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Journal articles on the topic 'Rotating Machines Diagnostic'
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1
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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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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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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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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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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Khan, Muhammad Amir, Bilal Asad, Karolina Kudelina, Toomas Vaimann, and Ants Kallaste. "The Bearing Faults Detection Methods for Electrical Machines—The State of the Art." Energies 16, no. 1 (December 27, 2022): 296. http://dx.doi.org/10.3390/en16010296.
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Electrical machines are prone to faults and failures and demand incessant monitoring for their confined and reliable operations. A failure in electrical machines may cause unexpected interruptions and require a timely inspection of abnormal conditions in rotating electric machines. This article aims to summarize an up-to-date overview of all types of bearing faults diagnostic techniques by subdividing them into different categories. Different fault detection and diagnosis (FDD) techniques are discussed briefly for prognosis of numerous bearing faults that frequently occur in rotating machines. Conventional approaches, statistical approaches, and artificial intelligence-based architectures such as machine learning and deep learning are discussed summarily for the diagnosis of bearing faults that frequently arise in revolving electrical machines. The most advanced trends for diagnoses of frequent bearing faults based on intelligence and novel applications are reviewed. Future research directions that are helpful to enhance the performance of conventional, statistical, and artificial intelligence (machine learning, deep learning) and novel approaches are well addressed and provide hints for future work.
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Gizelska, Małgorzata, Dorota Kozanecka, and Zbigniew Kozanecki. "Monitoring and Diagnostics of the Rotating System with an Active Magnetic Bearing." Solid State Phenomena 198 (March 2013): 547–52. http://dx.doi.org/10.4028/www.scientific.net/ssp.198.547.
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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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Sule, Aliyu Hamza. "Rotating Electrical Machines: Types, Applications and Recent Advances." European Journal of Theoretical and Applied Sciences 1, no. 5 (September 1, 2023): 589–97. http://dx.doi.org/10.59324/ejtas.2023.1(5).47.
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The Rotating Electrical Machines (REMs) are classified into Motors and Generators. They powered the industrial, domestic and commercial loads. Because of their importance. This paper discussed different types of REMs, their applications and recent advances. REMs are applied in Teaching, Domestic, Mechatronics, Motorcycle, Three-wheelers, Electric Vehicle, Healthcare, Flywheel Energy Storage and Wind Energy Conversion Systems. It periscopes the advances of REMs in design, Fault diagnostic, control and condition monitoring. Its significance is to shed light on some advances made in REM.
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Kumar, Rahul R., Mauro Andriollo, Giansalvo Cirrincione, Maurizio Cirrincione, and Andrea Tortella. "A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors." Energies 15, no. 23 (November 25, 2022): 8938. http://dx.doi.org/10.3390/en15238938.
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This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of electrical drives in detail, especially the ones that are common in Industry 4.0. After giving an overview on fault statistics, standard methods for the FD and CM of rotating machines are first visited, and then its orientation towards intelligent approaches is discussed. Major diagnostic procedures are addressed in detail together with their advancements to date. In particular, the emphasis is given to motor current signature analysis (MCSA) and digital signal processing techniques (DSPTs) mostly used for feature engineering. Consequently, the statistical procedures and machine learning techniques (stemming from artificial intelligence—AI) are also visited to describe how FD is carried out in various systems. The effectiveness of the amalgamation of the model, signal, and data-based techniques for the FD and CM of inductions motors (IMs) is also highlighted in this review. It is worth mentioning that a variety of neural- and non-neural-based approaches are discussed concerning major faults in rotating machines. Finally, after a thorough survey of the diagnostic techniques based on specific faults for electrical drives, several open problems are identified and discussed. The paper concludes with important recommendations on where to divert the research focus considering the current advancements in the FD and CM of rotating machines.
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Bielawski, Piotr. "Marine Propulsion System Vibration Sensor Heads." New Trends in Production Engineering 1, no. 1 (October 1, 2018): 729–37. http://dx.doi.org/10.2478/ntpe-2018-0092.
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Abstract Vibration symptoms are the main symptoms used for diagnosing machines. This applies mainly to vibrations of non-rotating machines. Symptoms of rotating element vibrations are used in a limited scope, while mostly used are the symptoms of radial vibrations of rotating shafts. Across industries, the use of technical vibration diagnosis varies. Marine propulsion systems are poorly equipped with diagnostic equipment of that type. One of the main reasons is lack of appropriate sensors. The study presents two solutions of sensor heads. One solution applies to a sensor head built into the free end of the crankshaft of a reciprocating machine. The shaft free end sensor allows measurement of torsional and longitudinal vibration accelerations of the free end as a function of shaft rotation. The other solution refers to a sensor head built into sealed slide bearing of a straight shaft. The slide bearing head enables measurement of the eccentricity to the journal relative to the shell. Sensor heads under consideration are particularly suitable to be built in the ship’s propulsion system and integrated with the ship’s maintenance system. Sensors of the ship’s maintenance system equipped with these heads will allow the operator to draw conclusions concerning the wear margins of the propulsion engine and that of the tail shaft.
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Zhou, Qi, Xuyan Zhang, and Chaoqun Wu. "A Novel MSFED Feature for the Intelligent Fault Diagnosis of Rotating Machines." Machines 10, no. 9 (August 29, 2022): 743. http://dx.doi.org/10.3390/machines10090743.
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The vibration energy distribution pattern usually changes with the rotating machine’s health state and is a good indicator for intelligent fault diagnosis (IFD). The existing initial features such as RMS are less effective in revealing the vibration energy distribution pattern, and the frequency spectrum cannot provide a rich and hierarchical description of the vibration energy distribution pattern. Addressing this issue, we proposed a multi-scale frequency energy distribution (MSFED) feature for the IFD of rotating machines. The MSFED feature can reveal the vibration energy distribution patterns in the frequency domain in a multi-scale manner, and its one-dimensional vector and two-dimensional map formats make it usable for most IFD models. Experimental validation on the gearbox and bearing datasets verified that the MSFED feature achieved the highest diagnostic accuracy among commonly used initial features, in typical fault diagnosis scenarios except for the variable-load scenario. Furthermore, the separability and transferability of the MSFED feature were evaluated by distance-based metrics, and the results were in agreement with the features’ diagnostic performance. This work provides an important reference for the IFD of rotating machines, not only proposing a novel MSFED feature but also opening a new avenue for model-independent methods of the initial quality evaluation.
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Kindl, Vladimir, Miroslav Byrtus, Bohumil Skala, and Vaclav Kus. "Key Assembling Issues Relating to Mechanical Vibration of Fabricated Rotor of Large Induction Machines." Communications - Scientific letters of the University of Zilina 21, no. 2 (May 24, 2019): 58–68. http://dx.doi.org/10.26552/com.c.2019.2.58-68.
