Статті в журналах: "Machine Diagnostic"

1

Duda, Arkadiusz, and Maciej Sułowicz. "A New Effective Method of Induction Machine Condition Assessment Based on Zero-Sequence Voltage (ZSV) Symptoms." Energies 13, no. 14 (July 9, 2020): 3544. http://dx.doi.org/10.3390/en13143544.

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Non-invasive diagnostic methods for electric machines’ diagnostics, which can be used during their operation in a drive system, are needed in many branches of the production industry. For the reliable condition assessment of electric machines, especially those operating in drive systems, various tools and methods have been suggested. One diagnostic method that has not been fully recognized and documented is a diagnostic method based on zero-sequence voltage component (ZSV) applications for the condition assessment of induction machines. In this paper, the application of ZSV in induction machine diagnostics is proposed. A factor that speaks in favor of applying this signal in such diagnostics is the high sensitivity of the signal to damage occurrence, and the distinct change of extracted symptoms in the case of asymmetry. It is possible to obtain a high signal amplitude, which simplifies its processing and the elaboration of reliable diagnostic factors. This ZSV-based method is also able to be applied to big machines used in industry. Due to the saturation effects visible in the ZSV signal, new diagnostic symptoms can appear, which allows for an easier condition assessment of certain machines. The usefulness of the described diagnostic method in machine condition assessment was shown through an equivalent circuit modeling process, finite element analysis, and laboratory tests of the machine.

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2

Frosini, Lucia. "Novel Diagnostic Techniques for Rotating Electrical Machines—A Review." Energies 13, no. 19 (September 27, 2020): 5066. http://dx.doi.org/10.3390/en13195066.

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This paper aims to update the review of diagnostic techniques for rotating electrical machines of different type and size. Each of the main sections of the paper is focused on a specific component of the machine (stator and rotor windings, magnets, bearings, airgap, load and auxiliaries, stator and rotor laminated core) and divided into subsections when the characteristics of the component are different according to the type or size of the machine. The review considers both the techniques currently applied on field for the diagnostics of the electrical machines and the novel methodologies recently proposed by the researchers in the literature.

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Veselovska, Nataliia. "DEVELOPMENT OF ALGORITHMIC SUPPORT FOR PRACTICAL IMPLEMENTATION OF TESTING AND DIAGNOSTIC COMPLEX OF CNC MACHINES." Vibrations in engineering and technology, no. 1(104) (April 29, 2022): 71–80. http://dx.doi.org/10.37128/2306-8744-2022-1-9.

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CNC machines, including multi-purpose machines (product processing centers), have been widely used in connection with the creation of flexible production systems and significant progress in developing and improving the reliability of multi-purpose freely programmable, multiprocessor and small numerical program control devices (CNC) , so the development of algorithmic software for the practical implementation of the test and diagnostic complex and the use of diagnostic systems as a means of automation of information technology is one of the general directions of improving the efficiency of machine-building enterprises. However, in order to make decisions about the scope, stages and feasibility of using a particular diagnostic system for the selected object of study at a particular enterprise, it is necessary to assess its expected benefits. The use of diagnostics during operation of the machine imposes its influence on the means and methods of diagnosis, which should be convenient for use in the factory, to ensure the diagnosis process in the shortest time, to have reliable readings, especially with high requirements for product reliability. and in some cases without disrupting the mechanism, to be economically feasible. The design of the machine is adapted to the needs of diagnostics and must: have built-in devices that evaluate its parameters (pressure in the hydraulic system, temperature of energy-intensive units, accuracy of machining, speed); periodically connect to special equipment that will diagnose the basic parameters of the machine and provide data on its condition. The parameters of the technical condition (diagnostic features), which can be judged on the OD and which are diagnosed during operation of the machine, are: machine parameters that directly characterize its efficiency; damage and defects that occur during operation and lead or may lead to failure; side effects that are functionally or stochastically related to the initial parameters.

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4

VISHNEVSKY, A. A., I. I. ARTOBOLEVSKY, and M. L. BYKHOVSKY. "Principles of Diagnostic Machine Construction1." Acta Medica Scandinavica 176, no. 2 (April 24, 2009): 129–35. http://dx.doi.org/10.1111/j.0954-6820.1964.tb00919.x.

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5

Зимовець, Вікторія Ігорівна, Олександр Сергійович Приходченко та Микита Ігорович Мироненко. "ІНФОРМАЦІЙНО-ЕКСТРЕМАЛЬНИЙ КЛАСТЕР-АНАЛІЗ ВХІДНИХ ДАНИХ ПРИ ФУНКЦІОНАЛЬНОМУ ДІАГНОСТУВАННІ". RADIOELECTRONIC AND COMPUTER SYSTEMS, № 4 (25 грудня 2019): 105–15. http://dx.doi.org/10.32620/reks.2019.4.12.

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The study aims to increase the functional efficiency of machine learning of the functional diagnosis system of a multi-rope shaft hoist through cluster analysis of diagnostic features. To achieve the goal, it was necessary to solve the following tasks: formalize the formulation of the task of information synthesis, capable of learning a functional diagnosis system, which operates in the cluster-analysis mode of diagnostic signs; to propose a categorical model and, on its basis, to develop an algorithm for information-extreme cluster analysis of diagnostic signs in the process of information-extreme machine learning of a functional diagnostic system; carry out fuzzification of input fuzzy data by optimizing the geometric parameters of hyperspherical containers of recognition classes that characterize the possible technical conditions of the diagnostic object; to develop an algorithm and implement it on the example of information synthesis of the functional diagnostics system of a multi-rope mine hoisting machine. The object of the study is the processes of information synthesis of a functional diagnostic system capable of learning, integrated into the automated control system of a multi-rope mine hoisting machine. The subject of the study is categorical models, an information-extremal machine learning algorithm of a functional diagnostic system that operates in the cluster analysis model of diagnostic signs and constructs decision rules. The research methods are based on the ideas and methods of information-extreme intellectual data analysis technology, a theoretical-informational approach to assessing the functional effectiveness of machine learning and on the geometric approach of pattern recognition theory. As a result, the following results were obtained: a categorical model was proposed, and on its basis, an algorithm for information-extremal machine learning of the functional diagnostics system for a multi-rope mine hoist was developed and implemented, which allows you to automatically generate an input classified fuzzy training matrix, which significantly reduces time and material costs when creating incoming mathematical description. The obtained result was achieved by cluster analysis of structured vectors of diagnostic signs obtained from archival data for three recognition classes using the k-means procedure. As a criterion for optimizing machine learning parameters, we considered a modified Kullback measure in the form of a functional on the exact characteristics of diagnostic solutions and distance criteria for the proximity of recognition classes. Based on the optimal geometric parameters of the containers of recognition classes obtained during machine learning, decisive rules were constructed that allowed us to classify the vectors of diagnostic features of recognition classes with a rather high total probability of making the correct diagnostic decisions. Conclusions. The scientific novelty of the results obtained consists in the development of a new method for the information synthesis of the functional diagnostics system of a multi-rope mine hoisting machine, which operates in the cluster analysis model, which made it possible to automatically form an input classified fuzzy training matrix with its subsequent dephasification in the process of information-extreme machine learning system.

