understand the importance of adequate operation to allow a low maintenance cost for the entire power plant. The method how to acquire these quantities is discussed in details together with the importance of correct sensor balancing, calibration and adequate correlation with the physical quantities. Typical results of the dynamic machine behavior, with adequate interpretation, obtained in recent measurement campaigns of some important hydraulic turbines were presented. The paper highlights the investigation focus of the hydraulic machine behavior and how to tailor the measurement strategy to accomplish all goals. Finally some typical recommendations based on the experience obtained on previous diagnostic reports of Francis turbines are performed in order to allow a better and safe operation of these power plant units.
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CHEN, Peng, and Toshio TOYOTA. "Sequential Fuzzy Diagnosis for Plant Machinery." JSME International Journal Series C 46, no. 3 (2003): 1121–29. http://dx.doi.org/10.1299/jsmec.46.1121.

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Xue, Wangyu, Xiu Li, and Biqing Huang. "Health diagnosis of nuclear power plant." International Journal of Advanced Robotic Systems 16, no. 5 (September 1, 2019): 172988141988065. http://dx.doi.org/10.1177/1729881419880654.

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At present, nuclear power plant is developing rapidly, and its application has been involved in many aspects including life, military, industry and many other important fields, bringing benefits to people’s life. However, the nuclear power plant has a relatively special structure. Once a safety accident occurs, the consequences will be unimaginable, and the cost of its operation and maintenance will be relatively high. Therefore, how to effectively diagnose the health status of the nuclear power plant is an urgent problem to be solved. On the above-mentioned research background, we need to study nuclear power plant health diagnosis method. Considering the characteristic of the nuclear power plant system and special failure mode, both the safety and economy, a health condition diagnosis method based on analytic hierarchy process and fuzzy comprehensive evaluation method is proposed for the structural characteristics and functional characteristics of nuclear power plants. According to the special failure mode and complex system structure of nuclear power plant, the evaluation index system based on failure mode is constructed by laying the system using the hierarchical analysis method, and the system is scored by fuzzy comprehensive evaluation method. The health status makes a coarse-grained diagnosis and provides a reference for the development of the operation and maintenance strategy.
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Dissertations / Theses on the topic "Plant diagnosis"

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Fisal, Zahedi B. "Real-time process plant fault diagnosis." Thesis, Aston University, 1989. http://publications.aston.ac.uk/9703/.

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Operators can become confused while diagnosing faults in process plant while in operation. This may prevent remedial actions being taken before hazardous consequences can occur. The work in this thesis proposes a method to aid plant operators in systematically finding the causes of any fault in the process plant. A computer aided fault diagnosis package has been developed for use on the widely available IBM PC compatible microcomputer. The program displays a coloured diagram of a fault tree on the VDU of the microcomputer, so that the operator can see the link between the fault and its causes. The consequences of the fault and the causes of the fault are also shown to provide a warning of what may happen if the fault is not remedied. The cause and effect data needed by the package are obtained from a hazard and operability (HAZOP) study on the process plant. The result of the HAZOP study is recorded as cause and symptom equations which are translated into a data structure and stored in the computer as a file for the package to access. Probability values are assigned to the events that constitute the basic causes of any deviation. From these probability values, the a priori probabilities of occurrence of other events are evaluated. A top-down recursive algorithm, called TDRA, for evaluating the probability of every event in a fault tree has been developed. From the a priori probabilities, the conditional probabilities of the causes of the fault are then evaluated using Bayes' conditional probability theorem. The posteriori probability values could then be used by the operators to check in an orderly manner the cause of the fault. The package has been tested using the results of a HAZOP study on a pilot distillation plant. The results from the test show how easy it is to trace the chain of events that leads to the primary cause of a fault. This method could be applied in a real process environment.
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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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Jalel, Nameer Adnan. "Fault diagnosis and accident analysis in nuclear power plant." Thesis, University of Sheffield, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.335950.

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Lardner, Richard. "Early diagnosis and detection of Eutypa dieback of grapevines." Title page, table of contents and abstract only, 2003. http://hdl.handle.net/2440/37969.