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In many applications, where rotating machines of high power are used, high demands on reliability and safety are laid. Precise manufacturing procedures has to be kept even in case of machines retrofits when e.g. rotors are newly assembled. Even small inaccuracies or misguiding the precise technological procedure can lead to improper running of the machine and it can result in shut-down of complete drive. In case of primary circuits of nuclear power plants, it further means the shut-down of the whole power plant. Consequently, it results in significant financial losses (foregone profit and cost given by problem fixing). The paper presents a methodology of vibration origin investigation in case of large rotating machines which are part of drives like pump, compressors etc. The methodology is based on the detection of undesirable vibration using a diagnostic system on-site and further it uses mathematical modelling of corresponding mechanical parts to reveal the vibration origin. The modelling along with the measurement showed that the detected dangerous vibration is caused by misguided assembling of the rotor of the machine.
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Ewert, Pawel, Czeslaw T. Kowalski, and Michal Jaworski. "Comparison of the Effectiveness of Selected Vibration Signal Analysis Methods in the Rotor Unbalance Detection of PMSM Drive System." Electronics 11, no. 11 (May 31, 2022): 1748. http://dx.doi.org/10.3390/electronics11111748.
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Mechanical unbalance is a phenomenon that concerns rotating elements, including rotors in electrical machines. An unbalanced rotor generates vibration, which is transferred to the machine body. The vibration contributes to reducing drive system reliability and, as a consequence, leads to frequent downtime. Therefore, from an economic point of view, monitoring the unbalance of rotating elements is justified. In this paper, the rotor unbalance of a drive system with a permanent magnet synchronous motor (PMSM) was physically modelled using a specially developed shield, with five test masses fixed at the motor shaft. The analysed diagnostic signal was mechanical vibration. Unbalance was detected using selected signal analysis methods, such as frequency-domain methods (classical spectrum analysis FFT and a higher-order bispectrum method) and two methods applied in technical diagnostics (order analysis and orbit method). The efficiency of unbalance symptom detection using these four methods was compared for the frequency controlled PMSM. The properties of the analysed diagnostic methods were assessed and compared in terms of their usefulness in rotor unbalance diagnosis, and the basic features characterizing the usefulness of these methods were determined depending on the operating conditions of the drive. This work could have a significant impact on the process of designing diagnostic systems for PMSM drives.
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Marçal, Rui Francisco Martins, Kazuo Hatakeyama, and Dani Juliano Czelusniak. "Expert System Based on Fuzzy Rules for Monitoring and Diagnosis of Operation Conditions in Rotating Machines." Advanced Materials Research 1061-1062 (December 2014): 950–60. http://dx.doi.org/10.4028/www.scientific.net/amr.1061-1062.950.
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This work provides a detection method for failure in rotating machines based on a change of vibration pattern and offers the diagnosis about the operation conditions using Fuzzy Logic. A mechanic structure (as an experimental prototype where faults can be inserted) called Rotating System has been used. The vibration standard of the Rotating System, called "The Spectral Signature", has been obtained. The changes in the vibration standard have been analyzed and used as parameters for detecting incipient failures, as well as their condition evolution, allowing predictive monitoring and planning of maintenance. The faults analyzed in this work are caused due to insertion of asymmetric masses for unbalancing in the axle wheel. The system for diagnosing Fuzzy System was calibrated to detect and diagnose the conditions: normal, incipient failure, maintenance, and danger, using linguistic variables. The frequency of rotation and the amplitudes of vibration of the axle wheel are considered in each situation as parameters for analysis, diagnostic, for the decision by the Expert System based on Fuzzy rules. The results confirm that the proposed method is useful for detecting incipient failures, monitoring the evolution of severity and offering grants for planning and decision making about maintenance or prevention of rotating machines.
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Mortazavizadeh, S. "A Review on Condition Monitoring and Diagnostic Techniques of Rotating Electrical Machines." Physical Science International Journal 4, no. 3 (January 10, 2014): 310–38. http://dx.doi.org/10.9734/psij/2014/4837.
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Novaković, Borivoj, Mića Đurđev, Luka Đorđević, and Tamara Šajnović. "The application of modern methods of vibration diagnostics in detecting potential faults in rotating equipment." Tehnika 78, no. 5 (2023): 559–63. http://dx.doi.org/10.5937/tehnika2305559n.
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Industrial systems require absolute productivity in order to achieve maximum profits and meet economic objectives. To accomplish this, systems need to be maintained and diagnosed in an adequate, precise, and reliable manner. Modern diagnostic techniques, such as vibration diagnostics, enable timely action to prevent potential major failures and unplanned downtime, which are the most undesirable costs within a production system. The implementation of vibration diagnostic controls provides a precise and clear picture of the condition of rotating equipment and machinery, such as pumps, compressors, and turbines. By analyzing signals collected from measurement points, the condition of the machines can be determined, and a maintenance plan can be established for the upcoming period.
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Wong, Pak Kin, Jian-Hua Zhong, Zhi-Xin Yang, and Chi Man Vong. "A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 6 (November 14, 2016): 1146–61. http://dx.doi.org/10.1177/0954406216632022.
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This paper proposes a new diagnostic framework, namely, probabilistic committee machine, to diagnose simultaneous-fault in the rotating machinery. The new framework combines a feature extraction method with ensemble empirical mode decomposition and singular value decomposition, multiple pairwise-coupled sparse Bayesian extreme learning machines (PCSBELM), and a parameter optimization algorithm to create an intelligent diagnostic framework. The feature extraction method is employed to find the features of single faults in a simultaneous-fault pattern. Multiple PCSBELM networks are built as different signal committee members, and each member is trained using vibration or sound signals respectively. The individual diagnostic result from each fault detection member is then combined by a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable fault as compared to individual classifier acting alone. The effectiveness of the proposed framework is verified by a case study on a gearbox fault detection. Experimental results show the proposed framework is superior to the existing single probabilistic classifier. Moreover, the proposed system can diagnose both single- and simultaneous-faults for the rotating machinery while the framework is trained by single-fault patterns only.
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Rokicki, Edward, Paweł Lindstedt, Jerzy Manerowski, and Jarosław Spychała. "The Concept of Monitoring Blades of Rotor Machines with the Identification of their Vibration Frequency." Journal of KONBiN 44, no. 1 (December 1, 2017): 389–412. http://dx.doi.org/10.1515/jok-2017-0080.
