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1
Mogal, S. P., und D. I. Lalwani. „A Brief Review on Fault Diagnosis of Rotating Machineries“. Applied Mechanics and Materials 541-542 (März 2014): 635–40. http://dx.doi.org/10.4028/www.scientific.net/amm.541-542.635.
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Vibration in any rotating machines is due to faults like misalignment, unbalance, crack, mechanical looseness etc. Identification of these faults in rotor systems, model and vibration signal based methods are used. Signal processing techniques such as Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Wigner-Ville Distribution (WVD) and Wavelet Transform (WT) are applied to vibration data for faults identification. The intent of the paper is to present a review and summarize the recent research and developments performed in condition monitoring of rotor system with the purpose of rotor faults detection. In present paper discuss the different signal processing techniques applied for fault diagnosis. Vibration response measurement has given information concerning any fault within a rotating machine and many of the methods utilizing this technique are reviewed. A detail review of the subject of fault diagnosis in rotating machinery is presented.
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Sinha, Jyoti K. „Quantification of Faults in Rotating Machines“. Noise & Vibration Worldwide 38, Nr. 9 (Oktober 2007): 20–29. http://dx.doi.org/10.1260/095745607782689836.
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Over some decades, vibration based condition monitoring has become well accepted and widely used identifying faults in for rotating machines. However the quantification of faults may require a number of experiments to be carried out, which can be time consuming and exorbitant, if not impossible by experiments alone. Experience shows that the combined approach (Experiment and Analysis often Finite Element Analysis) is efficient in quantifying the fault in a much quicker and reliable manner. A few case studies are discussed here to bring out the usefulness of the combined approach.
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Kovaleski, J. L., A. A. Susin, M. Negreiros und R. F. M. Marcal. „Detecting faults in rotating machines“. IEEE Instrumentation & Measurement Magazine 3, Nr. 4 (2000): 24–26. http://dx.doi.org/10.1109/5289.887456.
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4
Espinoza-Sepulveda, Natalia, und Jyoti Sinha. „Mathematical Validation of Experimentally Optimised Parameters Used in a Vibration-Based Machine-Learning Model for Fault Diagnosis in Rotating Machines“. Machines 9, Nr. 8 (07.08.2021): 155. http://dx.doi.org/10.3390/machines9080155.
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Mathematical models have been widely used in the study of rotating machines. Their application in dynamics has eased further research since they can avoid time-consuming and exorbitant experimental processes to simulate different faults. The earlier vibration-based machine-learning (VML) model for fault diagnosis in rotating machines was developed by optimising the vibration-based parameters from experimental data on a rig. Therefore, a mathematical model based on the finite-element (FE) method is created for the experimental rig, to simulate several rotor-related faults. The generated vibration responses in the FE model are then used to validate the earlier developed fault diagnosis model and the optimised parameters. The obtained results suggest the correctness of the selected parameters to characterise the dynamics of the machine to identify faults. These promising results provide the possibility of implementing the VML model in real industrial systems.
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Altaf, S., M. S. Mehmood und M. W. Soomro. „Advancement of Fault Diagnosis and Detection Process in Industrial Machine Environment“. Journal of Engineering Sciences 6, Nr. 2 (2019): d1—d8. http://dx.doi.org/10.21272/jes.2019.6(2).d1.
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Machine fault diagnosis is a very important topic in industrial systems and deserves further consideration in view of the growing complexity and performance requirements of modern machinery. Currently, manufacturing companies and researchers are making a great attempt to implement efficient fault diagnosis tools. The signal processing is a key step for the machine condition monitoring in complex industrial rotating electrical machines. A number of signal processing techniques have been reported from last two decades conventionally and effectively applied on different rotating machines. Induction motor is the one of widely used in various industrial applications due to small size, low cost and operation with existing power supply. Faults and failure of the induction machine in industry can be the cause of loss of throughput and significant financial losses. As compared with the other faults with the broken rotor bar, it has significant importance because of severity which leads to a serious breakdown of motor. Detection of rotor failure has become significant fault but difficult task in machine fault diagnosis. The aim of this paper is indented to summarizes the fault diagnosis techniques with the purpose of the broken rotor bar fault detection. Keywords: machine fault diagnosis, signal processing technique, induction motor, condition monitoring.
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Walker, Ryan, Sureshkumar Perinpanayagam und Ian K. Jennions. „Rotordynamic Faults: Recent Advances in Diagnosis and Prognosis“. International Journal of Rotating Machinery 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/856865.
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Diagnosis and condition monitoring in rotating machinery has been a subject of intense research for the last century. Recent developments indicate the drive towards integration of diagnosis and prognosis algorithms in future integrated vehicle health management (IVHM) systems. With this in mind, this paper concentrates on highlighting some of the latest research on common faults in rotating machines. Eight key faults have been described; the selected faults include unbalance, misalignment, rub/looseness, fluid-induced instability, bearing failure, shaft cracks, blade cracks, and shaft bow. Each of these faults has been detailed with regard to sensors, fault identification techniques, localization, prognosis, and modeling. The intent of the paper is to highlight the latest technologies pioneering the drive towards next-generation IVHM systems for rotating machinery.
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Tvorić, Stjepan, Miroslav Petrinić, Ante Elez und Mario Brčić. „STATIC ECCENTRICITY FAULT DETECTION METHOD FOR ELECTRICAL ROTATING MACHINES BASED ON THE MAGNETIC FIELD ANALYSIS IN THE AIR GAP BY MEASURING COILS“. Journal of Energy - Energija 69, Nr. 4 (30.12.2020): 3–7. http://dx.doi.org/10.37798/202069451.