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Eutypa dieback of grapevines, caused by Eutypa lata, is a major cause of reduced longevity in vineyards worldwide. The fungus grows in the woody tissue of infected vines, producing translocatable toxins that cause foliar symptoms of the disease. By the time foliar symptoms are evident the pathogen may have become well established in the vine. One aim of this study was to develop DNA markers to allow rapid reliable identification of E. lata and to detect the pathogen in infected wood. The second aim was to analyse secondary metabolite production by E. lata in order to gain information on the compounds responsible for the foliar symptoms of the disease and to identify metabolites which could be used as markers to detect the early stages of the disease prior to the expression of foliar symptoms. In addition, genetic variation of the pathogen was assessed using RFLP and RAPD analysis. Two techniques were used to develop DNA markers; first, SCAR markers derived from RAPD fragments were developed and, second, an E. lata genomic DNA library was constructed, from which DNA fragments specific to E. lata were identified. These markers were used in either PCR- or Southern hybridisation-based assays to detect the pathogen in infected wood. PCR-based detection of the pathogen in infected wood was prone to inhibition by phenolic compounds, however, Southern hybridisation techniques were capable of detecting E. lata in wood. Genetic variation among 38 isolates of E. lata was assessed using six randomly selected clones from the genomic DNA library. A subset of 11 isolates was subjected to RAPD analysis using 10 random primers. Considerable genetic diversity, in terms of RFLP and RAPD profiles, was observed among isolates. There was no apparent correlation between grouping of isolates following neighbour joining analysis and either host species or geographic origin of isolates. The RAPD and RFLP profiles of two isolates differed significantly from the majority of the other isolates. These isolates, which were morphologically similar to all other isolates, were subsequently found not to be E. lata. Secondary metabolite production of 11 isolates was analysed by HPLC following growth on a range of media. A wider range of secondary metabolites was detected in E. lata than has previously been reported. Two of the secondary metabolites, eutypine and an unidentified compound with a retention time of 19.6 min, were produced by eight of nine isolates of E. lata. Neither of the non-E. lata isolates produced these compounds. It was concluded that the remaining isolate of E. lata may have lost the ability to produce these compounds following storage. Whilst a wider range of isolates needs to be screened before a candidate marker can be selected, these results suggest that certain compounds are present in the majority of E. lata isolates and, hence, may prove suitable markers for the detection of the pathogen prior to the expression of foliar symptoms. The molecular probes developed in this study will allow the rapid and reliable identification and detection of E. lata in grapevine cane or wood. These probes also have the potential to be used as a research tool to gather information on the epidemiology of the disease and to assess the efficacy of potential control agents against E. lata. Suitable control measures could then be applied to vines which have been shown by the use of chemical markers to have latent infection. Used in combination, therefore, the DNA and biochemical markers could facilitate improved management of eutypa dieback.
Thesis (Ph.D.) -- University of Adelaide, School of Agriculture and Wine, 2003.
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Pramanik, Saugata. "A Hybrid Knowledge-Based System for Process Plant Fault Diagnosis." Thesis, Indian Institute of Science, 1989. http://hdl.handle.net/2005/83.

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Knowledge-Based Systems (KBSs) represent a relatively new programming approach and methodology that has evolved and is still evolving as an important sub-area of Artificial Intelligence (AI) research. The most prevalent application of KBSs, which emerged in recent times, has been various types of diagnosis and troubleshooting. KBS has an important role to play, particularly in fault diagnosis of process plants, which involve lot of challenges starting from commonly occurring malfunctions to rarely occurring emergency situations. The KBS approach is promising for this domain as it captures efficient problem-solving of experts, guides the human operator in rapid fault detection, explains the line of reasoning to the human operator, and supports modification and refinement of the process knowledge as experience is gained. However, most of the current KBSs in process plants are built on expert knowledge compiled in the form of production rules. These systems lack flexibility due to their process-specific nature and are unreliable when faced with unanticipated faults. Although attempts have been made to integrate knowledge based on experience and 'deep' process knowledge to overcome this lack of flexibility, very little work has been reported to make the diagnostic system flexible and usable for various plant configurations. In this thesis, we propose a hybrid knowledge framework which includes both process-specific and process-common knowledge of the structure and behavior of the domain, and a process-independent diagnostic mechanism based on causal and qualitative reasoning. This framework is flexible and allows a unified design methodology for fault diagnosis of process plants. The process-specific knowledge includes experiential knowledge about commonly occurring faults, behavioral knowledge about causal interactions among process-dependent variables, and structural knowledge about components' description and connectivity. The process-common knowledge comprises template models of various types of components commonly present in any process plant, constraints and confluences based on mass and energy balances between parameters across components. The process behavioral knowledge is qualitatively represented in the form of Signed Digraph (SDG), which is converted into a set of rules (SDGrules), added with control premises for the purpose of diagnostic reasoning. Frame-objects are used to represent the structural knowledge, while rules are used to capture experiential knowledge about common faults. An interface program viz., Knowledge Acquisition Interface (KAI) aids acquisition and conversion of (i) behavioral knowledge into a set of SDG-rules and (ii) structural knowledge and experience-based heuristic rules into a set of facts. The Diagnostic Mechanism is based on a steady state model of the process and is composed of three consecutive phases for locating a fault. The first phase is Malfunction Block Identification (MBT), which locates a malfunctioning subsystem or Malfunction Block (MB) that is responsible for causing the process malfunction. It is based on alarm data whenever violation of process parameters occurs. Once the suspected MB is identified, the second phase viz., Malfunction Parameter Identification (MPI) is invoked t o locate parameters which indicate the prime cause(s) of the fault in that MB. This is achieved by correlating various instrumentation data through causal relationships described by the SDG-rules of that MB. Finally, Malfunctioning Component Identification (MCI) phase is invoked to locate the malfunctioning component. MCI phase uses the malfunction parameter (s) obtained from previous phase and experiential and structural knowledge of that MA for this purpose. The Diagnostic Mechanism is process-independent and, therefore, is capable of adapting to various types of plant configurations. Since, the Knowledge Base and the Diagnostic Mechanism are separate, modification of either of them can be done independently. The Diagnostic Mechanism is potentially capable of investigating symptoms that have multiple or unrelated origins. It also provides explanation facility for justifying the line of diagnostic reasoning to the human operator.
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Hughes, Kelvin J. D. "Molecular methods for the diagnosis of fungal quarantine plant pathogens." Thesis, University of Bristol, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.272031.