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Abstract The paper presents the basis of a new method for monitoring the technical condition of rotating blades during their operation. Utilizing the measurement of blade tip instantaneous speed under subsequent sensors, enables direct determination of the blade vibration frequency. The method utilizes a diagnostic model in the form of amplitude amplification W2ij and phase shift φij of a diagnostic signal y(t) resulting from the operation of the blade and the signal from its environment, when the blade tip passes under a cascade of blade tip instantaneous speed sensors. The adopted diagnostic model, indirectly takes into account the current surrounding of a blade without the need to measure it [12, 14]. Evaluation of the blade technical condition in real time and static analysis shall be performed on the basis of the vibration process parameter analysis. The suggested method may play an important role in the diagnostics of rotor machine blades during their operation.
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Michalak, Anna, and Jacek Wodecki. "Parametric simulator of cyclic and non-cyclic impulsive vibration signals for diagnostic research applications." IOP Conference Series: Earth and Environmental Science 942, no. 1 (November 1, 2021): 012015. http://dx.doi.org/10.1088/1755-1315/942/1/012015.
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Abstract In recent years cyclostationary analysis of vibration signals is considered to be one of the most potent approaches for diagnostics of machines with rotating components. However, it is a subject of an extensive research towards extending its robustness due to its significant inefficiency in the presence of non-cyclic impulsive components in measured data. This problem is especially visible in datasets measured on machines such as ore crushers, where the high-energy impacts are a natural phenomenon. Unfortunately, due to practical inaccessibility, real-life datasets necessary to properly study this problem are extremely difficult to obtain. To address this issue, the authors propose an easy to use simulator of impulsive components. It covers both cyclic components that can describe various types of fault signatures, and non-cyclic ones that can represent impacts occurring naturally due to the nature of machine operation. Simulated signals have been compared with real ones to ensure a high similarity degree, which in turn guarantees a relatively high level of realism.
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Khalil, Ardalan F., and Sarkawt Rostam. "Machine Learning-based Predictive Maintenance for Fault Detection in Rotating Machinery: A Case Study." Engineering, Technology & Applied Science Research 14, no. 2 (April 2, 2024): 13181–89. http://dx.doi.org/10.48084/etasr.6813.
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In the realm of industrial production, condition monitoring plays a pivotal role in ensuring the reliability and longevity of rotating machinery. Since most of the production facilities rely heavily on vibration analysis, it has become the cornerstone of condition monitoring practices. However, manual analysis of vibration signals is a time-consuming and expertise-intensive task, often requiring specialized domain knowledge. The current research addresses the aforementioned challenges by proposing a novel semi-automated diagnostics system. The approach leverages historical vibration data in the form of Fast Fourier Transform (FFT) spectrums. The system extracts energy features from the frequency domain by dividing the frequency range into a predefined number of bins and summing the energy values within each bin. Subsequently, each datapoint is labeled based on the corresponding machine condition, enabling the system to learn diagnostic patterns by employing machine learning models. This approach facilitates efficient and accurate diagnostics with minimal manual intervention. The resulting dataset effectively represents and provides an interpretable result. Support Vector Machines (SVM), and ensemble algorithms are utilized to diagnose the faults instantaneously and with minimal error rates. The proposed system is capable of providing early warnings and thus prevents further deterioration and unplanned downtimes. Experimental validation using real-world data demonstrates the system's efficacy, achieving an accuracy of over 90%.
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Quiles-Cucarella, Eduardo, Alejandro García-Bádenas, Ignacio Agustí-Mercader, and Guillermo Escrivá-Escrivá. "Optimizing Bearing Fault Diagnosis in Rotating Electrical Machines Using Deep Learning and Frequency Domain Features." Applied Sciences 15, no. 6 (March 13, 2025): 3132. https://doi.org/10.3390/app15063132.
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This study uses deep learning techniques to optimize fault diagnosis in rolling element bearings of rotating electrical machines. Leveraging the Case Western Reserve University bearing fault database, the methodology involves transforming one-dimensional vibration signals into two-dimensional scalograms, which are used to train neural networks via transfer learning. By employing SqueezeNet—a pre-trained convolutional neural network—and optimizing hyperparameters, this study significantly reduces the computational resources and time needed for effective fault classification. The analysis evaluates the effectiveness of two wavelet transforms (amor and morse) for feature extraction in correlation with varying learning rates. Results indicate that precise hyperparameter tuning enhances diagnostic accuracy, achieving a classification accuracy of 99.37% using the amor wavelet. Scalograms proved particularly effective in identifying distinct vibration patterns for faults in bearings’ inner and outer races. This research underscores the critical role of advanced signal processing and machine learning in predictive maintenance. The proposed methodology ensures higher reliability and operational efficiency and demonstrates the feasibility of transfer learning in industrial diagnostic applications, particularly for optimizing resource-constrained systems. These findings improve the robustness and precision of machine fault diagnosis systems.
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BEN RAHMOUNE, Mohamed, Abdelhamid IRATNI, Ahmed HAFAIFA, and Ilhami COLAK. "Gas Turbine Vibration Detection and Identification based on Dynamic Artificial Neural Networks." Electrotehnica, Electronica, Automatica 71, no. 2 (May 15, 2023): 19–27. http://dx.doi.org/10.46904/eea.23.71.2.1108003.
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Vibration control in rotating machinery is a major challenge in oil and gas facilities that use these machines. In gas turbines, the instability phenomenon is generated by the rotor, and measurements must be made in the axial plane of the turbine. Minor defects can lead to significant vibration amplifications, making it imperative to detect these defects early. The goal of this study is to develop a diagnostic strategy to monitor faults affecting a turbine system using a supervision approach based on artificial neural networks. This strategy allows for early detection of faults, which allows for efficient management of vibration-induced failures, as well as economic gain, by recovering the transported gas used in these machines. By describing the vibration-related parameters and representing the state of the vibratory motion, the proposed approach provides a powerful tool for vibration control in rotating machines.
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Niyongabo, Julius, Yingjie Zhang, and Jérémie Ndikumagenge. "Bearing Fault Detection and Diagnosis Based on Densely Connected Convolutional Networks." Acta Mechanica et Automatica 16, no. 2 (March 24, 2022): 130–35. http://dx.doi.org/10.2478/ama-2022-0017.