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6

Nikitin, Yury, Pavol Božek, and Jozef Peterka. "Logical–Linguistic Model of Diagnostics of Electric Drives with Sensors Support." Sensors 20, no. 16 (August 8, 2020): 4429. http://dx.doi.org/10.3390/s20164429.

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The presented paper scientifically discusses the progressive diagnostics of electrical drives in robots with sensor support. The AI (artificial intelligence) model proposed by the authors contains the technical conditions of fuzzy inference rule descriptions for the identification of a robot drive’s technical condition and a source for the description of linguistic variables. The parameter of drive diagnostics for a robotized workplace that is proposed here is original and composed of the sum of vibration acceleration amplitudes ranging from a frequency of 6.3 Hz to 1250 Hz of a one-third-octave filter. Models of systems for the diagnostics of mechatronic objects in the robotized workplace are developed based on examples of CNC (Computer Numerical Control) machine diagnostics and mechatronic modules based on the fuzzy inference system, concluding with a solved example of the multi-criteria optimization of diagnostic systems. Algorithms for CNC machine diagnostics are implemented and intended only for research into precisely determined procedures for monitoring the lifetime of the mentioned mechatronic systems. Sensors for measuring the diagnostic parameters of CNC machines according to precisely determined measuring chains, together with schemes of hardware diagnostics for mechatronic systems are proposed.

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7

Fariz Qafarov, Fariz Qafarov, Elnarə Səlimova Elnarə Səlimova, and Aybəniz Əmirova Aybəniz Əmirova. "VIBRATION PROCESSES AND THEIR RELATIONSHIP WITH DEFECTS." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 11, no. 07 (November 5, 2021): 81–86. http://dx.doi.org/10.36962/pahtei1107202181.

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ABSTRACT The article is devoted to vibration diagnostics, an effective method for assessing the parameters of the mechanical state of centrifugal pumping units. The use of vibration diagnostics allows, due to early detection of malfunctions, to improve target operation, increase the turnaround time and reduce the likelihood of emergency destruction of pumping unit elements. Diagnostic signs of the presence of defects in various elements of the pumping unit are presented. During the development of diagnostic methods, the character stages of the development of injuries are selected. These stages are the formation of the injury, the accumulation of injuries, collapse, and so on. consists of stages. Dynamic forces are considered to be the main cause of vibration in machine parts. It is under the influence of dynamic forces that fatigue breaks down in machine parts. The use of vibrodiagnostics in machine parts allows to accurately assess the degree of damage to its individual nodes. This, in turn, leads to improved operating conditions. In conclusion, it should be noted that vibrodiagnostics not only detects malfunctions in machines, but also reveals the causes of its formation. Keywords: vibrodiagnostics, improvement of operational conditions, determination of defects, probability of emergency destruction, repair, assessment.

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8

Bartels, P. H., D. Thompson, H. G. Bartels, and R. Shoemaker. "Machine Vision System for Diagnostic Histopathology." Pathology - Research and Practice 185, no. 5 (December 1989): 635–46. http://dx.doi.org/10.1016/s0344-0338(89)80209-2.

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9

HRANIAK, Valerii, and Oleh HRYSHCHUK. "DEVELOPMENT OF THE CONCEPT OF BUILDING DIAGNOSTIC SYSTEMS OF ROTATING ELECTRICAL MACHINES UNDER THE CONDITIONS OF LIMITED INFORMATIONALITY OF DIAGNOSTIC SIGNS." Herald of Khmelnytskyi National University. Technical sciences 311, no. 4 (August 2022): 70–77. http://dx.doi.org/10.31891/2307-5732-2022-311-4-70-77.

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The article examines the peculiarities of the construction of systems for diagnosing rotating electric machines in the real conditions of their operation. It is shown that in the specified modes of operation there is a problem of limited informativeness of input information parameters that can be used to build such systems. At the same time, an additional limiting factor that must be considered when designing and implementing such equipment is the limited possibility of intervention in the design of the electric machine, which is usually limited to the manufacturing plant. As a result of a thorough analysis of the latest research in the direction of the development of diagnostic systems for rotating electric machines, a systematization of the technological parameters of electric machines that are most suitable for use in diagnostic systems was carried out. It is shown that when choosing input parameters of diagnostic systems, it is advisable to consider their informativeness, selectivity, expressiveness and complexity of the acquisition algorithm. At the same time, it is substantiated that the choice of the optimal combination of diagnostic features cannot be considered from the point of view of superposition, since each of them will be characterized by the entropy of selectivity and severity relative to defects of different types. The expediency of choosing the type of input information of diagnostic systems based on the method of evolutionary search is shown. It is demonstrated that the mentioned method allows to more completely cover the search space than, for example, gradient optimization methods, and to obtain a solution close to the optimal one in a relatively short time (a small number of iterations). The concept and typical structural diagram of the system for diagnosing rotating electric machines based on a modified non-standard artificial neural network (ANN) and the structure of the ANN itself, which considers the current mode of operation of the electric machine during diagnosis and is characterized by high adaptability to the object of diagnosis, are proposed. An example of its hardware implementation is given.

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10

Szabó, József Zoltán. "Forgógépek üzem közbeni mozgásának próbapadi és ipari vizsgálata." Jelenkori Társadalmi és Gazdasági Folyamatok 7, no. 1-2 (January 1, 2012): 73–79. http://dx.doi.org/10.14232/jtgf.2012.1-2.73-79.