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Gemmell, Brian David. "A consultative expert system for intelligent diagnosis on steam turbine plant." Thesis, University of Strathclyde, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340915.

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Sharif, Mohamed Abdulla Mohamed. "Application of intelligent instrumentation in process plant condition monitoring and fault diagnosis." Thesis, Cardiff University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340896.

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Göpfert, Johannes Georg. "Model-based fault diagnosis via structural analysis of a reverse osmosis plant." Master's thesis, Pontificia Universidad Católica del Perú, 2021. http://hdl.handle.net/20.500.12404/19043.

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Water desalination is one approach to force water scarcity. One of the processes used for desalination is reverse osmosis. Like other systems, a reverse osmosis plant is susceptible to faults. A fault can lead to a loss of efficiency, or if the fault is severe to a total breakdown. Appropriate measures can minimize the impact of faults, but this requires in time fault detection. The following thesis shows a proposal for an online fault diagnosis system of a reverse osmosis plant. For the model-based approach, a mathematical model of a reverse osmosis plant has been developed. The model contains a new approach for modeling the interaction between the high-pressure pump, the brine valve, and the membrane module. Furthermore, six faults considered for fault diagnosis have been modeled. Two of the faults are plant faults: The leakage of the feed stream and membrane fouling. The other four faults are sensor or actuator malfunctions. The fault diagnosis system is developed via structural analysis, a graph-based approach to determine a mathematical model’s overdetermined systems of equations. With the structural analysis, 73 fault-driven minimal structurally overdetermined (FMSO) sets have been determined. The results show that all six faults are detectable. However, two faults are not isolable. Five of the FMSO sets have been chosen to deduce the residuals used for online fault detection and isolation. The simulations demonstrate that the calculated residuals are appropriate to detect and isolate the faults. If one assumes that only the considered faults occur, it is possible to determine some faults’ magnitude.
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SILVA, AUCYONE A. da. "An integrated approach for plant monitoring and diagnosis using multiresolution wavelet analysis." reponame:Repositório Institucional do IPEN, 1997. http://repositorio.ipen.br:8080/xmlui/handle/123456789/11643.

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Tese (Doutoramento)
IPEN/T
The University of Tennessee
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Books on the topic "Plant diagnosis"

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Matthews, R. E. F. 1921-, ed. Diagnosis of plant virus diseases. Boca Raton: CRC Press, 1993.

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Fisal, Zahedi Bin. Real-time process plant fault diagnosis. Birmingham: Aston University. Department of Chemical Engineering and Applied Chemistry, 1989.

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Narayanasamy, P. Plant pathogen detection and disease diagnosis. 2nd ed. New York: M. Dekker, 2001.

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Narayanasamy, P. Plant pathogen detection and disease diagnosis. New York: Marcel Dekker, 1997.

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Chase, A. R. Foliage plant diseases: Diagnosis and control. St. Paul, Minn: APS Press, 1997.

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Dehne, H. W., G. Adam, M. Diekmann, J. Frahm, A. Mauler-Machnik, and P. van Halteren, eds. Diagnosis and Identification of Plant Pathogens. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-009-0043-1.

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Narayanasamy, P. Microbial Plant Pathogens-Detection and Disease Diagnosis:. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-9735-4.

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Narayanasamy, P. Microbial Plant Pathogens-Detection and Disease Diagnosis:. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-9754-5.

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Narayanasamy, P. Microbial Plant Pathogens-Detection and Disease Diagnosis:. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-90-481-9769-9.

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J, Ritchie B., and Holderness M, eds. Plant clinic handbook. Oxon: CAB International, 1998.

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Book chapters on the topic "Plant diagnosis"

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Fox, R. T. V. "Plant disease diagnosis." In The Epidemiology of Plant Diseases, 14–41. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-017-3302-1_2.