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Abstract Rotating machines are widely used in today’s world. As these machines perform the biggest tasks in industries, faults are naturally observed on their components. For most rotating machines such as wind turbine, bearing is one of critical components. To reduce failure rate and increase working life of rotating machinery it is important to detect and diagnose early faults in this most vulnerable part. In the recent past, technologies based on computational intelligence, including machine learning (ML) and deep learning (DL), have been efficiently used for detection and diagnosis of bearing faults. However, DL algorithms are being increasingly favoured day by day because of their advantages of automatically extracting features from training data. Despite this, in DL, adding neural layers reduces the training accuracy and the vanishing gradient problem arises. DL algorithms based on convolutional neural networks (CNN) such as DenseNet have proved to be quite efficient in solving this kind of problem. In this paper, a transfer learning consisting of fine-tuning DenseNet-121 top layers is proposed to make this classifier more robust and efficient. Then, a new intelligent model inspired by DenseNet-121 is designed and used for detecting and diagnosing bearing faults. Continuous wavelet transform is applied to enhance the dataset. Experimental results obtained from analyses employing the Case Western Reserve University (CWRU) bearing dataset show that the proposed model has higher diagnostic performance, with 98% average accuracy and less complexity.
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Ismagilov, Flyur, Irek Khayrullin, Vyacheslav Vavilov, and Valentina Ayguzina. "An Electromagnetic Moment in Short Circuits in Electrical Rotating Machines with High-Coercivity Permanent Magnets." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 2 (August 1, 2017): 483. http://dx.doi.org/10.11591/ijeecs.v7.i2.pp483-491.
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<p>This paper presents a computer model of an electrical rotating machine with high-coercivity permanent magnets and research of various short-circuit types in the electrical rotating machine with high-coercivity permanent magnets, including turn-to-turn short circuit. Diagnostic criteria of short circuits are revealed. There are the electromagnetic moment and the magnetic flux density in the stator core back. With comparison the experiment and computer modeling results, it was found that the created computer model is highly accurate and completely repeats the experiment results. The numerical discrepancy between the experimental data and the simulation data is below 5%. The obtained results can be used in practice in the design of the electrical rotating machine with high-coercivity permanent magnets.</p>
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Al-Ameri, Salem Mgammal, Ahmed Allawy Alawady, Mohd Fairouz Mohd Yousof, Muhammad Saufi Kamarudin, Ali Ahmed Salem, Ahmed Abu-Siada, and Mohamed I. Mosaad. "Application of Frequency Response Analysis Method to Detect Short-Circuit Faults in Three-Phase Induction Motors." Applied Sciences 12, no. 4 (February 16, 2022): 2046. http://dx.doi.org/10.3390/app12042046.
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The industry has widely accepted Frequency Response Analysis (FRA) as a reliable method to detect power transformers mechanical deformations. While the FRA technique has been recommended in recent literature as a potential diagnostic method to detect internal faults within rotating machines, detailed feasibility studies have not been fully addressed yet. This paper investigates the feasibility of using the FRA technique to detect several short circuit faults in the stator winding of three-phase induction motors (TPIMs). In this regard, FRA testing is conducted on two sets of induction motors with various short circuit faults. Investigated faults include short circuits between two phases, short circuit turns within the same phase, phase-to-ground, and phase-to-neutral short circuit. The measured FRA signatures are divided into three frequency ranges: low, medium, and high. Several statistical indicators are employed to quantify the variation between faulty and healthy signatures in each frequency range. Experimental results attest the feasibility of the FRA technique as a diagnostic tool to detect internal faults in rotating machines, such as induction motors.
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Pennacchi, P., and A. Vania. "Identification of a Generator Fault by Model-Based Diagnostic Techniques." International Journal of Rotating Machinery 10, no. 4 (2004): 293–300. http://dx.doi.org/10.1155/s1023621x04000302.
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Model-based diagnostic techniques can be used successfully in the health analysis of rotormachinery. Unfortunately, a poor accuracy of the model of the fully assembled machine, as well as noise in the signals and errors in the evaluation of the experimental vibrations that are caused only by the impending fault, can affect the accuracy of the fault identifications. This can make it difficult to identify the type of actual fault as well as to evaluate with care its severity and position. This article shows some techniques that have been developed by the authors to measure the accuracy of the results obtained with model-based identification methods aimed to diagnose faults in rotating machines. In this article, the results obtained by means of the analysis of experimental data collected in a power plant are described. Finally, the capabilities of the developed methods are shown and discussed.
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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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Ainapure, Abhijeet, Shahin Siahpour, Xiang Li, Faray Majid, and Jay Lee. "Intelligent Robust Cross-Domain Fault Diagnostic Method for Rotating Machines Using Noisy Condition Labels." Mathematics 10, no. 3 (January 30, 2022): 455. http://dx.doi.org/10.3390/math10030455.
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Cross-domain fault diagnosis methods have been successfully and widely developed in the past years, which focus on practical industrial scenarios with training and testing data from numerous machinery working regimes. Due to the remarkable effectiveness in such problems, deep learning-based domain adaptation approaches have been attracting increasing attention. However, the existing methods in the literature are generally lower compared to environmental noise and data availability, and it is difficult to achieve promising performance under harsh practical conditions. This paper proposes a new cross-domain fault diagnosis method with enhanced robustness. Noisy labels are introduced to significantly increase the generalization ability of the data-driven model. Promising diagnosis performance can be obtained with strong noise interference in testing, as well as in practical cases with low-quality data. Experiments on two rotating machinery datasets are carried out for validation. The results indicate that the proposed algorithm is well suited to be applied in real industrial environments to achieve promising performance with variations of working conditions.
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Al-Obaidi, Salah M. Ali, M. Salman Leong, R. I. Raja Hamzah, and Ahmed M. Abdelrhman. "A Review of Acoustic Emission Technique for Machinery Condition Monitoring: Defects Detection & Diagnostic." Applied Mechanics and Materials 229-231 (November 2012): 1476–80. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.1476.
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Acoustic emission (AE) measurements are one of many non-destructive testing methods which had found applications in defects detection in machines. This paper reviews the state of the art in AE based condition monitoring with particular emphasis on rotating and reciprocating machinery applications. Advantages and limitations of the AE technique in comparison to other condition monitoring techniques in detecting common machinery faults are also discussed.
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Jang, Gye-Bong, and Sung-Bae Cho. "Feature Space Transformation for Fault Diagnosis of Rotating Machinery under Different Working Conditions." Sensors 21, no. 4 (February 18, 2021): 1417. http://dx.doi.org/10.3390/s21041417.