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The significance of predictive maintenance and vibration diagnostic increasing continuously in every area of industry. Vibration diagnostics delivers considerable economic benefits for machinery operation. Vibration measurement can show up mechanical problems and harmful effects which acts on the parts throughout the operating period of the machine. This paper describes an alternative method of vibration diagnostic with ,,Vibshape"moving-animation software. With this program and an vibration analyser the moving of the machines, and the mechanical, structures can be visible. This article shows the basic knowledge of vibration diagnostic with animation program, and a case study from my practice. The case study shows the moving of the machines in main mechanical problems, for example resonance and electrical problem. Across of the case study we can see the resonance problem of a supporting structure and a big blower rotor bar crack.

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11

Kluczyk, Marcin, Andrzej Grządziela, and Tomislav Batur. "Diagnostic Model of the Marine Propulsion System." Applied Mechanics and Materials 817 (January 2016): 57–63. http://dx.doi.org/10.4028/www.scientific.net/amm.817.57.

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Naval propulsion systems are characterized by a high degree of complexity within a single system and a large variation between the solutions applied to individual vessels. In this situation, issues relating to the comprehensive diagnostic is a serious problem. Diagnostics models are useful to made the this problem easier. It should be emphasized that it is impossible to develop a universal model correct for all types of vessels. The paper presents general guidelines for the creation of diagnostic models. The results of first stage of studies on diagnostic model covers unit equipped with a twin-engine twin-shaft drive system had been presented.Introduction Changes of technical state of the machine occur as a result of its response to changes in the energy emitted by them. If qualitative and quantitative parameters of this energy are known diagnostician after proper analysis is able to determine the technical condition of the machine. It can be concluded that the technical diagnostics is a test of object response to the impact of energy causing change of its technical condition [9]. As far as the destruction of the object model is concerned we find that the degree of wear of the machine is proportional to the energy dissipated from it.

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12

Gasparjans, Aleksandrs, Aleksandrs Terebkovs, and Anastasia Zhiravetska. "Voltage Spectral Structure as a Parameter of System Technical Diagnostics of Ship Diesel Engine-Synchronous Generators." Electrical, Control and Communication Engineering 8, no. 1 (July 1, 2015): 37–42. http://dx.doi.org/10.1515/ecce-2015-0005.

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Abstract A method of technical diagnostics of ship diesel engine – generator installation – is proposed. Spectral-power diagnostic parameters of the synchronous generator voltage and currents are used. The electric machine in this case is the multipurpose sensor of diagnostic parameters. A judgment on the quality of the operational processes in diesel engine cylinders and its technical condition is possible on the basis of these parameters. This method is applicable to piston compressor installations with electric drive. On the basis of such parameters as rotating torque, angular speed and angular acceleration it is possible to estimate the quality of the operating process in the cylinders of a diesel engine, the condition of its cylinder-piston group and the crank gear mechanism. The investigation was realized on the basis of a diesel-generator with linear load. The generator operation was considered for the case of constant RL load. Together with the above mentioned, the condition of bearings of synchronous machines, uniformity of the air gap, windings of the electric machine were estimated during the experiments as well. The frequency spectrum of the stator current of the generator was researched and analyzed. In this case the synchronous machine is becoming a rather exact multipurpose diagnostic sensor. The signal of non-uniformity in the operation process of diesel engine cylinders and its technical condition is the increasing of the amplitudes of typical frequencies.

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Gizelska, Małgorzata, Dorota Kozanecka, and Zbigniew Kozanecki. "Diagnostics of the Mechatronic Rotating System." Key Engineering Materials 588 (October 2013): 101–8. http://dx.doi.org/10.4028/www.scientific.net/kem.588.101.

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Nowadays rotating systems are equipped with diagnostic systems that are based on collecting and recording measurement data and that process a huge amount of data during the machine operation. An analysis of these data and their interpretation, as well as finding a correlation between process parameters and dynamics of the machine is a very important problem. In the paper, a concept and selected procedures of the specialized software using advanced information technology for the diagnostic system dedicated for systems of rotating machines with active magnetic bearings will be presented. It is used in the actual operation of the machine, enabling an increase of its reliability. The paper presents some selected results of control of the proper operation of the mechatronic rotating system, carried out in the automatic mode.

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14

Mittal, Prakhar, Udit Parasher, Ruhi Khanna, Parv Yadav, Sunil Kumar, and Mariya Khurshid. "A Machine Learning-based Healthcare Diagnostic Model." International Journal of Computer Applications 184, no. 16 (June 18, 2022): 1–4. http://dx.doi.org/10.5120/ijca2022922147.

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Mittal, Prakhar, Udit Parasher, Ruhi Khanna, Parv Yadav, Sunil Kumar, and Mariya Khurshid. "A Machine Learning based Healthcare Diagnostic Model." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 3195–99. http://dx.doi.org/10.22214/ijraset.2022.42985.

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Abstract: This To live a healthy life, healthcare is considered very important but it is terribly difficult to get consultation from a doctor nowadays. The basic idea and motive of the project is to build a chatbot system by using AI (Artificial Intelligence) that can predict the health problems and give details of the disease before consulting the Doctor. The system provides text assistance to coordinate with the chatbot. Based on user’s symptoms chatbot will provide what kind of disease user have and provide doctor’s details according to the diagnose. Based on the symptom that user have chatbot will tell user the disease with all the other possible symptoms. The chat will inform whether the disease is major or minor. Hence, user can gain the maximum benefit of this AI based chatbot only after it diagnoses maximum kinds of illness and provide with all the required information(Abstract) Keywords: Online Diagnosis, Chatbot, Healthcare, Diseases (key words)

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16

Barr, Frances. "The right diagnostic ultrasound machine for you?" In Practice 14, no. 3 (May 1992): 142–44. http://dx.doi.org/10.1136/inpract.14.3.142.

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17

Sawaqed, Laith S., and Ayman M. Alrayes. "Bearing fault diagnostic using machine learning algorithms." Progress in Artificial Intelligence 9, no. 4 (October 1, 2020): 341–50. http://dx.doi.org/10.1007/s13748-020-00217-z.