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Electrical rotating machines have a great economic significance as they enable conversion of energy between mechanical and electrical state. Reliability and operation safety of these machines can be greatly improved by implementation of continuous condition monitoring and supervisory systems. Especially important feature of such systems is the ability of early fault detection. For this reason, several methods for detection and diagnosis of the machine faults have been developed and designed. As fault detection methods can largely differ in the types of detectable faults, machine adoptability and price of the system, a novel method was developed that can be used for cost-effective detection of various faults of electrical machine. Machine fault detection technique presented in this paper is based on the measurement of magnetic field in the air gap. Numerous studies have proven that crucial information about the machine condition can be determined based on measurement and analysis of the magnetic field in the air gap. It has also been confirmed that analysis of the air gap magnetic field can be used to detect, diagnose and recognize various electrical faults in their very early stage. Proposed method of positioning and installation of the measuring coils on ferromagnetic core parts within the air gap region of the machine enables differentiation of various faults. Furthermore, different faults can be detected if measuring coils are placed on the stator teeth then when placed on the rotor side. The paper presents method on how to analyse and process the measured voltages acquired from measuring coils placed within the machine, especially in the case of rotor static eccentricity detection. The methodology is explained by means of finite element method (FEM) calculations and verified by measurements that were performed on the induction machine. FEM calculation model was used to predict measurement coil output of the induction motor for healthy and various faulty states (at different amounts of static eccentricity). These results were then confirmed by measurements performed in the laboratory on the induction traction motor that was specially modified to enable measurements of faulty operation states of the machine. Measurements comprised of several machine fault conditions broken one rotor bar, broken multiple rotor bars, broken rotor end ring and various levels of rotor static eccentricity.
Other methods used for faults detection are primarily based on the monitoring of quantities such as current and vibration and their harmonic analysis. This new system is based on the tracing the changes of induced voltage of the measuring coils installed on the stator teeth. Faults can be detected and differentiated based on RMS value of these voltages and the number of voltage spikes of voltage waveform i.e. without the need of harmonic analyses. If these coils are installed on the rotor it is possible to detect the stator winding faults in a similar manner.
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Jiang, Xiaomo, Fumin Wang, Haixin Zhao, Shengli Xu und Lin Lin. „Novel Orbit-based CNN Model for Automatic Fault Identification of Rotating Machines“. Annual Conference of the PHM Society 12, Nr. 1 (03.11.2020): 7. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1147.
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Various faults in high-fidelity turbomachinery such as steam turbines and centrifugal compressors usually result in unplanned outage thus lowering the reliability and productivity while largely increasing the maintenance costs. Condition monitoring has been increasingly applied to provide early alerting on component faults by using the vibration signals. However, each type of fault in different types of rotating machines usually require an individual model to isolate the damage for accurate condition monitoring, which require costly computation efforts and resources due to the data uncertainties and modeling complexity. This paper presents a generalized deep learning methodology for accurately automatic diagnostics of various faults in general rotating machines by utilizing the shaft orbits generated from vibration signals, considering the high non-linearity and uncertainty of the sensed vibration signals. The sensor anomalies and environmental noise in the vibration signals are first addressed through waveform compensation and Bayesian wavelet noise reduction filtering. Shaft orbit images are generated from the cleansed vibration data collected from different turbomachinery with various fault modes. A multi-layer convolutional neural network model is then developed to classify and identify the shaft orbit images of each fault. Finally, the fault diagnosis of rotating machinery is realized through the automated identification process. The proposed approach retains the fault information in the axis trajectory to the greatest extent, and can adeptly extract and accurately identify features of various faults. The effectiveness and feasibility of the proposed methodology is demonstrated by using the sensed vibration signals collected from real-world centrifugal compressors and steam turbines with different fault modes.
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Luwei, Kenisuomo C., Akilu Yunusa-Kaltungo und Yusuf A. Sha’aban. „Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks“. Machines 6, Nr. 4 (20.11.2018): 59. http://dx.doi.org/10.3390/machines6040059.
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The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount of time is taken to accurately detect and classify rotor-related anomalies which often negate the drive to achieve a truly robust maintenance decision-making system. Earlier studies have attempted to address these limitations by classifying the poly coherent composite spectra (pCCS) features generated at different machine speeds using principal components analysis (PCA). As valuable as the observations obtained were, the PCA-based classifications applied are linear which may or may not limit their applicability to some real-life machine vibration data that are often associated with certain degrees of non-linearities due to faults. Additionally, the PCA-based faults classification approach used in earlier studies sometimes lack the capability to self-learn which implies that routine machine health classifications would be done manually. The initial parts of the current paper were presented in the form of a thorough search of the literature related to the general concept of data fusion approaches in condition monitoring (CM) of rotation machines. Based on the potentials of pCCS features, the later parts of the article are concerned with the application of the same features for the exploration of a simplified two-staged artificial neural network (ANN) classification approach that could pave the way for the automatic classification of rotating machines faults. This preliminary examination of the classification accuracies of the networks at both stages of the algorithm offered encouraging results, as well as indicates a promising potential for this enhanced approach during field-based condition monitoring of critical rotating machines.
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Yunusa-kaltungo, Akilu, und Jyoti K. Sinha. „Effective vibration-based condition monitoring (eVCM) of rotating machines“. Journal of Quality in Maintenance Engineering 23, Nr. 3 (14.08.2017): 279–96. http://dx.doi.org/10.1108/jqme-08-2016-0036.