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Munk, Lisa, David B. Collinge, Annika Djurle, and Anne Marte Tronsmo. "Diagnosis of plant diseases." In Plant pathology and plant diseases, 164–81. Wallingford: CABI, 2020. http://dx.doi.org/10.1079/9781789243185.0164.

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Browning, Isla A. "Bioassay for Diagnosis of Plant Viruses." In Plant Pathology, 1–13. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-062-1_1.

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Tomlinson, Jenny, and Neil Boonham. "Real-Time LAMP for Chalara fraxinea Diagnosis." In Plant Pathology, 75–83. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2620-6_6.

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Völz, Ronny, and Rita Groß-Hardt. "Female Gametophytic Mutants: Diagnosis and Characterization." In Plant Developmental Biology, 143–53. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-60761-765-5_10.

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Santos, Anésia A., Lilian H. Florentino, Acássia B. L. Pires, and Elizabeth P. B. Fontes. "Geminivirus: Biolistic Inoculation and Molecular Diagnosis." In Plant Virology Protocols, 563–79. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-102-4_39.

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Voigt, K., S. Schleier, and J. Wöstemeyer. "Molecular diagnosis of rape seed pathogens." In Developments in Plant Pathology, 195–98. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-009-0043-1_41.

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Mule’, Giuseppina, and Antonio Logrieco. "Molecular Diagnosis of Toxigenic Fusarium Species." In Developments in Plant Pathology, 213–17. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-009-0043-1_45.

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Zamora-Ballesteros, Cristina, Reinaldo Pire, and Julio Javier Diez. "Field and Laboratory Procedures for Fusarium circinatum Identification and Diagnosis." In Plant Pathology, 51–73. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2517-0_3.

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Reid, Alex, Fiona Evans, Vincent Mulholland, Yvonne Cole, and Jon Pickup. "High-Throughput Diagnosis of Potato Cyst Nematodes in Soil Samples." In Plant Pathology, 137–48. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2620-6_11.

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Conference papers on the topic "Plant diagnosis"

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Mu, Yu, and Hong Xia. "A Study on Fault Diagnosis Technology of Nuclear Power Plant Based on Decision Tree." In 18th International Conference on Nuclear Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/icone18-29510.

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The technology of real-time fault diagnosis for NPP has great significance to improve the safety and economy of reactor. At present, expert system, artificial neural network (ANN) and support vector machine (SVM) algorithms are most widely used in the field of NPP fault diagnosis. According to the shortcomings of expert systems, ANN and SVM, the decision tree algorithm is applied in the field of NPP fault diagnosis in this paper. ID3 and C4.5 are applied separately to learn from training samples which are the typical faults of NPP, and diagnose using the acquired knowledge. Then the diagnostic results are compared with the results of SVM method. The results show that: comparing with SVM, decision tree has the advantages of much faster training speed and a little higher accuracy. Furthermore, decision tree can obtain rules from the sample set, so it has good explanatory ability for the diagnostic results.
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Yu, Rui, Xianling Li, Mo Tao, and Zhiwu Ke. "Fault Diagnosis of Feedwater Pump in Nuclear Power Plants Using Parameter-Optimized Support Vector Machine." In 2016 24th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/icone24-60334.

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The condition monitoring of the feedwater pump in secondary circuit is critical to the safe operation of the nuclear power plant. This article presents a fault diagnosis method of feedwater pump by using parameter-optimized support vector machine (SVM). While the fault features of feedwater pump are reflected from the power spectrum of the vibration signals, we trained and diagnosed the fault feature table with support vector machine. The optimal penalty factor C and kernel parameter γ of support vector machine are selected by grid search and k-fold cross validation. Then the faults are diagnosed by the SVM model under the optimal parameters. Diagnostic results show that the parameter-optimized SVM method achieves higher diagnostic accuracy than the PNN method, exhibiting superior performance to effectively diagnose the faults of feedwater pump.
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Ke, Zhiwu, Xu Hu, Dawei Teng, and Mo Tao. "Intelligent Fault Diagnosis Method Based on Operating Parameters in Nuclear Power Plant." In 2017 25th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/icone25-66494.

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The safety of mechanical equipment is more important, it directly determines the safety of nuclear power plant operation, and even nuclear safety. So it is necessary to monitor the operating state of NPP system and mechanical equipment in real time by inspecting operating parameters. However, the key technology is real-time fault diagnosis of the mechanical equipment in NPP. Traditional fault diagnosis method based on analytic model is difficult to diagnose relevant and superimposed fault because of model error, disturbance and noise. This paper studies the application of fault diagnosis method based on BP neural network in NPP, and proposes an improved method for neural BP network method. For the feed-water system in the variable load operation process, we select the normal operation, the single feed-water valve fault, feed-water pump and feed-water valve superimposed fault as the analysis objects. One hundred points of data are extracted as BP algorithm training elements in these three processes averagely. The normal and abnormal conditions (including single fault and superimposed fault) can be accurately judged, but the single fault and superimposed failure would produce miscarriage of justice, about 2.4% of the single fault is diagnosed as superimposed fault, the diagnosis time delay is less than 1 second. These results meet the accuracy and real-time requirements. Then we study the application of support vector machine (SVM), which can make up for the deficiency of BP neural network. The results of this paper are useful for the real-time and reliable fault diagnosis of NPP.
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Liu, Yongchao, Xiangyu Li, Biao Liang, Bo Wang, Sichao Tan, and Puzhen Gao. "Research on Accident Diagnosis Method of Reactor System Based on XGBoost Using Bayesian Optimization." In 2022 29th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/icone29-92061.