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18

Szymański, Zygmunt. "Intelligent, Energy Saving Power Supply and Control System of Hoisting Mine Machine with Compact and Hybrid Drive System / Inteligentne, Energooszczędne Układy Zasilania I Sterowania Górniczych Maszyn Wyciągowych Z Napędem Zintegrowanym Lub Hybrydowym." Archives of Mining Sciences 60, no. 1 (March 1, 2015): 239–51. http://dx.doi.org/10.1515/amsc-2015-0016.

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Abstract In the paper present’s an analysis of suitableness an application of compact and hybrid drive system in hoisting machine. In the paper presented the review of constructional solutions of hoisting machines drive system, driving with AC and DC motor. In the paper presented conception of modern, energy sparing hoisting machine supply system, composed with compact motor, an supplied with transistor or thyristor converter supply system, and intelligent control system composed with multilevel microprocessor controller. In the paper present’s also analysis of suitableness application an selected method of artificial intelligent in hoisting machine control system, automation system, and modern diagnostic system. In the paper one limited to analysis of: fuzzy logic method, genetic algorithms method, and modern neural net II and III generation. That method enables realization of complex control algorithms of hosting machine with insurance of energy sparing exploitation conditions, monitoring of exploitation parameters, and prediction diagnostic of hoisting machine technical state, minimization a number of failure states. In the paper present’s a conception of control and diagnostic system of the hoisting machine based on fuzzy logic neural set control. In the chapter presented also a selected control algorithms and results of computer simulations realized for particular mathematical models of hoisting machine. Results of theoretical investigation were partly verified in laboratory and industrial experiments.

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19

Walid Ibrahim Alnusirat, Litvin Оleksandr, Ibrahim Farhan Alrefo, and Kravez Оleksandr. "TOOL FOR RESEARCHING THE DYNAMIC SYSTEM OF METAL-CUTTING MACHINE." World Science, no. 9(37) (September 30, 2018): 5–9. http://dx.doi.org/10.31435/rsglobal_ws/30092018/6128.

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Dynamic characteristics of the system flexibility of the machine affect the accuracy of machining, so the study of this problem is very important. Fluctuations of the machine elements significantly affect the error of the shape of the workpiece. The quality of the processing is determined not so much by the static displacements between the tool and the workpiece but the stability of the machine system as a whole. There are many solutions of vibroacoustic diagnostics devices for machines and machine-tools in the related publications. Defects in the spectrum of vibroacustic signals are found in the process of manufacturing and assembling machines in the form of discrete components, parameters of which are used in vibroacoustic diagnostics as informative diagnostic features. Along with that there is, but not so common, another type of dynamic system analysis of the machine, which can be carried out by experimental methods, or, in particular, by simulating the perturbation of a dynamic system by cutting forces of special type. Imitated disturbance is carried out by using a tool of a special form. During the processing cutting edges of the tool create a pseudorandom process with certain statistical characteristics, in particular, the correlation function. The proposed design of the tool makes it possible to perform the research of frequency parameters of the dynamic system of the machine without complex loading devices.

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20

Decner, Adam, Marcin Baranski, Tomasz Jarek, and Sebastian Berhausen. "Methods of Diagnosing the Insulation of Electric Machines Windings." Energies 15, no. 22 (November 12, 2022): 8465. http://dx.doi.org/10.3390/en15228465.

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The presented article concerns issues related to the diagnostics of the technical condition of the insulation of electrical machines. It discusses the importance of the operational supervision, maintenance and diagnostics of electrical machine insulation systems. The structure of the insulation system is presented and known solutions for making winding insulation are described. The negative impact of conditions and various exposures on the technical condition of the insulation system is described. Special attention is focused on the review of available diagnostic methods for insulating systems of electrical machines. These methods have been arranged in a systematic order according to the type of tests to be carried out.

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Císar, Miroslav, Ivan Kuric, and Vasile Adrian Ceclan. "Device for Training of Machine Tool Diagnostic Routines under Various Conditions." Applied Mechanics and Materials 808 (November 2015): 3–8. http://dx.doi.org/10.4028/www.scientific.net/amm.808.3.

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The article deals with diagnostics of machine tool precision and necessity to train basic routines of measurement and its preparation. Such training is essential for efficiency of diagnostic processes as preparation is usually the most time-consuming and skill-demanding part of overall measurement. The article roughly describes simulation of machine tool errors on proposed experimental device and its implementation into the training process in order to gain experiences with measurement on machine tools in wide scale of conditions. Described device is designed to simulate several geometrical errors, inaccuracies and environmental impacts on precision of positioning which affects not only machine precision but also effectivity of measurement itself.

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Zimovets, V. I., S. V. Shamatrin, D. E. Olada, and N. I. Kalashnykova. "Functional Diagnostic System for Multichannel Mine Lifting Machine Working in Factor Cluster Analysis Mode." Journal of Engineering Sciences 7, no. 1 (2020): E20—E27. http://dx.doi.org/10.21272/jes.2020.7(1).e4.

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The primary direction of the increase of reliability of the automated control systems of complex electromechanical machines is the application of intelligent information technologies of the analysis of diagnostic information directly in the operating mode. Therefore, the creation of the basics of information synthesis of a functional diagnosis system (FDS) based on machine learning and pattern recognition is a topical task. In this case, the synthesized FDS must be adaptive to arbitrary initial conditions of the technological process and practically invariant to the multidimensionality of the space of diagnostic features, an alphabet of recognition classes, which characterize the possible technical states of the units and devices of the machine. Besides, an essential feature of FDS is the ability to retrain by increasing the power of the alphabet recognition classes. In the article, information synthesis of FDS is performed within the framework of information-extreme intellectual data analysis technology, which is based on maximizing the information capacity of the system in the process of machine learning. The idea of factor cluster analysis was realized by forming an additional training matrix of unclassified vectors of features of a new recognition class obtained during the operation of the FDS directly in the operating mode. The proposed algorithm allows performing factor cluster analysis in the case of structured feature vectors of several recognition classes. In this case, additional training matrices of the corresponding recognition classes are formed by the agglomerative method of cluster analysis using the k-means procedure. The proposed method of factor cluster analysis is implemented on the example of information synthesis of the FDS of a multi-core mine lifting machine. Keywords: information-extreme intelligent technology, a system of functional diagnostics, multichannel mine lifting machine, machine learning, factor cluster analysis.