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Purpose The purpose of this paper is mainly to highlight how a simplified and streamlined approach to the condition monitoring (CM) of industrial rotating machines through the application of frequency domain data combination can effectively enhance the eMaintenance framework. Design/methodology/approach The paper commences by providing an overview to the relevance of maintenance excellence within manufacturing industries, with particular emphasis on the roles that rotating machines CM of rotating machines plays. It then proceeds to provide details of the eMaintenance as well as its possible alignment with the introduced concept of effective vibration-based condition monitoring (eVCM) of rotating machines. The subsequent sections of the paper respectively deal with explanations of data combination approaches, experimental setups used to generate vibration data and the theory of eVCM. Findings This paper investigates how a simplified vibration-based rotating machinery faults classification method based on frequency domain data combination can increase the feasibility and practicality of eMaintenance. Research limitations/implications The eVCM approach is based on classifying data acquired under several experimentally simulated conditions on two different machines using combined higher order signal processing parameters so as to reduce CM data requirements. Although the current study was solely based on the application of vibration data acquired from rotating machines, the knowledge exchange platform that currently dominates present day scientific research makes it very likely that the lessons learned from the development of eVCM concept can be easily transferred to other scientific domains that involve continuous CM such as medicine. Practical implications The concept of eMaintenance as a cost-effective and smart means of increasing the autonomy of maintenance activities within industries is rapidly growing in maintenance-related literatures. As viable as the concept appears, the achievement of its optimum objectives and full deployment to the industry is still subjective due to the complexity and data intensiveness of conventional CM practices. In this paper, an eVCM approach is proposed so that rotating machine faults can be effectively detected and classified without the need for repetitive analysis of measured data. Social implications The main strength of eVCM lies in the fact that it permits the sharing of historical vibration data between identical rotating machines irrespective of their foundation structures and speed differences. Since eMaintenance is concerned with driving maintenance excellence, eVCM can potentially contribute towards its optimisation as it cost-effectively streamlines faults diagnosis. This therefore implies that the simplification of vibration-based CM of rotating machines positively impacts the society with regard to the possibility of reducing how much time is actually spent on the accurate detection and classification of faults. Originality/value Although the currently existing body of literature already contains studies that have attempted to show how the combination of measured vibration data from several industrial machines can be used to establish a universal vibration-based faults diagnosis benchmark for incorporation into eMaintenance framework, these studies are limited in the scope of faults, severity and rotational speeds considered. In the current study, the concept of multi-faults, multi-sensor, multi-speed and multi-rotating machine data combination approach using frequency domain data fusion and principal components analysis is presented so that faults diagnosis features for identical rotating machines with different foundations can be shared between industrial plants. Hence, the value of the current study particularly lies in the fact that it significantly highlights a new dimension through which the practical implementation and operation of eMaintenance can be realized using big data management and data combination approaches.
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Zhou, Juan Li. „Intellectual Gear Fault Detection Based on Wavelet Time-Frequency Analysis“. Applied Mechanics and Materials 373-375 (August 2013): 762–69. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.762.
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In this paper, wavelet packet transform and support vector machines are used to detect gear system faults. Testing signals were obtained by measuring the vibration signals of gear system at different rotating speed for different faults. Vibration feature signals were analyzed using wavelet de-noising. By using wavelet packet transform (WPT), signals were decomposed into different frequency bands. the fault detection is used for calculation of energy percents of every frequency. All these were used for fault recognition using Support vector machine (SVM). SVM and neural network transform results were compared. The research indicates that the de-noised signal is superior to the original one. When dealing with various signals, such as Multi-Faults, the diagnosis identification rates are over 92%. This method can be effectively used not only in engineering diagnosis of different faults of gear system, but also for other machinery fault style classification.
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Cao, Ruifeng, und Akilu Yunusa-Kaltungo. „An Automated Data Fusion-Based Gear Faults Classification Framework in Rotating Machines“. Sensors 21, Nr. 9 (23.04.2021): 2957. http://dx.doi.org/10.3390/s21092957.
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The feasibility and usefulness of frequency domain fusion of data from multiple vibration sensors installed on typical industrial rotating machines, based on coherent composite spectrum (CCS) as well as poly-coherent composite spectrum (pCCS) techniques, have been well-iterated by earlier studies. However, all previous endeavours have been limited to rotor faults, thereby raising questions about the proficiency of the approach for classifying faults related to other critical rotating machine components such as gearboxes. Besides the restriction in scope of the founding CCS and pCCS studies on rotor-related faults, their diagnosis approach was manually implemented, which could be unrealistic when faced with routine condition monitoring of multi-component industrial rotating machines, which often entails high-frequency sampling at multiple locations. In order to alleviate these challenges, this paper introduced an automated framework that encompassed feature generation through CCS, data dimensionality reduction through principal component analysis (PCA), and faults classification using artificial neural network (ANN). The outcomes of the automated approach are a set of visualised decision maps representing individually simulated scenarios, which simplifies and illustrates the decision rules of the faults characterisation framework. Additionally, the proposed approach minimises diagnosis-related downtime by allowing asset operators to easily identify anomalies at their incipient stages without necessarily possessing vibration monitoring expertise. Building upon the encouraging results obtained from the preceding part of this approach that was limited to well-known rotor-related faults, the proposed framework was significantly extended to include experimental and open-source gear fault data. The results show that in addition to early established rotor-related faults classification, the approach described here can also effectively and automatically classify gearbox faults, thereby improving the robustness.
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Friswell, Michael I., und Yong Yong He. „Smart Rotating Machines for Condition Monitoring“. Key Engineering Materials 413-414 (Juni 2009): 423–30. http://dx.doi.org/10.4028/www.scientific.net/kem.413-414.423.