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In recent years, various deep learning models have been developed for the fault diagnosis of rotating machines. However, in practical applications related to fault diagnosis, it is difficult to immediately implement a trained model because the distribution of source data and target domain data have different distributions. Additionally, collecting failure data for various operating conditions is time consuming and expensive. In this paper, we introduce a new transformation method for the latent space between domains using the source domain and normal data of the target domain that can be easily collected. Inspired by semantic transformations in an embedded space in the field of word embedding, discrepancies between the distribution of the source and target domains are minimized by transforming the latent representation space in which fault attributes are preserved. To match the feature area and distribution, spatial attention is applied to learn the latent feature spaces, and the 1D CNN LSTM architecture is implemented to maximize the intra-class classification. The proposed model was validated for two types of rotating machines such as a dataset of rolling bearings as CWRU and a gearbox dataset of heavy machinery. Experimental results show the proposed method has higher cross-domain diagnostic accuracy than others, therefore showing reliable generalization performance in rotating machines operating under various conditions.
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Sundukov, A. Ye, and Ye V. Shakhmatov. "Series of diagnostic indicators of gearbox teeth wear in aircraft gas turbine engines." VESTNIK of Samara University. Aerospace and Mechanical Engineering 21, no. 4 (January 18, 2023): 109–17. http://dx.doi.org/10.18287/2541-7533-2022-21-4-109-117.
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Aircraft gas turbine engine gearboxes are intended for providing optimum rotational speeds for propellers and fans. Wear of the tooth flanks is their key and most dangerous defect. The defect generates vibrations leading to fatigue failure of engine components. Vibration-based diagnostics is the most effective tool of non-destructive testing of the technical condition of rotating machines. This review of the known diagnostic indicators of the defect in question shows the need for its significant expansion. Previously performed researches made it possible to suggest a series of diagnostic indicators of tooth wear for the sun gear satellites couple in the differential reducer of a gas turbine engine. It is shown that the mathematical models of the dependence of the levels of diagnostic indicators on the wear value have both linear and power form. It was found that diagnostic indicators described by power dependences are the closest ones to the model of wear development. It is noted that when selecting diagnostic indicators for operating conditions, the optimal ones should be recognized as those based on the parameters of the current frequency.
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Puchalski, Andrzej, and Iwona Komorska. "Data-driven monitoring of the gearbox using multifractal analysis and machine learning methods." MATEC Web of Conferences 252 (2019): 06006. http://dx.doi.org/10.1051/matecconf/201925206006.
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Data-driven diagnostic methods allow to obtain a statistical model of time series and to identify deviations of recorded data from the pattern of the monitored system. Statistical analysis of time series of mechanical vibrations creates a new quality in the monitoring of rotating machines. Most real vibration signals exhibit nonlinear properties well described by scaling exponents. Multifractal analysis, which relies mainly on assessing local singularity exponents, has become a popular tool for statistical analysis of empirical data. There are many methods to study time series in terms of their fractality. Comparing computational complexity, a wavelet leaders algorithm was chosen. Using Wavelet Leaders Multifractal Formalism, multifractal parameters were estimated, taking them as diagnostic features in the pattern recognition procedure, using machine learning methods. The classification was performed using neural network, k-nearest neighbours’ algorithm and support vector machine. The article presents the results of vibration acceleration tests in a demonstration transmission system that allows simulations of assembly errors and teeth wear.
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Rutuja Mane and Abhinandan Admuthe. "Design and Development of Experimental Test Rig for Fault Diagnosis of Ball Bearing Using Fuzzy Logic Concept." International Journal of Engineering and Management Research 13, no. 4 (August 31, 2023): 158–63. http://dx.doi.org/10.31033/ijemr.13.4.20.
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Rolling element bearings are frequently employed in industry. Many machine-related issues are linked to bearing failures. To minimize downtime and preserve product quality in a highly automated production, an online detection system is required. Condition-based monitoring for deep groove ball bearings is becoming more common. All rotating machinery uses these bearings extensively to accommodate both static and dynamic loads. Techniques for condition-based monitoring can be utilized to diagnose bearing defects to prevent this failure. So, it is important to study these faults present in the machines. The techniques for fault detection will be described in this paper. We are discussing Fast Fourier Transform (FFT) technique. In FFT, we obtain frequency-relationship graphs, and based on peak frequencies, we predict faults. A detailed analysis using the FFT Methodology is done to find out the possible faults, and finally validate with MATLAB software. For the aim of bearing diagnostics, the system performs vibration analysis. More advanced diagnostic systems use fuzzy logic and classification methods to identify the state of the machinery. These techniques enable the creation of more automatic and trustworthy diagnostic systems.
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Pawlik, Paweł, Konrad Kania, and Bartosz Przysucha. "The Use of Deep Learning Methods in Diagnosing Rotating Machines Operating in Variable Conditions." Energies 14, no. 14 (July 13, 2021): 4231. http://dx.doi.org/10.3390/en14144231.
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This paper presents the use of artificial neural networks in diagnosing the technical condition of drive systems operating under variable conditions. The effects of temperature and load variations on the values of diagnostic parameters were considered. An experiment was conducted on a testing rig where a variable load was introduced corresponding to the load of the main gearbox of the bucket wheel excavator. The signals of vibration acceleration on the gearbox body, rotational speed, and current consumption of the drive motor for different values of oil temperature were measured. Synchronous analysis was performed, and the values of order amplitudes and the corresponding values of current, speed, and temperature were determined. Such datasets were the learning vectors for a set of artificial deep learning neural networks. A new approach proposed in this paper is to train the network using a learning set consisting only of data from the efficient system. The responses of the trained neural networks to new data from the undamaged system were performed against the response to data recorded for three damage states: misalignment, unbalance, and simultaneous misalignment and unbalance. As a result, a diagnostic parameter as a normalized measure of the deviation of the network results was developed for the faulted system from the result for the undamaged condition.
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de Sá Só Martins, Dionísio Henrique Carvalho, Denys Pestana Viana, Amaro Azevedo de Lima, Milena Faria Pinto, Luís Tarrataca, Fabrício Lopes e Silva, Ricardo Homero Ramírez Gutiérrez, Thiago de Moura Prego, Ulisses Admar Barbosa Vicente Monteiro, and Diego Barreto Haddad. "Diagnostic and severity analysis of combined failures composed by imbalance and misalignment in rotating machines." International Journal of Advanced Manufacturing Technology 114, no. 9-10 (April 21, 2021): 3077–92. http://dx.doi.org/10.1007/s00170-021-06873-2.
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Habyarimana, Mathew, and Abayomi A. Adebiyi. "A Review of Artificial Intelligence Applications in Predicting Faults in Electrical Machines." Energies 18, no. 7 (March 24, 2025): 1616. https://doi.org/10.3390/en18071616.