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23

Li, Yuan, and Linru Zhao. "Application of Machine Learning in Rheumatic Immune Diseases." Journal of Healthcare Engineering 2022 (January 25, 2022): 1–9. http://dx.doi.org/10.1155/2022/9273641.

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People are paying greater attention to their personal health as society develops and progresses, and rheumatic immunological disorders have become a serious concern that affects human health. As a result, research on a stable, trustworthy, and effective auxiliary diagnostic method for rheumatic immune disorders is critical. Machine learning overcomes the inefficiencies and volatility of human data processing, ushering in a revolution in artificial intelligence research. With the use of big data, machine learning-based application research on rheumatic immunological disorders has already demonstrated detection abilities that are on par with or better than those of medical professionals. Artificial intelligence systems are now being applied in the field of rheumatic immune disorders, with an emphasis on the identification of patient joint images. This article focuses on the use of machine learning algorithms in the diagnosis of rheumatic illnesses, as well as the practical implications of disease-assisted diagnosis systems and intelligent medical diagnosis. This article focuses on three common machine learning algorithms for research and debate: logistic regression, support vector machines, and adaptive boosting techniques. The three algorithms are used to build diagnostic models based on rheumatic illness data, and the performance of each model is assessed. According to a thorough analysis of the assessment data, the diagnostic model based on the limit gradient boosting method has the best resilience. This article presents machine learning's use and advancement in rheumatic immunological disorders, as well as new ideas for investigating more appropriate and efficient diagnostic and treatment techniques.

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Manjunath, Aditya G., Sabahudin Vrtagić, Fatih Dogan, Milan Dordevic, Mileta Zarkovic, Jasmin Kevric, and Goran Dobric. "Machine Learning MOSA Monitoring System." Instrumentation Mesure Métrologie 20, no. 4 (August 31, 2021): 203–8. http://dx.doi.org/10.18280/i2m.200404.

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This research paper deals with the problem of Metal-Oxide Surge Arrester (MOSA) condition monitoring and a new methodology in surge arrester monitoring and diagnostics is presented. A machine learning algorithm (back propagation regression) is used to estimate the non-linearity coefficient of the surge arrester, based on operating voltage and leakage current of the arrester. Using a simulated system, this research investigates the possibility of application and efficiency of machine learning. It is shown that the applied learning algorithm results are competitive with the model results parameters calculated as R2 = 0.999 and mean absolute real error computed as 0.005 which has shown that the proposed model can be used for MOSA monitoring and diagnostic purposes.

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25

Cempel, C. "Passive Diagnostics and Reliability Experiment: Application in Machine Condition Monitoring." Journal of Vibration and Acoustics 111, no. 1 (January 1, 1989): 82–87. http://dx.doi.org/10.1115/1.3269828.

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For many critical machines the only source of diagnostic knowledge is passive experiment during machine operation. If we treat the observed vibration symptoms for a group of machines as an outcome of the Weibull type stochastic process we can calculate the average symptom life-curve and the alarm and break-down ratio. These appear to be dependent on the Weibull shape factor (existing machine quality) and maintenance parameters used at a given plant. The results of passive experiment shown in the paper confirm fully the paper’s theoretical consideration.

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MENDAZ, Kheira, Houria BOUNOUA, and Baghdadi BENAZZA. "Diagnostic Fault of the Induction Machine with Application of the Neural Network." Acta Electrotechnica et Informatica 14, no. 1 (April 1, 2014): 58–66. http://dx.doi.org/10.15546/aeei-2014-0009.

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27

Dunayev, Anatoliy V., and Мikhail N. Kostomakhin. "About development of methods and means for management of reliability of agricultural machinery in GOSNITI." Tekhnicheskiy servis mashin, no. 1 (March 1, 2020): 196–206. http://dx.doi.org/10.22314/2618-8287-2020-58-1-196-206.

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The article presents the historical materials about the creation and growth of GOSNITI, its employees who developed and implemented methods and means of diagnosis, maintenance and repair of machinery and tractor fleet of the agro-industrial complex of the country. The information on the history of the mathematical apparatus to the method of reli-ability control machines to minimize the total unit costs of maintenance and repair, taking into account the costs of downtime of faulty machines are presented. (Research purpose) The approbation of the express method of substantiation for diagnostic and structural pa-rameters of machine elements and analysis of the results of the development, production and implementation of MTP diagnostic tools in the agricultural sector of the USSR. (Materials and methods) We have analyzed the achievements of the GOSNITI teams, other research institutes and universities of the country, the engineering service of agriculture, statistical data on the production and use of diagnostic tools for tractors and cars. (Results and dis-cussion) The article reviews universal approach by V. M. Mikhlin of feasibility study of permissible values of resource and diagnostic parameters. The article notes that the Finnish mathematicians implemented it in the universal computer program TURBO-NEK, which al-lows optimizing the permissible values of diagnostic and structural parameters of machine elements, other characteristics in the organization of technical operation of machines. We have tested the express method of calculation of permissible values of structural and diag-nostic parameters taking into account the real rates of change in the values of parameters in operation. We have described the engineering and calculation method of limiting and per-missible parameters of diesel engines, their parts and interfaces in the guidance technical material of 10.16.0001.008-89 as a result of thirty-five years of work of the repair laborato-ry of GOSNITI. We have considered the results of the creation and implementation of diag-nostic equipment, aspects of scientific, technical and organizational difficulties in its devel-opment, production, movement and use. (Conclusion) We have prepared proposals for the resuscitation of diagnostics of the machine and tractor fleet of the agro-industrial complex for the coming period. In addition to the parameters measured by built-in and external means of control, it was proposed to legalize the equal use of organoleptic control of the state of machines, for which a wealth of experience has been accumulated.

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Novikov, Pavel. "Adjustment of specialized technological equipment when machining small diameter internal threads with deforming cutting taps." MATEC Web of Conferences 224 (2018): 01135. http://dx.doi.org/10.1051/matecconf/201822401135.

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The article provides options for threading process diagnostics. For the previously proposed method of diagnostics, an algorithm for adjusting the machine, equipped with a pneumatic drive and a diagnostic module was developed. The interrelation between the technological system characteristics and the device for diagnostic signal recording was shown. As a result, it is possible to provide maximum productivity of the technological operation without the loss of processing quality.