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The concept that changes in the dynamic behaviour of a rotor could be used for general fault detection and monitoring is well established. Current methods rely on the response of the machine to unbalance excitation during run-up, run-down or normal operation, and are mainly based on pattern recognition approaches. Of all machine faults, probably cracks in the rotor pose the greatest danger and research in crack detection has been ongoing for the past 30 years. For large unbalance forces the crack will remain permanently open and the rotor is then asymmetric, which can lead to stability problems. If the static deflection of the rotor due to gravity is large then the crack opens and closes due to the rotation of the shaft (a breathing crack), producing a parametrically excited dynamical system. Although monitoring the unbalance response of rotors is able to detect the presence of a crack, often the method is relatively insensitive, and the crack must be large before it can be robustly detected. Recently methods to enhance the quality of the information obtained from a machine have been attempted, by using additional excitation, for example from active magnetic bearings. This research is directed towards the concept of a smart rotating machine, where the machine is able to detect and diagnose faults and take action automatically, without any human intervention. This paper will consider progress to date in this area, with examples, and consider the prospects for future development.
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Patel, R. K., und V. K. Giri. „Condition monitoring of induction motor bearing based on bearing damage index“. Archives of Electrical Engineering 66, Nr. 1 (01.03.2017): 105–19. http://dx.doi.org/10.1515/aee-2017-0008.
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Abstract The rolling element bearings are used broadly in many machinery applications. It is used to support the load and preserve the clearance between stationary and rotating machinery elements. Unfortunately, rolling element bearings are exceedingly prone to premature failures. Vibration signal analysis has been widely used in the faults detection of rotating machinery and can be broadly classified as being a stationary or non-stationary signal. In the case of the faulty rolling element bearing the vibration signal is not strictly phase locked to the rotational speed of the shaft and become “transient” in nature. The purpose of this paper is to briefly discuss the identification of an Inner Raceway Fault (IRF) and an Outer Raceway Fault (ORF) with the different fault severity levels. The conventional statistical analysis was only able to detect the existence of a fault but unable to discriminate between IRF and ORF. In the present work, a detection technique named as bearing damage index (BDI) has been proposed. The proposed BDI technique uses wavelet packet node energy coefficient analysis method. The well-known combination of Hilbert transform (HT) and Fast Fourier Transform (FFT) has been carried out in order to identify the IRF and ORF faults. The results show that wavelet packet node energy coefficients are not only sensitive to detect the faults in bearing but at the same time they are able to detect the severity level of the fault. The proposed bearing damage index method for fault identification may be considered as an ‘index’ representing the health condition of rotating machines.
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Zuber, Ninoslav, Dragan Cvetkovic und Rusmir Bajrić. „Multiple Fault Identification Using Vibration Signal Analysis and Artificial Intelligence Methods“. Applied Mechanics and Materials 430 (September 2013): 63–69. http://dx.doi.org/10.4028/www.scientific.net/amm.430.63.
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Paper addresses the implementation of feature based artificial neural networks and self-organized feature maps with the vibration analysis for the purpose of automated faults identification in rotating machinery. Unlike most of the research in this field, where a single type of fault has been treated, the research conducted in this paper deals with rotating machines with multiple faults. Combination of different roller elements bearing faults and different gearbox faults is analyzed. Experimental work has been conducted on a specially designed test rig. Frequency and time domain vibration features are used as inputs to fault classifiers. A complete set of proposed vibration features are used as inputs for self-organized feature maps and based on the results they are used as inputs for supervised artificial neural networks. The achieved results show that proposed set of vibration features enables reliable identification of developing bearing and gear faults in geared power transmission systems.
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Dineva, Adrienn, Amir Mosavi, Mate Gyimesi, Istvan Vajda, Narjes Nabipour und Timon Rabczuk. „Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification“. Applied Sciences 9, Nr. 23 (25.11.2019): 5086. http://dx.doi.org/10.3390/app9235086.
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Fault Detection and Diagnosis of electrical machine and drive systems are of utmost importance in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. Multi-label classification has recently gained popularity in various application domains as an efficient method for fault detection and monitoring of systems with promising results. The contribution of this work is to propose a novel methodology for multi-label classification for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. In this research, the Electrical Signature Analysis as well as traditional vibration data have been considered for modeling. Furthermore, the performance of various multi-label classification models is compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.
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Saimurugan, M., und R. Ramprasad. „A dual sensor signal fusion approach for detection of faults in rotating machines“. Journal of Vibration and Control 24, Nr. 12 (01.02.2017): 2621–30. http://dx.doi.org/10.1177/1077546316689644.
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The growing industrial sector utilizes machinery that needs to be monitored continuously by proficient experts and robust software to ensure a proper maintenance strategy. Condition monitoring using vibration signal analysis is one of the chief methods used in predictive maintenance strategy for rotating machinery. Generally, sound signal analysis is considered as secondary as it involves noise. In this paper, the signals for various rotating machinery faults are studied by simulating them in a machine fault simulator at various speeds and the best features are fused to obtain more efficiency in the fault diagnosis of rotating machinery. The vibration signal data obtained from accelerometers and sound signal data from a microphone is extracted for features using wavelet transform. The best features from vibration and sound signals are selected using the decision tree algorithm and are fused. Further, the features are classified using an artificial neural network and the corresponding efficiency at various motor speeds is found. The results of this paper imply that the signal fusion of vibration and sound by the decision tree algorithm is effective in machine fault diagnosis methodologies.
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Wong, Pak Kin, Jian-Hua Zhong, Zhi-Xin Yang und 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, Nr. 6 (14.11.2016): 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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Khan, Asif, Hyunho Hwang und Heung Soo Kim. „Synthetic Data Augmentation and Deep Learning for the Fault Diagnosis of Rotating Machines“. Mathematics 9, Nr. 18 (21.09.2021): 2336. http://dx.doi.org/10.3390/math9182336.