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Abstract Traditional machine learning algorithms have problems such as overfitting, low accuracy, and difficulty in hyperparameter optimization when performing fault diagnosis.In order to improve the accident diagnosis ability of nuclear power plant reactor system, this paper combines Bayesian optimization (BO) algorithm with eXtreme Gradient Boosting (XGBoost) algorithm to develop a reactor accident diagnosis model.First, data preprocessing and feature quantity analysis are performed on accident data samples.Then, the BO algorithm is used to optimize the hyperparameters of the XGBoost model. Finally, the BO-XGBoost model is used to diagnose the operating conditions of seven nuclear power plants, and the diagnostic effects of various traditional machine learning classification algorithms are compared and analyzed.The results show that the BO-XGBoost model can achieve more efficient and accurate identification of reactor accident types, and the model has better generalization ability.This research can help nuclear power plant operators to accurately identify the types of reactor accidents, assist decision-making, and ensure the safe operation of nuclear power plants.
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Petrellis, Nikos. "Plant Disease Diagnosis with Color Normalization." In 2019 8th International Conference on Modern Circuits and Systems Technologies (MOCAST). IEEE, 2019. http://dx.doi.org/10.1109/mocast.2019.8741614.

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Correas, Luis, Ángel Martínez, and Antonio Valero. "Operation Diagnosis of a Combined Cycle Based on the Structural Theory of Thermoeconomics." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0848.

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Abstract Diagnosis of the performance of energy was theoretically developed based on the Structural Theory (Valero, Serra and Lozano, 1993), and traditionally Thermoeconomics have usually been applied to the design of power plants and comparison between alternatives. However, the application of thermoeconomic techniques to actual power plants has always to face the generally poor quality of measurement readings from the standard field instrumentation as an unavoidable first step. The proposed methodology focuses on measurement uncertainty estimation and performance calculation by means of data reconciliation techniques, in order to obtain the most confident plant balance upon the available instrumentation. The formulation of the Structural Theory has been applied to a combined cycle, where the Fuel-Product relationships at the component level must be optimally defined for a correct malfunction interpretation. This set of relationships determines the ability to diagnose and the level of the diagnostics obtained. The paper reports the application of the methodology to a 280 MW rated combined cycle, where performance diagnosis is illustrated with results from a collection of actual operation data sets. The results show that data reconciliation yields sufficient accuracy to conduct a thermoeconomic analysis, and how the estimated impact on fuel correlates with physical causes. Hence the feasibility of thermoeconomic analysis of plant operation is demonstrated.
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Kanada, Masaki, Ryota Kamoshida, Yoshihiko Ishii, Tadaaki Ishikawa, Setsuo Arita, and Kenichi Katono. "Development of Inherently Safe Technologies for Large Scale BWRs: (5) Operation Support System for Plant Accidents." In 2014 22nd International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/icone22-31104.

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When accident events are caused by a large-scale natural disaster, conditions beyond those at the plant site may affect the accident. As well, quick diagnosis and recognition of damaged equipment are necessary. We have been developing inherently safe technologies for boiling water reactor (BWR) plants in response to these. An operation support system for plant accident events is one of these technologies. Our operation support system identifies accident events and predicts the progression of plant behavior. The system consists of three main functions: sensor integrity diagnosis, accident event identification, and plant simulation functions. The sensor integrity diagnosis function diagnoses whether sensor signals have maintained their integrity by correlating redundant sensors with the plant design information. The accident event identification function extracts a few of candidate accident events using alarm and normal sensor signals received by the sensor integrity diagnosis function. The scale and position of the accident event are determined by comparing plant simulation results with normal sensor signals. The plant simulation function uses a detailed three-dimensional model of the nuclear reactor and plant. This simulation can predict future plant behavior on the basis of identified accident events. This proposed operation support system provides available results of accident event identification and plant condition prediction to plant operators. This system will reduce the occurrence of false identifications of accident events and human errors of operators.
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Yan, Ying, Peter B. Luh, and Krishna R. Pattipati. "Chiller plant fault diagnosis considering fault propagation." In 2015 International Conference on Complex Systems Engineering (ICCSE). IEEE, 2015. http://dx.doi.org/10.1109/complexsys.2015.7385977.