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29

Boykov, Vladimir, and Aleksandr Povarecho. "Methodology of machine destruction testing." MATEC Web of Conferences 182 (2018): 01007. http://dx.doi.org/10.1051/matecconf/201818201007.

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This paper presents selected problems connected with automation of procedures involved in assessment of machine degradation degree using vibration method with special emphasis on the machine state prognosis. The current knowledge of these problems is not sufficient and needs further research on data processing, analysis of efficiency of diagnostic and prognostic procedures, collection and selection of diagnostic parameters and development of automatic procedures for recognition and prognosis of a machine state. New solutions and different aspects of diagnostic prognosis based on the proposed partial procedures focus on factors determining automation of procedures for identification of technical systems states. New automated procedures for acquisition and processing of symptoms indicating the machine state provide better possibilities of control and supervision of technical systems operation and maintenance through identification of their current states, and its good prognosis.

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30

Pennacchi, P., and A. Vania. "Diagnosis and Model Based Identification of a Coupling Misalignment." Shock and Vibration 12, no. 4 (2005): 293–308. http://dx.doi.org/10.1155/2005/607319.

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This paper is focused on the application of two different diagnostic techniques aimed to identify the most important faults in rotating machinery as well as on the simulation and prediction of the frequency response of rotating machines. The application of the two diagnostics techniques, the orbit shape analysis and the model based identification in the frequency domain, is described by means of an experimental case study that concerns a gas turbine-generator unit of a small power plant whose rotor-train was affected by an angular misalignment in a flexible coupling, caused by a wrong machine assembling. The fault type is identified by means of the orbit shape analysis, then the equivalent bending moments, which enable the shaft experimental vibrations to be simulated, have been identified using a model based identification method. These excitations have been used to predict the machine vibrations in a large rotating speed range inside which no monitoring data were available. To the best of the authors' knowledge, this is the first case of identification of coupling misalignment and prediction of the consequent machine behaviour in an actual size rotating machinery. The successful results obtained emphasise the usefulness of integrating common condition monitoring techniques with diagnostic strategies.

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Kudelina, Karolina, Toomas Vaimann, Bilal Asad, Anton Rassõlkin, Ants Kallaste, and Galina Demidova. "Trends and Challenges in Intelligent Condition Monitoring of Electrical Machines Using Machine Learning." Applied Sciences 11, no. 6 (March 19, 2021): 2761. http://dx.doi.org/10.3390/app11062761.

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A review of the fault diagnostic techniques based on machine is presented in this paper. As the world is moving towards industry 4.0 standards, the problems of limited computational power and available memory are decreasing day by day. A significant amount of data with a variety of faulty conditions of electrical machines working under different environments can be handled remotely using cloud computation. Moreover, the mathematical models of electrical machines can be utilized for the training of AI algorithms. This is true because the collection of big data is a challenging task for the industry and laboratory because of related limited resources. In this paper, some promising machine learning-based diagnostic techniques are presented in the perspective of their attributes.

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Peletiri, Iseimokumo Christopher. "Validation of automated malaria parasite diagnostic machines based on first principle: A pre-requisite for acceptable results and treatment monitoring in resource limited settings." Annals of Medical Laboratory Science 2, no. 1 (March 30, 2022): 35–41. http://dx.doi.org/10.51374/annalsmls.2022.2.1.0056.

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Background: Following the very recent introduction of automated malaria parasite diagnostic machines; the need to validate these high technology machines based on the first principle protocol in malaria parasite density determination for acceptable results and treatment monitoring cannot be over-emphasized. The aim of this review is to update Medical Laboratory Scientists, Medical Laboratory Technicians, and researchers alike on the first principle in the diagnosis of malaria using Giemsa stained thick and thin blood films and to build their capacity on how to validate any automated malaria parasite diagnostic machine. Methods: The first principle protocol in malaria parasite density determination was used. With 8 µL of blood spread within 18 mm diameter of circle (thick film), the volume of blood in one thick film field (0.002 µL) is obtained; which when multiplied by a factor (500) gives 1 µL. The number of parasites seen per 100 thick film fields or average number per each thick film field multiplied by 500 gives the number of parasites / µL of blood. Results: Malaria parasites counts of 5 – 50 parasites (1+), 50 – 500 parasites (2+), 500 – 5000 parasites (3+), and (4+) > 5000 parasites / µL of blood, and with the results obtained from the automated machine which when entered into a 2 x 2 table reveal the performance evaluation of automated machine. Conclusion: With several results obtained, any automated malaria diagnostic machine can be validated for its ability to detect disease (sensitivity, specificity, positive and negative predictive values). Commencement of the use of automated malaria parasites diagnostic machines in parasitology laboratory should not lead to discontinuity in the use of thick and thin blood films in malaria diagnosis as it remains the gold standard in resource limited settings.

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33

Giwa, A., and R. Giwa. "A 20-Gene Expression Diagnostic Signature of Bovine Respiratory Disease in Cattle." Journal of Scientific Research 14, no. 2 (May 6, 2022): 593–99. http://dx.doi.org/10.3329/jsr.v14i2.55193.

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Bovine Respiratory Disease (BRD) is a prevalent disease in cattle rearing systems globally with significant health and economic costs. Current diagnostic methods of BRD rely on subjective visual signs and physical examination, which are suboptimal. This study, therefore, aims to find a blood-based gene expression signature for the diagnostic identification of BRD in cattle. The Gene Expression Omnibus dataset, GSE152959, was downloaded and used for analysis. The analyses performed included differential gene expression (DGE), clustering and machine learning prediction. Ninety genes were differentially expressed in BRD samples compared to controls. The GSE150706 dataset was used as the test dataset for machine learning prediction. The DEGs identified clustered the GSE150706 samples with good accuracy. For the machine learning prediction, 92 % of correctly predicted samples were obtained using twenty genes as features. Therefore, the identified 20-gene expression signature has BRD diagnostic utility in cattle. This signature could potentially be used to develop standardized and reliable diagnostic tests of Bovine Respiratory Disease in cattle. Improved diagnostics will lead to early detection and treatment, reducing the health and economic costs associated with the disease. Further validation in larger cattle cohorts is required.