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As failures in rotating machines can have serious implications, the timely detection and diagnosis of faults in these machines is imperative for their smooth and safe operation. Although deep learning offers the advantage of autonomously learning the fault characteristics from the data, the data scarcity from different health states often limits its applicability to only binary classification (healthy or faulty). This work proposes synthetic data augmentation through virtual sensors for the deep learning-based fault diagnosis of a rotating machine with 42 different classes. The original and augmented data were processed in a transfer learning framework and through a deep learning model from scratch. The two-dimensional visualization of the feature space from the original and augmented data showed that the latter’s data clusters are more distinct than the former’s. The proposed data augmentation showed a 6–15% improvement in training accuracy, a 44–49% improvement in validation accuracy, an 86–98% decline in training loss, and a 91–98% decline in validation loss. The improved generalization through data augmentation was verified by a 39–58% improvement in the test accuracy.
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Li, Ke, Peng Chen und Hao Sun. „Intelligent Diagnosis Method for Rotating Machinery Using Ant Colony Optimization“. Advanced Materials Research 518-523 (Mai 2012): 3814–19. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.3814.
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This paper proposes an intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization (ACO) and non-dimensional symptom parameters (NSPs) in order to detect faults and distinguish fault types at an early stage. NSPs are defined for reflecting the features of vibration signals measured in each state. Detection index (DI) using statistical theory has also been defined to evaluate the applicability of the NSPs. The DI can be used to indicate the fitness of an NSP for ACO. Lastly, the state identification for the condition diagnosis of rotating machinery is converted to a clustering problem of the values of NSPs calculated from vibration signals in different states of the machine. Ant colony optimization (ACO) is also introduced for this purpose. Practical examples of fault diagnosis for rotating machinery are provided to verify the effectiveness of the proposed method. The verification results show that the structural faults often occurring in a rotation machinery, such as a unbalance, a misalignment and a looseness states are effectively identified by the proposed method.
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Mishra, Nikhita, Ipshitta Chaturvedi und Janhvi Mehta. „Semiconductor Bearing Fault Recognition“. International Journal of Engineering and Advanced Technology 11, Nr. 1 (30.10.2021): 21–26. http://dx.doi.org/10.35940/ijeat.f3090.1011121.
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Semiconductor manufacturing is consid-ered to be one of the most technologically complicated manufacturing processes. Bearing, being a critical part of the rotating machinery used in the process, plays an essential role as it supports the mechanical rotating body and decreases the friction coefficient. However, extensive use makes this element a target of health degradation, which indirectly causes machine failure. A defective bearing causes approximately 50% of failures in electrical machines. Hence, there arises a dire need for effective fault detection and diagnosis methods to recog-nise fault patterns and help take preventive measures. This paper carries out a comprehensive comparative study of the pre-existing machine learning and deep learning techniques used for diagnosing bearing faults and further devises a novel framework for bearing fault diagnosis based on the results. Unlike the conventional Fault Detection Classifiers (FDC) that operate in the original data space, this algorithm explores the scope for feature extraction and transferability empowered by the deep learning models used.
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Gu, Yi, Jiawei Cao, Xin Song und Jian Yao. „A Denoising Autoencoder-Based Bearing Fault Diagnosis System for Time-Domain Vibration Signals“. Wireless Communications and Mobile Computing 2021 (14.05.2021): 1–7. http://dx.doi.org/10.1155/2021/9790053.
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The condition monitoring of rotating machinery is always a focus of intelligent fault diagnosis. In view of the traditional methods’ excessive dependence on prior knowledge to manually extract features, their limited capacity to learn complex nonlinear relations in fault signals and the mixing of the collected signals with environmental noise in the course of the work of rotating machines, this article proposes a novel approach for detecting the bearing fault, which is based on deep learning. To effectively detect, locate, and identify faults in rolling bearings, a stacked noise reduction autoencoder is utilized for abstracting characteristic from the original vibration of signals, and then, the characteristic is provided as input for backpropagation (BP) network classifier. The results output by this classifier represent different fault categories. Experimental results obtained on rolling bearing datasets show that this method can be used to effectively diagnose bearing faults based on original time-domain signals.
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Mahfoud, Jarir, und Claire Breneur. „Experimental identification of multiple faults in rotating machines“. Smart Structures and Systems 4, Nr. 4 (25.07.2008): 429–38. http://dx.doi.org/10.12989/sss.2008.4.4.429.
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Ngolah, Cyprian F., Ed Morden und Yingxu Wang. „Intelligent Fault Recognition and Diagnosis for Rotating Machines using Neural Networks“. International Journal of Software Science and Computational Intelligence 3, Nr. 4 (Oktober 2011): 67–83. http://dx.doi.org/10.4018/jssci.2011100105.
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Monitoring industrial machine health in real-time is not only in high demand, it is also complicated and difficult. Possible reasons for this include: (a) access to the machines on site is sometimes impracticable, and (b) the environment in which they operate is usually not human-friendly due to pollution, noise, hazardous wastes, etc. Despite theoretically sound findings on developing intelligent solutions for machine condition-based monitoring, few commercial tools exist in the market that can be readily used. This paper examines the development of an intelligent fault recognition and monitoring system (Melvin I), which detects and diagnoses rotating machine conditions according to changes in fault frequency indicators. The signals and data are remotely collected from designated sections of machines via data acquisition cards. They are processed by a signal processor to extract characteristic vibration signals of ten key performance indicators (KPIs). A 3-layer neural network is designed to recognize and classify faults based on a pre-determined set of KPIs. The system implemented in the laboratory and applied in the field can also incorporate new experiences into the knowledge base without overwriting previous training. Results show that Melvin I is a smart tool for both system vibration analysts and industrial machine operators.
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Yao, Zheng, und Qing Xin Zhao. „A Neuron-Fuzzy Technique for Fault Diagnosis in Rotating Machinery“. Advanced Materials Research 204-210 (Februar 2011): 2188–91. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.2188.