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Figueroa Ibarra, Luis R., J. Hugo Rodri´guez Marti´nez, Ce´sar A. Romo Millares, and Laura E. Sa´nchez Herna´ndez. "Using Integral Power Plant Diagnosis to Boost Maintenance Efficiency." In ASME 2006 Power Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/power2006-88103.

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Significant decrements in generated power at fossil fuel power plants occur in the few months following annual maintenance which, besides causing economic losses for the power plants, reduce their availability. In order to determine the causes of these decrements, it is a common practice to carry out tests to evaluate the performance of the equipment in which the problem supposedly originates. Because these tests are made individually in the equipment, it is not possible to have an integral vision of the plant operation as a whole and, therefore, the problem related to energy efficiency is not attacked from the root [1]. This paper shows a practical method using in-situ measurements and a commercial simulation computer tool that allows the power plant operators to make an integral thermodynamic assessment. It makes possible to identify the causes of efficiency decrease (for the whole plant and its components) and to quantify the contribution of each equipment to the total power loss. As a result, priorities on the maintenance of the equipment can be determined to tackle the most important energy losses, and obtaining a total solution to the problem of energy decrement. The paper includes the results obtained from the application of this methodology to assess a 158 MW fossil fuel power plant unit in Mexico.
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Cerri, Giovanni, Sandra Borghetti, and Coriolano Salvini. "Models for Simulation and Diagnosis of Energy Plant Components." In ASME 2006 Power Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/power2006-88146.

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This paper describes a methodology to set up models for simulation and diagnosis of energy plant components. The adopted approach consists in a simultaneous solution of modules representing plant components taking their actual behavior into account. Models are characterized by Reality Functions to adapt them to the reality of machines and apparatuses so that the New&Clean map of the real component can be established. Furthermore, to account for deterioration phenomena occurring during plant operations, Actuality Functions affecting component performance in terms of work and heat transfer, losses and effective flow functions have been introduced. Models have been validated and tested against real CHP plant data. Two applications to different kinds of power plants are presented and discussed. Results show a good capability to estimate component deterioration statuses and reproduce component actual behavior maps.
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Reports on the topic "Plant diagnosis"

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Araseethota Manjunatha, Koushik, Andrea L. Mack, Vivek Agarwal, David Koester, and Douglas Adams. Diagnosis of Corrosion Process in Nuclear Power Plant Secondary Piping Structures. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1616098.

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Jordan, Ramon L., Abed Gera, Hei-Ti Hsu, Andre Franck, and Gad Loebenstein. Detection and Diagnosis of Virus Diseases of Pelargonium. United States Department of Agriculture, July 1994. http://dx.doi.org/10.32747/1994.7568793.bard.

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Pelargonium (Geranium) is the number one pot plant in many areas of the United States and Europe. Israel and the U.S. send to Europe rooted cuttings, foundation stocks and finished plants to supply a certain share of the market. Geraniums are propagated mainly vegetatively from cuttings. Consequently, viral diseases have been and remain a major threat to the production and quality of the crop. Among the viruses isolated from naturally infected geraniums, 11 are not specific to Pelargonium and occur in other crops while 6 other viruses seem to be limited to geranium. However, several of these viruses are not sufficiently characterized to conclude that they are distinct agents and their nomenclature and taxonomy are confusing. The ability to separate, distinguish and detect the different viruses in geranium will overcome obstacles te developing effective detection and certification schemes. Our focus was to further characterize some of these viruses and develop better methods for their detection and control. These viruses include: isolates of pelargonium line pattern virus (PLPV), pelargonium ringspot virus (PelRSV), pelargonium flower break virus (PFBV), pelargonium leaf curl (PLCV), and tomato ringspot virus (TomRSV). Twelve hybridoma cell lines secreting monoclonal antibodies specific to a geranium isolate of TomRSV were produced. These antibodies are currently being characterized and will be tested for the ability to detect TomRSV in infected geraniums. The biological, biochemical and serological properties of four isometric viruses - PLPV, PelRSV, and PFBV (and a PelRSV-like isolate from Italy called GR57) isolated from geraniums exhibiting line and ring pattern or flower break symptoms - and an isolate ol elderbeny latent virus (ELV; which the literature indicates is the same as PelRSV) have been determined Cloned cDNA copies of the genomic RNAs of these viruses were sequenced and the sizes and locations of predicted viral proteins deduced. A portion of the putative replicase genes was also sequenced from cloned RT-PCR fragments. We have shown that, when compared to the published biochemical and serological properties, and sequences and genome organizations of other small isometric plant viruses, all of these viruses should each be considered new, distinct members of the Carmovirus group of the family Tombusviridae. Hybridization assays using recombinant DNA probes also demonstrated that PLPV, PelRSV, and ELV produce only one subgenomic RNA in infected plants. This unusual property of the gene expression of these three viruses suggests that they are unique among the Carmoviruses. The development of new technologies for the detection of these viruses in geranium was also demonstrated. Hybridization probes developed to PFBV (radioactively-labeled cRNA riboprobes) and to PLPV (non-radioactive digoxigenin-labeled cDNAs) were generally shown to be no more sensitive for the detection of virus in infected plants than the standard ELISA serology-based assays. However, a reverse transcriptase-polymerase chain reaction assay was shown to be over 1000 times more sensitive in detecting PFBV in leaf extracts of infected geranium than was ELISA. This research has lead to a better understanding of the identity of the viruses infecting pelargonium and to the development of new tools that can be used in an improved scheme of providing virus-indexed pelargonium plants. The sequence information, and the serological and cloned DNA probes generated from this work, will allow the application of these new tools for virus detection, which will be useful in domestic and international indexing programs which are essential for the production of virus-free germplasm both for domestic markets and the international exchange of plant material.
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Clayton, Dwight A., and Willis P. Poore, III. Key Parameters for Operator Diagnosis of BWR Plant Condition during a Severe Accident. Office of Scientific and Technical Information (OSTI), January 2015. http://dx.doi.org/10.2172/1185624.