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de Vries, Reinier J., Stephanie E. J. Cronin, Padraic Romfh, Casie A. Pendexter, Rohil Jain, Benjamin T. Wilks, Siavash Raigani, et al. "Non-invasive quantification of the mitochondrial redox state in livers during machine perfusion." PLOS ONE 16, no. 10 (October 27, 2021): e0258833. http://dx.doi.org/10.1371/journal.pone.0258833.

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Ischemia reperfusion injury (IRI) is a critical problem in liver transplantation that can lead to life-threatening complications and substantially limit the utilization of livers for transplantation. However, because there are no early diagnostics available, fulminant injury may only become evident post-transplant. Mitochondria play a central role in IRI and are an ideal diagnostic target. During ischemia, changes in the mitochondrial redox state form the first link in the chain of events that lead to IRI. In this study we used resonance Raman spectroscopy to provide a rapid, non-invasive, and label-free diagnostic for quantification of the hepatic mitochondrial redox status. We show this diagnostic can be used to significantly distinguish transplantable versus non-transplantable ischemically injured rat livers during oxygenated machine perfusion and demonstrate spatial differences in the response of mitochondrial redox to ischemia reperfusion. This novel diagnostic may be used in the future to predict the viability of human livers for transplantation and as a tool to better understand the mechanisms of hepatic IRI.

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35

Fatima, Meherwar, and Maruf Pasha. "Survey of Machine Learning Algorithms for Disease Diagnostic." Journal of Intelligent Learning Systems and Applications 09, no. 01 (2017): 1–16. http://dx.doi.org/10.4236/jilsa.2017.91001.

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36

Marković, Svetislav. "Determination of machine parts technical state (diagnostic checking)." Vojnotehnicki glasnik 44, no. 6 (1996): 92–103. http://dx.doi.org/10.5937/vojtehg9601092m.

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37

Chessa, S., P. Maestrini, W. Errico, B. Sallay, F. Schifano, and R. Tripiccione. "Self-diagnostic tools of the APEmille parallel machine." IEE Proceedings - Computers and Digital Techniques 149, no. 6 (2002): 273. http://dx.doi.org/10.1049/ip-cdt:20020808.

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38

Tiernan, Timothy A., Earl R. Geddes, and Mark L. Mollon. "Integrated active vibration cancellation and machine diagnostic system." Journal of the Acoustical Society of America 97, no. 2 (February 1995): 1361. http://dx.doi.org/10.1121/1.412199.

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39

Cios, Krzysztof J., Ning Liu, and Lucy S. Goodenday. "Generation of Diagnostic Rules via Inductive Machine Learning." Kybernetes 22, no. 5 (May 1993): 44–56. http://dx.doi.org/10.1108/eb005985.

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40

Chow, T. W. S., and S. Hai. "Induction Machine Fault Diagnostic Analysis With Wavelet Technique." IEEE Transactions on Industrial Electronics 51, no. 3 (June 2004): 558–65. http://dx.doi.org/10.1109/tie.2004.825325.

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41

Jedrzejewski, J., W. Kwasny, D. Milejski, and M. Szafarczyk. "Selected Diagnostic Methods for Machine Tools Acceptance Tests." CIRP Annals 34, no. 1 (1985): 343–46. http://dx.doi.org/10.1016/s0007-8506(07)61787-9.

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42

Dister, Carl J. "Machine diagnostic system and method for vibration analysis." Journal of the Acoustical Society of America 111, no. 5 (2002): 1969. http://dx.doi.org/10.1121/1.1486367.

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43

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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44

Buryak, P., �. Parkhomchuk, and Ye Buryak. "IMPROVEMENT OF THE REPAIR SYSTEM OF AUTOMOTIVE VEHICLES OF THE NATIONAL GUARD OF UKRAINE." Collection of scientific works of the National Academyof the National Guard of Ukraine 1, no. 37 (2021): 52–59. http://dx.doi.org/10.33405/2409-7470/2021/1/37/237843.

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In the military units of the National Guard of Ukraine, a system for repairing automotive vehicles has been introduced, which provides for current, secondary and overhaul repairs. Maintenance of machines is carried out in order to eliminate malfunctions arising from the use of machines, and allows the replacement of individual units, including the main one. The average repair consists in the replacement or overhaul of not more than half of the main units. Overhaul restores the life of the machine and should be carried out at specialized enterprises of the Ministry of Defense of Ukraine or at other enterprises under contracts. Actually, in the military units of NGU, only current repairs are carried out. This is due to the high cost of overhaul and its low quality. Therefore, for the military units of the NGU, a diagnostic and recovery system for repairing automotive vehicles is proposed, including two types of repair: maintenance and restoration. The essence of maintenance repair consists in eliminating malfunctions, violations of adjustments during the operation of the machines according to the inspection, listening ? diagnostics with built-in and external diagnostic devices. Reconditioning repair consists in carrying out repair operations related to disassembling, troubleshooting, mechanical or other processing of component parts, assembling units, assemblies, testing them, and, if necessary, replacing them with new or repaired ones. Maintenance and repair repairs of automotive equipment are carried out only if there is a need for their implementation. It is proposed to establish ten parameters of the diagnostic and recovery system for the repair of automotive equipment at NGU and determine the life of the machine only before being discarding. The procedure for determining the number of machines that must be written off and get new ones to increase the non-reduced resource in each operation group is given.

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45

Liu, Zijian, Pinjia Zhang, Shan He, and Jin Huang. "A Review of Modeling and Diagnostic Techniques for Eccentricity Fault in Electric Machines." Energies 14, no. 14 (July 16, 2021): 4296. http://dx.doi.org/10.3390/en14144296.

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Research on the modeling and fault diagnosis of rotor eccentricities has been conducted during the past two decades. A variety of diagnostic theories and methods have been proposed based on different mechanisms, and there are reviews following either one type of electric machines or one type of eccentricity. Nonetheless, the research routes of modeling and diagnosis are common, regardless of machine or eccentricity types. This article tends to review all the possible modeling and diagnostic approaches for all common types of electric machines with eccentricities and provide suggestions on future research roadmap. The paper indicates that a reliable low-cost non-intrusive real-time online visualized diagnostic method is the trend. Observer-based diagnostic strategies are thought promising for the continued research.