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The on-line fault diagnostics technology for machines is fast emerging for the detection of incipient faults as to avoid the unexpected failure. On the basis of fault diagnosis theory and method, this paper presents a applications of techniques for fault detection and classification in rotating machinery based on fuzzy theory and neural network theory, the basic structure and working principle of the fault intelligent diagnosis system are introduced, the knowledge stored in the neuron-fuzzy system has been extracted by a fuzzy rule set with an acceptable degree of interpretability, the model of fuzzy fault diagnosis and the self-study principle are described. The practice proves that this is an effective method of large-scale and complicated electronic equipment, and it can also be applied to other fault diagnosis of complex systems and has certain portability.
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Yunusa-Kaltungo, Akilu, und Ruifeng Cao. „Towards Developing an Automated Faults Characterisation Framework for Rotating Machines. Part 1: Rotor-Related Faults“. Energies 13, Nr. 6 (17.03.2020): 1394. http://dx.doi.org/10.3390/en13061394.
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Rotating machines are pivotal to the achievement of core operational objectives within various industries. Recent drives for developing smart systems coupled with the significant advancements in computational technologies have immensely increased the complexity of this group of critical physical industrial assets (PIAs). Vibration-based techniques have contributed significantly towards understanding the failure modes of rotating machines and their associated components. However, the very large data requirements attributable to routine vibration-based fault diagnosis at multiple measurement locations has led to the quest for alternative approaches that possess the capability to reduce faults diagnosis downtime. Initiatives aimed at rationalising vibration-based condition monitoring data in order to just retain information that offer maximum variability includes the combination of coherent composite spectrum (CCS) and principal components analysis (PCA) for rotor-related faults diagnosis. While there is no doubt about the potentials of this approach, especially that it is independent of the number of measurement locations and foundation types, its over-reliance on manual classification made it prone to human subjectivity and lack of repeatability. The current study therefore aims to further enhance existing CCS capability in two facets—(1) exploration of the possibility of automating the process by testing its compatibility with various machine learning techniques (2) incorporating spectrum energy as a novel feature. It was observed that artificial neural networks (ANN) offered the most accurate and consistent classification outcomes under all considered scenarios, which demonstrates immense opportunity for automating the process. The paper describes computational approaches, signal processing parameters and experiments used for generating the analysed vibration data.
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Holguín-Londoño, Mauricio, Oscar Cardona-Morales, Edgar F. Sierra-Alonso, Juan D. Mejia-Henao, Álvaro Orozco-Gutiérrez und German Castellanos-Dominguez. „Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis“. Mathematical Problems in Engineering 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/7906834.
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Vibration and acoustic analysis actively support the nondestructive and noninvasive fault diagnostics of rotating machines at early stages. Nonetheless, the acoustic signal is less used because of its vulnerability to external interferences, hindering an efficient and robust analysis for condition monitoring (CM). This paper presents a novel methodology to characterize different failure signatures from rotating machines using either acoustic or vibration signals. Firstly, the signal is decomposed into several narrow-band spectral components applying different filter bank methods such as empirical mode decomposition, wavelet packet transform, and Fourier-based filtering. Secondly, a feature set is built using a proposed similarity measure termed cumulative spectral density index and used to estimate the mutual statistical dependence between each bandwidth-limited component and the raw signal. Finally, a classification scheme is carried out to distinguish the different types of faults. The methodology is tested in two laboratory experiments, including turbine blade degradation and rolling element bearing faults. The robustness of our approach is validated contaminating the signal with several levels of additive white Gaussian noise, obtaining high-performance outcomes that make the usage of vibration, acoustic, and vibroacoustic measurements in different applications comparable. As a result, the proposed fault detection based on filter bank similarity features is a promising methodology to implement in CM of rotating machinery, even using measurements with low signal-to-noise ratio.
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Chen, Chih-Hao, Rong-Juin Shyu und Chih-Kao Ma. „A New Fault Diagnosis Method of Rotating Machinery“. Shock and Vibration 15, Nr. 6 (2008): 585–98. http://dx.doi.org/10.1155/2008/203621.
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This paper presents a new fault diagnosis procedure for rotating machinery using the wavelet packets-fractal technology and a radial basis function neural network. The faults of rotating machinery considered in this study include imbalance, misalignment, looseness and imbalance combined with misalignment conditions. When such faults occur, they usually induce non-stationary vibrations to the machine. After measuring the vibration signals, the wavelet packets transform is applied to these signals. The fractal dimension of each frequency bands is extracted and the box counting dimension is used to depict the failure characteristics of the vibration signals. The failure modes are then classified by a radial basis function neural network. An experimental study was performed to evaluate the proposed method and the results show that the method can effectively detect and recognize different kinds of faults of rotating machinery.
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Tang, Shengnan, Shouqi Yuan und Yong Zhu. „Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery“. Processes 8, Nr. 10 (28.09.2020): 1217. http://dx.doi.org/10.3390/pr8101217.
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In the light of the significance of the rotating machinery and the possible severe losses resulted from its unexpected defects, it is vital and meaningful to exploit the effective and feasible diagnostic methods of its faults. Among them, the emphasis of the analysis approaches for fault type and severity is on the extraction of useful components in the fault features. On account of the common cyclostationarity of vibration signal under faulty states, fault diagnosis methods based on cyclostationary analysis play an essential role in the rotatory machine. Based on it, the fundamental definition and classification of cyclostationarity are introduced briefly. The mathematical principles of the essential cyclic spectral analysis are outlined. The significant applications of cyclostationary theory are highlighted in the fault diagnosis of the main rotating machinery, involving bearing, gear, and pump. Finally, the widely-used methods on the basis of cyclostationary theory are concluded, and the potential research directions are prospected.