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Xue, Feng, Hao Huang, Yiwei Fu, Bojun Feng, Weizhong Yan, and Tianyi Wang. Deep Analysis Net with Causal Embedding for Coal-fired power plant Fault Detection and Diagnosis (DANCE4CFDD). Office of Scientific and Technical Information (OSTI), February 2022. http://dx.doi.org/10.2172/1844966.

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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.
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Splitter, Gary, and Menachem Banai. Microarray Analysis of Brucella melitensis Pathogenesis. United States Department of Agriculture, 2006. http://dx.doi.org/10.32747/2006.7709884.bard.

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Original Objectives 1. To determine the Brucella genes that lead to chronic macrophage infection. 2. To identify Brucella genes that contribute to infection. 3. To confirm the importance of Brucella genes in macrophages and placental cells by mutational analysis. Background Brucella spp. is a Gram-negative facultative intracellular bacterium that infects ruminants causing abortion or birth of severely debilitated animals. Brucellosis continues in Israel, caused by B. melitensis despite an intensive eradication campaign. Problems with the Rev1 vaccine emphasize the need for a greater understanding of Brucella pathogenesis that could improve vaccine designs. Virulent Brucella has developed a successful strategy for survival in its host and transmission to other hosts. To invade the host, virulent Brucella establishes an intracellular niche within macrophages avoiding macrophage killing, ensuring its long-term survival. Then, to exit the host, Brucella uses placenta where it replicates to high numbers resulting in abortion. Also, Brucella traffics to the mammary gland where it is secreted in milk. Missing from our understanding of brucellosis is the surprisingly lillie basic information detailing the mechanisms that permit bacterial persistence in infected macrophages (chronic infection) and dissemination to other animals from infected placental cells and milk (acute infection). Microarray analysis is a powerful approach to determine global gene expression in bacteria. The close genomic similarities of Brucella species and our recent comparative genomic studies of Brucella species using our B. melitensis microarray, suqqests that the data obtained from studying B. melitensis 16M would enable understanding the pathogenicity of other Brucella organisms, particularly the diverse B. melitensis variants that confound Brucella eradication in Israel. Conclusions Results from our BARD studies have identified previously unknown mechanisms of Brucella melitensis pathogenesis- i.e., response to blue light, quorum sensing, second messenger signaling by cyclic di-GMP, the importance of genomic island 2 for lipopolysaccharide in the outer bacterial membrane, and the role of a TIR domain containing protein that mimics a host intracellular signaling molecule. Each one of these pathogenic mechanisms offers major steps in our understanding of Brucella pathogenesis. Strikingly, our molecular results have correlated well to the pathognomonic profile of the disease. We have shown that infected cattle do not elicit antibodies to the organisms at the onset of infection, in correlation to the stealth pathogenesis shown by a molecular approach. Moreover, our field studies have shown that Brucella exploit this time frame to transmit in nature by synchronizing their life cycle to the gestation cycle of their host succumbing to abortion in the last trimester of pregnancy that spreads massive numbers of organisms in the environment. Knowing the bacterial mechanisms that contribute to the virulence of Brucella in its host has initiated the agricultural opportunities for developing new vaccines and diagnostic assays as well as improving control and eradication campaigns based on herd management and linking diagnosis to the pregnancy status of the animals. Scientific and Agricultural Implications Our BARD funded studies have revealed important Brucella virulence mechanisms of pathogenesis. Our publication in Science has identified a highly novel concept where Brucella utilizes blue light to increase its virulence similar to some plant bacterial pathogens. Further, our studies have revealed bacterial second messengers that regulate virulence, quorum sensing mechanisms permitting bacteria to evaluate their environment, and a genomic island that controls synthesis of its lipopolysaccharide surface. Discussions are ongoing with a vaccine company for application of this genomic island knowledge in a Brucella vaccine by the U.S. lab. Also, our new technology of bioengineering bioluminescent Brucella has resulted in a spin-off application for diagnosis of Brucella infected animals by the Israeli lab by prioritizing bacterial diagnosis over serological diagnosis.
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Wei, T. Y. C. On-line plant transient diagnostics and management. Office of Scientific and Technical Information (OSTI), December 1994. http://dx.doi.org/10.2172/36669.