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46

Żółtowski, Bogdan, and Mariusz Żółtowski. "Improvement the systems of exploitation machine/Doskonalenie strategii utrzymania zdatności maszyn." Journal of KONBiN 20, no. 1 (December 1, 2011): 119–32. http://dx.doi.org/10.2478/v10040-012-0041-5.

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Abstract It this work was talked over chosen and the new problems of exploitation of folded machine engines, helped with methods of technical diagnostics and monitoring the state. This concerns more and more often: the questions perfecting the effectiveness of methods of diagnosing, systems of preventive exchanges, new solutions of software on stage co-ordinating the decision (one - and the multi - dimension), as and the dedicated systems of diagnosing in engineering of diagnostics. Introduced problems of this study finds in processes of destruction of machine engines his reason, concurrent every machine engine near at hand after her producing, until to liquidation. The be shaping the costs of exploitation of machine engines and the possibilities of applying of well-known strategies of exploitation differentiate in this area the variety of organizational workings. The developing technical diagnostics gives to rational exploitation of machine engines the basis in newly created or the perfected diagnostic systems of exploitation.

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Burriel-Valencia, Jordi, Ruben Puche-Panadero, Javier Martinez-Roman, Angel Sapena-Bano, Manuel Pineda-Sanchez, Juan Perez-Cruz, and Martin Riera-Guasp. "Automatic Fault Diagnostic System for Induction Motors under Transient Regime Optimized with Expert Systems." Electronics 8, no. 1 (December 21, 2018): 6. http://dx.doi.org/10.3390/electronics8010006.

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Induction machines (IMs) power most modern industrial processes (induction motors) and generate an increasing portion of our electricity (doubly fed induction generators). A continuous monitoring of the machine’s condition can identify faults at an early stage, and it can avoid costly, unexpected shutdowns of production processes, with economic losses well beyond the cost of the machine itself. Machine current signature analysis (MCSA), has become a prominent technique for condition-based maintenance, because, in its basic approach, it is non-invasive, requires just a current sensor, and can process the current signal using a standard fast Fourier transform (FFT). Nevertheless, the industrial application of MCSA requires well-trained maintenance personnel, able to interpret the current spectra and to avoid false diagnostics that can appear due to electrical noise in harsh industrial environments. This task faces increasing difficulties, especially when dealing with machines that work under non-stationary conditions, such as wind generators under variable wind regime, or motors fed from variable speed drives. In these cases, the resulting spectra are no longer simple one-dimensional plots in the time domain; instead, they become two-dimensional images in the joint time-frequency domain, requiring highly specialized personnel to evaluate the machine condition. To alleviate these problems, supporting the maintenance staff in their decision process, and simplifying the correct use of fault diagnosis systems, expert systems based on neural networks have been proposed for automatic fault diagnosis. However, all these systems, up to the best knowledge of the authors, operate under steady-state conditions, and are not applicable in a transient regime. To solve this problem, this paper presents an automatic system for generating optimized expert diagnostic systems for fault detection when the machine works under transient conditions. The proposed method is first theoretically introduced, and then it is applied to the experimental diagnosis of broken bars in a commercial cage induction motor.

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48

Kandera, Matej, Miroslav Císar, and Ivan Zajačko. "Capabilities of processing and visualization of production facilities diagnostic data." MATEC Web of Conferences 244 (2018): 01020. http://dx.doi.org/10.1051/matecconf/201824401020.

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One of the main requirements of the safe and sustained workflow of production facilities and machines, is the proper choice and implementation of suitable technical diagnostic method. In the broader sense technical diagnostics includes approaches and methods of technical objects condition determination. This process is based on specific devices parameters inspecting, which values are directly or indirectly caused by certain changes of monitored production facilities condition. In order to reliably detect faults in right time, selected parameters indicating status of the production device should be monitored continuously. At a time when an Internet of Things platforms implemented to the industry business systems is the great temptation from the view of investors and company prestige are these principles common in the process of machine parameters online monitoring and its cross connections within the business realms. This paper describes specific software ThingWorx, which is a great example of sophisticated and universal IoT platform and also describes its application for production facilities diagnostic data collection and visualization.

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Grachev, V. V., A. V. Grishchenko, V. A. Kruchek, F. Yu Bazilevsky, M. A. Schwartz, and F. M. Schwartz. "Methodology for synthesis of neural network diagnostic models of complex technical objects." Automation on Transport 6, no. 4 (December 2020): 466–83. http://dx.doi.org/10.20295/2412-9186-2020-6-4-466-483.

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Despite the vast experience of using the neural networks for solving various machine learning problems, the numerous attempts to use them in technical diagnostics have not yet led to complete solutions so far (with rare exceptions). The reason is the specific nature of technical diagnostics that distinguishes such tasks from traditional machine learning problems. Having analyzed these specific features, the authors propose an approach to diagnosing complex technical objects that is focused on the use in built-in diagnostics systems and is based on the neural network reference diagnostic models of functionally isolated nodes and assemblies. The article describes the methodology for the synthesis of such models, their training on the data obtained by monitoring the object being tested using built-in diagnostic tools, determining the permissible response errors, and adapting to the current status of the object. The fuzzification of the diagnostic model results using the test sample proposed in the article makes it possible to standardize the approach to diagnosing complex technical objects designed for various purposes. The use of D. Trigg’s tracking control signal proposed by the authors to monitor regression residuals during the learning increases the training quality and generalization ability of models. The value of this signal determined by the model run on a test sample is an additional informative diagnostic parameter that increases the accuracy of classifying the status of the object under test. The proposed methodology applied at the complex technical object design stage allows optimizing the monitored parameters’ array and multiplying the efficiency of the diagnostic information recorded by the built-in diagnostic and monitoring tools.

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50

Golonka, Emil, and Michał Pająk. "Selected faults of low-speed machines, analysis of diagnostic signals." MATEC Web of Conferences 351 (2021): 01025. http://dx.doi.org/10.1051/matecconf/202135101025.

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In an industrial environment, a large part of the solutions used in machine parks is based on rotating systems. Therefore, the branches of diagnostics in this area are subjected to extensive research so that they effect with more and more new solutions. This article presents the problem of selected most common faults in a low-speed machinery environment. Presented chapters define the concept of symptoms and diagnostics and define its goals. Selected issues of the analysis of diagnostic signals were also discussed.

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