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Pennacchi, P., und A. Vania. „Diagnosis and Model Based Identification of a Coupling Misalignment“. Shock and Vibration 12, Nr. 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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Niu, Wei, Guo Qing Wang, Zheng Jun Zhai und Juan Cheng. „Fault Classification Model of Rotor Based on Support Vector Machine“. Applied Mechanics and Materials 66-68 (Juli 2011): 1982–87. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.1982.
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The vibration signals of rotating machinery in operation consist of plenty of information about its running condition, and extraction and identification of fault signals in the process of speed change are necessary for the fault diagnosis of rotating machinery. This paper improves DDAG classification method and proposes a new fault diagnosis model based on support vector machine to solve the problem of restricting the rotating machinery fault intelligent diagnosis due to the lack of fault data samples. The testing results demonstrate that the model has good classification precision and can correctly diagnose faults.
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Malla, Chandrabhanu, Ankur Rai, Vaishali Kaul und Isham Panigrahi. „Rolling element bearing fault detection based on the complex Morlet wavelet transform and performance evaluation using artificial neural network and support vector machine“. Noise & Vibration Worldwide 50, Nr. 9-11 (Oktober 2019): 313–27. http://dx.doi.org/10.1177/0957456519883280.
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Condition monitoring and fault diagnosis of rolling element bearings are very important to ensure proper working of different types of machinery. Condition monitoring of rotating machines is mainly based on the analysis of machine vibration. The vibration signals from the mechanical fault generally comprise periodic impulses with specified characteristic frequency corresponds to a particular defect. But due to heavy noise in the industry, the vibration signals have a very low signal-to-noise ratio. Hence, it requires an appropriate technique to extract the impulses from the noisy signal. This article emphasized on the fault diagnosis of rolling element bearings having some specific size of defects on various bearing elements using the complex Morlet wavelet analysis. The phase and amplitude map of the complex Morlet wavelet are utilized for identification and diagnosis of the fault in the rolling element bearing. The amplitude and phase map corresponding to the complex Morlet wavelet are found to show unique informative signatures in the presence of bearing faults. The classification technique based on artificial neural network and support vector machine for rolling element bearing fault detection is presented in this article. The classification results of bearing faults clearly indicate that support vector machine has a more precise bearing fault classification ability than artificial neural network.
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He, Wangpeng, Yin Ding, Yanyang Zi und Ivan W. Selesnick. „Sparsity-based algorithm for detecting faults in rotating machines“. Mechanical Systems and Signal Processing 72-73 (Mai 2016): 46–64. http://dx.doi.org/10.1016/j.ymssp.2015.11.027.
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Zhang, Chao, und De Qing Liu. „Research on Rotating Machinery Vibration Fault Based on Support Vector Machine“. Advanced Materials Research 139-141 (Oktober 2010): 2603–7. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.2603.
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The research on support vector machine in fault diagnose has already obtained a lot of breakthroughs, such as the mode identify problems in small sample, nonlinearity, high dimension and so on. However, there are some limitations in the traditional support vector machine. In this paper, in allusion to the current rotating machinery fault diagnosis problem, the basic principles of support vector machine are studied. According to the complex characteristics of rotating machinery vibration fault, a fault extraction method is proposed based on the K-L transform. Multi-classification algorithm of support vector machine is improved, and the algorithm is used to analyze the rotating machinery vibration. By using its capabilities of model identification and system modeling, the initial symptom, occurrence, development of the typical faults are dynamically analyzed. These provide new ideas and methods for fault diagnosis of rotating machinery.
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Zhang, Dingcheng, Dejie Yu und Xing Li. „Optimal resonance-based signal sparse decomposition and its application to fault diagnosis of rotating machinery“. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, Nr. 24 (26.11.2016): 4670–83. http://dx.doi.org/10.1177/0954406216671542.
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The fault diagnosis of rotating machinery is quite important for the security and reliability of the overall mechanical equipment. As the main components in rotating machinery, the gear and the bearing are the most vulnerable to faults. In actual working conditions, there are two common types of faults in rotating machinery: the single fault and the compound fault. However, both of them are difficult to detect in the incipient stage because the weak fault characteristic signals are usually submerged by strong background noise, thus increasing the difficulty of the weak fault feature extraction. In this paper, a novel decomposition method, optimal resonance-based signal spares decomposition, is applied for the detection of those two types of faults in the rotating machinery. This method is based on the resonance-based signal spares decomposition, which can nonlinearly decompose vibration signals of rotating machinery into the high and the low resonance components. To extract the weak fault characteristic signals in the presence of strong noise effectively, the genetic algorithm is used to obtain the optimal decomposition parameters. Then, the optimal high and low resonance components, which include the fault characteristic signals of rotating machinery, can be obtained by using the resonance-based signal spares decomposition method with the optimal decomposition parameters. Finally, the high and the low resonance components are subject to the Hilbert transform demodulation analysis; the faults of rotating machinery can be diagnosed based on the obtained envelop spectra. The optimal resonance-based signal spares decomposition method is successfully applied to the analysis of the simulation and experiment vibration signals. The analysis results demonstrate that the proposed method can successfully extract the fault features in rotating machinery.
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Nahvi, H., und M. Esfahanian. „Fault identification in rotating machinery using artificial neural networks“. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 219, Nr. 2 (01.02.2005): 141–58. http://dx.doi.org/10.1243/095440605x8469.