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Hanson, David Lester. Diagnostics development plan for ZR. Office of Scientific and Technical Information (OSTI), September 2003. http://dx.doi.org/10.2172/918272.

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Needham, Glenn R., Uri Gerson, Gloria DeGrandi-Hoffman, D. Samatero, J. Yoder, and William Bruce. Integrated Management of Tracheal Mite, Acarapis woodi, and of Varroa Mite, Varroa jacobsoni, Major Pests of Honey Bees. United States Department of Agriculture, March 2000. http://dx.doi.org/10.32747/2000.7573068.bard.

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Objectives: The Israeli work plan regarding HBTM included: (a) producing a better diagnostic method; (b) following infestations during the season and evaluating damage to resistant bees and, (c) controlling HBTM by conventional means under local conditions. For varroa our plans to try novel control (e.g. oil novel control (e.g. oil patties & essential oils) were initially delayed by very low pest populations, then disrupted by the emergence of fluvalinate resistance. We monitored the spread of resistance to understand it better, and analyzed an underlying biochemical resistance mechanism in varroa. The US work plan focused on novel management methods for both mites with an emphasis on reducing use of traditional insecticides due to resistance and contamination issues. Objectives were: (a) evaluating plant essential oils for varroa control; (b) exploring the vulnerability of varroa to desiccation for their management; and (c) looking for biological variation in HBTM that could explain virulence variability between colonies. Although the initial PI at the USDA Beltsville Bee Lab, W.A. Bruce, retired during the project we made significant strides especially on varroa water balance. Subcontracts were performed by Yoder (Illinois College) on varroa water balance and DeGrandi-Hoffman (USDA) who evaluated plant essential oils for their potential to control varroa. We devised an IPM strategy for mite control i the U.S. Background: Mites that parasitize honey bees are a global problem. They are threatening the survival of managed and feral bees, the well-being of commercial/hobby beekeeping, and due to pollination, the future of some agricultural commodities is threatened. Specific economic consequences of these mites are that: (a) apiculture/breeder business are failing; (b) fewer colonies exist; (c) demand and cost for hive leasing are growing; (d) incidences of bee pathogens are increasing; and, (e) there are ore problems with commercial-reared bees. As a reflection of the continued significance f bee mites, a mite book is now in press (Webster & delaplane, 2000); and the 2nd International Conference on Africanized Honey Bees and Bee Mites is scheduled (April, 2000, Arizona). The first such conference was at OSU (1987, GRN was co-organizer). The major challenge is controlling two very different mites within a colony while not adversely impacting the hive. Colony management practices vary, as do the laws dictating acaricide use. Our basic postulates were that: (a) both mites are of economic importance with moderate to high infestations but not at low rates and, (b) once established they will not be eradicated. A novel strategy was devised that deals with the pests concomitantly by maintaining populations at low levels, without unnecessary recourse to synthetic acaricides. Major Conclusions, Solutions, Achievements: A major recent revelation is that there are several species of "Varroa jacobsoni" (Anderson & Trueman 1999). Work on control, resistance, population dynamics, and virulence awaits knowing whether this is a problem. In the U.S. there was no difference between varroa from three locales in terms of water balance parameters (AZ, MN & PA), which bodes well for our work to date. Winter varroa (U.S.) were more prone to desiccation than during other seasons. Varroa sensitivity to desiccation has important implications for improving IPM. Several botanicals showed some promise for varroa control (thymol & origanum). Unfortunately there is varroa resistance to Apistan in Israel but a resistance mechanism was detected for the first time. The Israel team also has a new method for HBTM diagnosis. Annual tracheal mite population trends in Israel were characterized, which will help in targeting treatment. Effects of HBTM on honey yields were shown. HBTM control by Amitraz was demonstrated for at least 6 months. Showing partial resistance by Buckfast bees to HBTM will be an important IPM tactic in Israel and U.S.
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M.G. Bell, R.E. Bell, B.P. LeBlanc, and S.S. Medley. NSTX Diagnostics and Operation: Status and Plans. Office of Scientific and Technical Information (OSTI), August 2001. http://dx.doi.org/10.2172/788201.

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