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The operational efficiency of many industrial processes is greatly affected by condition monitoring, which has become more and more important in the detection and forecast of electrical machine failures. Early identification of possible problems and prompt and precise diagnosis reduce unscheduled downtime, lower maintenance costs, and prevent catastrophic failures. Traditional human-dependent diagnostic techniques are changing as a result of advances in artificial intelligence (AI), opening the door to automated and predictive maintenance plans. This paper provides a detailed examination of artificial intelligence (AI) applications in the prediction of electrical device failures, with a focus on techniques such as fuzzy systems, expert systems, artificial neural networks (ANNs), and complex machine-learning algorithms. These methods use both historical and present data to identify and predict problems and allow timely actions. The study looks at implementation challenges for AI-based diagnostic systems, including data dependencies, processing demands, and model interpretability, in addition to highlighting recent advances such as digital twins, explainable AI, and IoT integration. This review highlights the revolutionary potential of artificial intelligence (AI) in improving the sustainability, efficiency, and dependability of electrical machine systems, especially in the context of rotating machines, by addressing existing constraints and suggesting future research routes.
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Burdzik, Rafał, Łukasz Konieczny, and Piotr Folęga. "Structural Health Monitoring of Rotating Machines in Manufacturing Processes by Vibration Methods." Advanced Materials Research 1036 (October 2014): 642–47. http://dx.doi.org/10.4028/www.scientific.net/amr.1036.642.
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The paper presents results of the active diagnostics experiments on influence of fatigue metal damage of the inner race of bearing and unbalance of rotating masses on vibration generated by the machine. Analysis of vibration related phenomena is a solution commonly applied in Structural Health Monitoring (SHM) systems. The application of vibroacoustics methods for technical condition monitoring has been developed in the past years in many systems of manufacturing processes. Vibroacoustic methods, based on the analysis of vibration or acoustic signals perceived as residual processes of non-invasive nature, is becoming more and more important in this respect. The scope of its application as well as the applicability of methods in numerous diagnostic systems also results from the capabilities of advanced methods of signal analysis and identification of numerous characteristics of technical condition. One of the most common operation damages are caused by rolling bearings wear. The scope of research contains tests on bearing damage and the unbalance of disc. The wear processes and unbalance are closely related to the vibration levels (arising from the mass loss of plastic deformation, and the fatigue damage). The research was conducted on special research test bench for vibration monitoring for rotating machinery. Structural health monitoring of machinery has to be conducted in different states and working conditions of the manufacturing system. Thus for simulating of different operating conditions the experiments have been conducted during run up of the machine which consist the transient states of working and during work on constant rotational speed of the power generate engine. For the identification of the symptoms of the machinery and equipments health monitoring the vibration signal have been analysed in time domain and frequency transformation as well. The performed signals are not stationary. Thus it is better to observe the signal simultaneously in time and frequency domains. For this purpose the spectrograms were determined. Spectrograms computes the short-time Fourier transform of a signal by default divided into segments. During the transformation the Hamming window and noverlap were used. For the comparison of the vibration of good and damage bearings signals registered for different frequencies have been presented in form of spectrograms and RMS distributions.
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Pająk, Michał, Dragutin Lisjak, and Davor Kolar. "Identification of Inability States of Rotating Subsystems of Vehicles and Machines." Journal of KONES 26, no. 1 (March 1, 2019): 111–18. http://dx.doi.org/10.2478/kones-2019-0014.
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Abstract One of the most important subsystems of the vehicles and machines operating currently in industry and transportation are the rotating subsystems. During the subsystems operation, due to the forcing factors influence, the technical state of them is changing and the failure can occur. In order to avoid such a situation the technical state should be identified online. To do this the analysis of the subsystems vibrations is performed. The identified technical state should be considered in a context of the ability and different inability states. Therefore, the first step of the diagnostic procedure is the ability and different inability states identification. In the article, it is proposed to accomplish this goal by the vibrations analysis in time domain. The described research started with the vibration signals acquisition using the experimental stand. In this way, the vibration signals for ability and different inability states were obtained. Afterwards, the signals were divided into learning and testing data sets. For each signal from learning data set, several characteristics were calculated, and they selected the most significant among them. Using the selected characteristics, the signals from the testing data set were analysed. Thanks to it, the testing vibrations signals were counted among the signals collected on the rotating subsystem operating in ability or selected inability state. The result of the performed studies and the accuracy of the technical state of the tested system identification can be found at the end of the article.
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Niculescu, Dan Florin, Adrian Ghionea, and Adrian Olaru. "Diagnosis and Predictive Maintenance of Machinery and Equipment, by Measuring Vibration." Applied Mechanics and Materials 325-326 (June 2013): 186–91. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.186.
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The paper presents results of analysis of the dynamic behavior of rotating mechanisms and couplings of the main structure of a kinematic chain sharpening machine precision Cugir normal UAS 200. The ultimate goal is to establish diagnostic and maintenance program the car UAS 200. Diagnosis by measurement of vibration and noise, allow a company to monitor faults and machines and equipment, through a system of preventive maintenance, predictive. Diagnosis automatic machinery and equipment is made in order to ensure a higher reliability of these and how to obtain a more extended life cycle without the occurrence of defects. The application of preventive and predictive maintenance management supports enterprise, because it proves effective, the information you provide in making decisions.
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Hui, Qiuli. "Application of Multislice Spiral CT in Diagnosis of Ankle Joint Sports Injury." Journal of Medical Imaging and Health Informatics 11, no. 3 (March 1, 2021): 964–72. http://dx.doi.org/10.1166/jmihi.2021.3346.
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In recent years, with the continuous improvement of medical treatment at home and abroad, computerized tomography (CT) technology has developed rapidly. The market competition at home and abroad is becoming increasingly fierce. The manufacturing cost of CT machines is highly valued by manufacturers. Reducing the manufacturing costs of CT machines is of great significance to medical equipment companies. The CT host is composed of a fixed control system and a rotation control system. Based on the analysis of the existing CT host system structure, this paper designs and implements a set of low-cost asynchronous variable-frequency rotation control systems based on the unchanged control interface of the existing main rotation servo control system, improving the market competitiveness of the product. Based on the constant structure of the rotating machine of the CT machine, the simulation analysis of asynchronous variable frequency control is carried out. A set of asynchronous variable frequency control design schemes and multi-stage speed curve control methods compatible with the original system structure are obtained. The verification and analysis of the functions of acceleration and deceleration control, rotation positioning, fault protection, electromagnetic radiation and other functions at the highest speed meet the system’s main performance requirements, such as acceleration and deceleration time at the highest speed, positioning accuracy, and speed stability. Through internal system verification, testing and clinical verification, the control system designed in this paper meets the design requirements. According to experimental data, the system is fully compatible with a running accuracy of 0.3%. The main performance indicators such as acceleration time and positioning accuracy have been improved. The acceleration time from static acceleration to the maximum speed of 0.39 seconds per revolution increases from 21 seconds to 19.9 seconds, and the positioning accuracy was improved from 0.3° to 0.24°. Finally, this study explores the diagnostic value of multislice spiral CT (MSCT) in ankle trauma.
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Rajendran, P., N. Jamia, S. El-Borgi, and M. I. Friswell. "Wavelet Transform-Based Damage Identification in Bladed Disks and Rotating Blades." Shock and Vibration 2018 (October 17, 2018): 1–16. http://dx.doi.org/10.1155/2018/3027980.
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Blade vibration and blade clearance are effective diagnostic features for the identification of blade damage in rotating machines. Blade tip-timing (BTT) is a noncontact method that is often used to monitor the vibration and clearance of blades in a rotating machinery. Standard signal processing of BTT measurements give one blade response sample per revolution of the machine which is often insufficient for the diagnosis of damage. This paper uses the raw data signals from the sensors directly and employs a wavelet energy-based mistuning index (WEBMI) to predict the presence and locations of damage in rotating blades. The Lipschitz exponent is derived from the wavelet packet coefficients and used to estimate the severity of the damage. In this study, experiments were conducted to obtain BTT measurements on rotating blades at 100 rpm using three different sensors: an active eddy current sensor, a passive eddy current sensor, and an optical sensor. In addition, hammer excitation experiments were conducted for various added mass (damage) cases to compute the damage severity for a bladed disk. To simulate the damage experimentally in the bladed disk and rotating blades, masses were added to the blades to alter their dynamics and mimic the damage. The results indicate that the WEBMI can detect the presence and location of damage in rotating blades using measurements from common BTT sensors. To check the robustness of the proposed damage severity index, the experimental results were compared with numerical simulation for the bladed disk and showed good agreement.
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SZYCA, MIKOŁAJ. "ANALYSIS OF THE BMA K2400 VERTICAL CENTRIFUGE TURBINE IN TERMS OF BALANCING AND VIBRATION DIAGNOSTICS." HERALD OF KHMELNYTSKYI NATIONAL UNIVERSITY 297, no. 3 (July 2, 2021): 71–80. http://dx.doi.org/10.31891/2307-5732-2021-297-3-71-80.
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Physical damage to a material is a diffuse defect in the form of vacancies, microcracks, micro-voids or damaged micro-volumes, which reduce the effective or load-bearing part of the material. Surface fatigue defects, such as deformation and cracks, occur in the bearing during the load transfer. Imbalance is a practical problem in the operation of many rotating machines, causing not only increased vibration of the machine, but also leading to accelerated wear of the rotor bearings. The subject of this work is the analysis of the dynamics of the BMA K2400 centrifuge in terms of the possibility of correcting the balance in the given dynamic state. The paper describes the individual stages of solving the problem of excessive machine vibrations, assuming that its bearings were replaced before the diagnostic test. As a result of the lack of effects after replacing the motor bearings and after analyzing the vibration measurement results presented in article, a decision was made to inspect the centrifuge bearings. The diagnostics was performed again, but it concerned only the bearing node No. 1 with the disassembled basket. The measurements were performed using the DIAMOND 401 AX device, equipped with Wilcoxon 780B acceleration sensors with a sensitivity of 100mV/g. The appearance of a technological defect on the outer ring of the bearing, which is a friction pair with a housing, is not a typical damage for this type of machines and was an interesting problem. The consequence of the occurrence of bearing defects may be an increase in statistical values of the vibration signal and the appearance of new amplitudes in the FFT spectra. A vicious circle is created here, where bearings in poor dynamic condition increase the transmission of vibrations through the machine, and high vibrations accelerate the degradation of the bearings. The poor condition of rolling bearings may also prevent dynamic balancing of the rotor, and thus – lead to further propagation of bearing damage caused by an increased level of the machine’s own vibrations.
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Cheng, Jialu, Peter Werelius, and Nathaniel Taylor. "Temperature Influence on Dielectric Response of Rotating Machine Insulation and Its Correction." Proceedings of the Nordic Insulation Symposium, no. 26 (August 8, 2019): 145–49. http://dx.doi.org/10.5324/nordis.v0i26.3295.
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Insulation condition is an essential aspect for the operational reliability of high voltage rotating machines in power plants and industrial applications. Insulation resistance (IR) and line-frequency dissipation-factor / power-factor (tanδ) measurement are often performed for the assessment of stator insulation condition. These measured values need to be normalized to a reference temperature (e.g. 40 °C) for comparison and trending and this is traditionally achieved by multiplying the results with a certain factor. However, this correction could be subject to error for an individual device since the correction factors recommended by various standards are average values of a certain number of machines at different conditions. In addition to that, insulation condition also has some influence on the temperature dependent property.With the introduction of Dielectric Frequency Response, DFR and Polarization/Depolarization Current, PDC as more advanced insulation diagnostic methods, with proper modelling, temperature correction can be done based on the insulation condition of an individual device and thus accuracy is considerably improved. In this paper, the background of DFR and its superiority in temperature correction are introduced. After that, the numerical Fourier and Inverse Fourier Transformation algorithm is applied to correct the time domain measurement (IR and PDC).
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Hornak, Jaroslav, Václav Mentlík, Pavel Trnka, and Pavol Šutta. "Synthesis and Diagnostics of Nanostructured Micaless Microcomposite as a Prospective Insulation Material for Rotating Machines." Applied Sciences 9, no. 14 (July 22, 2019): 2926. http://dx.doi.org/10.3390/app9142926.
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This paper deals with the topic of composite insulation materials for rotating machines and it is primarily pointed to the synthesis of new three-component insulation system. In connection with this research, the basic components of the insulation system are selected and described by different diagnostic methods. The proposed insulation material is composed of epoxy resin based on bisphenol-A diglycidyl ether, magnesium oxide nanofiller (1 wt %) with its own surface treatment technology using epoxysilane coupling agent ( γ -glycidoxypropyltrimethoxysilane) and polyethylene naphthalate as a reinforcing component. Following the defined topic of the paper, the proposed three-component insulation system is confronted with commonly used insulating systems (PET reinforced and Glass reinforced mica composites) in order to verify the basic dielectric properties (dielectric strength, volume resistivity, dissipation factor) and other parameters determined from phenomenological voltage and current signals, respectively.
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Wu, Jie, Tang Tang, Ming Chen, and Tianhao Hu. "Self-Adaptive Spectrum Analysis Based Bearing Fault Diagnosis." Sensors 18, no. 10 (October 2, 2018): 3312. http://dx.doi.org/10.3390/s18103312.
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Bearings are critical parts of rotating machines, making bearing fault diagnosis based on signals a research hotspot through the ages. In real application scenarios, bearing signals are normally non-linear and unstable, and thus difficult to analyze in the time or frequency domain only. Meanwhile, fault feature vectors extracted conventionally with fixed dimensions may cause insufficiency or redundancy of diagnostic information and result in poor diagnostic performance. In this paper, Self-adaptive Spectrum Analysis (SSA) and a SSA-based diagnosis framework are proposed to solve these problems. Firstly, signals are decomposed into components with better analyzability. Then, SSA is developed to extract fault features adaptively and construct non-fixed dimension feature vectors. Finally, Support Vector Machine (SVM) is applied to classify different fault features. Data collected under different working conditions are selected for experiments. Results show that the diagnosis method based on the proposed diagnostic framework has better performance. In conclusion, combined with signal decomposition methods, the SSA method proposed in this paper achieves higher reliability and robustness than other tested feature extraction methods. Simultaneously, the diagnosis methods based on SSA achieve higher accuracy and stability under different working conditions with different sample division schemes.
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Li, Xiaochuan, Faris Elasha, Suliman Shanbr, and David Mba. "Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning." Energies 12, no. 14 (July 15, 2019): 2705. http://dx.doi.org/10.3390/en12142705.
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Components of rotating machines, such as shafts, bearings and gears are subject to performance degradation, which if left unattended could lead to failure or breakdown of the entire system. Analyzing condition monitoring data, implementing diagnostic techniques and using machinery prognostic algorithms will bring about accurate estimation of the remaining life and possible failures that may occur. This paper proposes a combination of two supervised machine learning techniques; namely, the regression model and multilayer artificial neural network model, to predict the remaining useful life of rolling element bearings. Root mean square and Kurtosis were analyzed to define the bearing failure stages. The proposed methodology was validated through two case studies involving vibration measurements of an operational wind turbine gearbox and a split cylindrical roller bearing in a test rig.
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Vania, A., and P. Pennacchi. "Effects of the Hot Alignment of a Power Unit on Oil-Whip Instability Phenomena." International Journal of Rotating Machinery 2010 (2010): 1–12. http://dx.doi.org/10.1155/2010/385947.
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This paper shows the results of the analysis of the dynamic behaviour of a power unit, whose shaft-train alignment was significantly influenced by the machine thermal state, that was affected in operating condition by high subsynchronous vibrations caused by oil-whip instability phenomena. The dynamic stiffness coefficients of the oil-film journal bearings of the generator were evaluated considering the critical average journal positions that caused the instability onsets. By including these bearing coefficients in a mathematical model of the fully assembled machine, the real part of the eigenvalue associated with the first balance resonance of the generator rotor became positive. This paper shows the successful results obtained by combining diagnostic techniques based on mathematical models of journal bearings and shaft train with detailed analyses of monitoring data aimed to investigate the effects of the hot alignment of rotating machines on the occurrence of oil-whip instability onsets.
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Abouhnik, A., Ghalib R. Ibrahim, R. Shnibha, and A. Albarbar. "Novel Approach to Rotating Machinery Diagnostics Based on Principal Component and Residual Matrix Analysis." ISRN Mechanical Engineering 2012 (March 5, 2012): 1–7. http://dx.doi.org/10.5402/2012/715893.
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Rotating machinery such as induction motors and gears driven by shafts are widely used in industry. A variety of techniques have been employed over the past several decades for fault detection and identification in such machinery. However, there is no universally accepted set of practices with comprehensive diagnostic capabilities. This paper presents a new and sensitive approach, to detect faults in rotating machines; based on principal component techniques and residual matrix analysis (PCRMA) of the vibration measured signals. The residual matrix for machinery vibration is extracted using the PCA method, crest factors of this residual matrix is determined and then machinery condition is assessed based on comparing the crest factor amplitude with the base line (healthy) level. PCRMA method has been applied to vibration data sets collected from several kinds of rotating machinery: a wind turbine, a gearbox, and an induction motor. This approach successfully differentiated the signals from healthy system and systems containing gear tooth breakage, cracks in a turbine blade, and phase imbalance in induction motor currents. The achieved results show that the developed method is found very promising and Crest Factors levels were found very sensitive for machinery condition.
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Chen, Zuoyi, Yuanhang Wang, Jun Wu, Chao Deng, and Weixiong Jiang. "Wide Residual Relation Network-Based Intelligent Fault Diagnosis of Rotating Machines with Small Samples." Sensors 22, no. 11 (May 30, 2022): 4161. http://dx.doi.org/10.3390/s22114161.
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Many existing fault diagnosis methods based on deep learning (DL) require numerous fault samples to train the diagnosis model. However, in industrial applications, rotating machines (RMs) operate in normal states for most of their service life with fault events being rare and thus failure samples are very limited. To solve the problem above, a novel wide residual relation network (WRRN) is proposed for intelligent fault diagnosis of the RMs. Specifically, the WRRN is trained by performing a series of learning tasks in RMs with sufficient samples to obtain knowledge about how to diagnose, and then it is directly transferred to realize fault task of the RM with small samples. In this method, a wide residual network-based feature extraction module is used to generate representative fault features from input samples, and a relation module is designed to calculate the relation score between the sample pairs so as to determine their categories. Extensive experiments are conducted on two RMs to validate the WRRN method. The results demonstrate that the WRRN can accurately identify the fault types of the RMs with only small samples or even one sample. The WRRN significantly outperforms the existing popular methods in diagnostic performance.
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Sinou, Jean-Jacques. "Damage Detection in a Rotor Dynamic System by Monitoring Nonlinear Vibrations and Antiresonances of Higher Orders." Applied Sciences 12, no. 23 (November 22, 2022): 11904. http://dx.doi.org/10.3390/app122311904.
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Since rotor systems are very sensitive and vulnerable to transverse crack, early detection of damage is of paramount importance and essential for rotating machinery. Therefore, one of the main issues is to identify robust characteristics of the rotor vibration response that can be directly attributed to the presence of a transverse crack in a rotating shaft, preferably when the crack is small enough, in order to avoid catastrophic failures of rotating machines. This study investigates the potential links between the nonlinear vibrations and the locations of higher-order antiresonances and structural modifications due to the presence of a breathing crack in rotor systems. Using the proposed numerical results on the evolution of the nonlinear responses of a cracked rotor system, it was observed that a robust diagnostic of the presence of slight damage can be conducted by tracking nonlinear vibrational measurements, with particular attention to the antiresonance behavior of higher orders. These observations can easily serve as target observations for the monitoring system and for identifying the positions of damage at an early stage.