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In this paper, an artificial neural network system is designed and employed for fault prediction of rotating machinery systems. Multi-layer feedforward networks, constituted of non-linear neurons, have been employed. A normalization scheme is implemented on the input and output vectors. The performance of the expert structure is optimized to encounter input data with different intensities and non-regular data. More than 40 rotating machinery faults are introduced into the algorithm. To train the network, the data in the vibration identification chart consisting of vibration signals of common rotating machinery faults are used. Computer software is developed to detect machinery faults by using the above techniques and is validated for fault detection of different machinery systems. It is found that the designed network is capable of identifing unknown faults in rotating machinery. The effectiveness of the proposed neural network algorithm is displayed by several tests.
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Guo, Liang, Yingqi Huang, Hongli Gao und Li Zhang. „Ball Screw Fault Detection and Location Based on Outlier and Instantaneous Rotational Frequency Estimation“. Shock and Vibration 2019 (10.07.2019): 1–12. http://dx.doi.org/10.1155/2019/7497363.
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Ball screw, as a crucial component, is widely used in various rotating machines. Its health condition significantly influences the efficiency and position precision of rotating machines. Therefore, it is important to accurately detect faults and estimate fault location in a ball screw system to make sure that the ball screw system runs safely and effectively. However, there are few research studies concerning the topic. The aim of this paper is to fill the gap. In this paper, we propose a method to automatically detect and locate faults in a ball screw system. The proposed method mainly consists of two steps: fault time estimation and instantaneous rotational frequency extraction. In the first step, a statistics-based outlier detection method is proposed to involve the fault information mixing in vibration signals and estimate the fault time. In the second step, a parameterized time-frequency analysis method is utilized to extract the instantaneous rotational frequency of the ball screw system. Once the fault time and instantaneous rotational frequency are estimated, the fault location in a ball screw system is calculated through an integral operation. In order to verify the effectiveness of the proposed method, two fault location experiments under the constant and varying speed conditions are conducted in a ball screw failure simulation testbed. The results demonstrate that the proposed method is able to accurately detect the faults in a ball screw system and estimate the fault location within an error of 22%.
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Li, Yongbo, Xianzhi Wang, Shubin Si und Xiaoqiang Du. „A New Intelligent Fault Diagnosis Method of Rotating Machinery under Varying-Speed Conditions Using Infrared Thermography“. Complexity 2019 (19.08.2019): 1–12. http://dx.doi.org/10.1155/2019/2619252.
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A novel systematic framework, infrared thermography- (IRT-) based method, for rotating machinery fault diagnosis under nonstationary running conditions is presented in this paper. In this framework, IRT technique is first applied to obtain the thermograph. Then, the fault features are extracted using bag-of-visual-word (BoVW) from the IRT images. In the end, support vector machine (SVM) is utilized to automatically identify the fault patterns of rotating machinery. The effectiveness of proposed method is evaluated using lab experimental signal of rotating machinery. The diagnosis results show that the IRT-based method has certain advantages in classification rotating machinery faults under nonstationary running conditions compared with the traditional vibration-based method.
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Luo, Dong Song, und Zheng Fan. „Wavelet Algorithm in Rotating Machinery Fault Feature Extraction“. Advanced Materials Research 823 (Oktober 2013): 451–55. http://dx.doi.org/10.4028/www.scientific.net/amr.823.451.
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A method of Rotating Machinery fault feature extraction based on wavelet transform and Hilbert demodulation is been studied. On the basis of rotating machinery fault mechanism and spectral characteristics, wavelet transform is used to be decompose the vibration acceleration signals of bearing faults into different frequency bands, Which is then used to achieve accurate fault information by Hilbert demodulation. The result shows the method can effectively improve the frequency resolution and realize accurate extraction of fault feature, and it has certain practical value for industrial production of rotating machinery faults diagnosis when applied to the production industry. Key words: Rotating Machinery; bearings; Wavelet algorithm; Hilbert demodulation
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Wang, Hua Qing, Yong Wei Guo, Jian Feng Yang, Liu Yang Song, Jia Pan, Peng Chen und Hong Fang Yuan. „Fault Diagnosis Based on Acoustic Emission Signal for Low Speed Rolling Element Bearing“. Advanced Materials Research 199-200 (Februar 2011): 1020–23. http://dx.doi.org/10.4028/www.scientific.net/amr.199-200.1020.
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The fault of a bearing may cause the breakdown of a rotating machine, leading to serious consequences. A rolling element bearing is an important part of, and is widely used in rotating machinery. Therefore, fault diagnosis of rolling bearings is important for guaranteeing production efficiency and plant safety. Although many studies have been carried out with the goal of achieving fault diagnosis of a bearing, most of these works were studied for rotating machinery with a high rotating speed rather than with a low rotating speed. Fault diagnosis for bearings under a low rotating speed, is more difficult than under a high rotating speed. Because bearing faults signal is very weak under a low rotating speed. This work acquires vibration and acoustic emission signals from the rolling bearing under low speed respectively, and analyzes the both kinds of signals in time domain and frequency domain for diagnosing the typical bearing faults contrastively. This paper also discussed the advantages using the acoustic emission signal for fault diagnosis of rolling speed bearing. From the results of analysis and experiment we can find the effectiveness of acoustic emission signal is better than vibration signal for fault diagnosis of a bearing under the low speed.
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Sepulveda, Natalia Espinoza, und Jyoti Sinha. „Parameter Optimisation in the Vibration-Based Machine Learning Model for Accurate and Reliable Faults Diagnosis in Rotating Machines“. Machines 8, Nr. 4 (23.10.2020): 66. http://dx.doi.org/10.3390/machines8040066.
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Artificial intelligence (AI)-based machine learning (ML) models seem to be the future for most of the applications. Recent research effort has also been made on the application of these AI and ML methods in the vibration-based faults diagnosis (VFD) in rotating machines. Several research studies have been published over the last decade on this topic. However, most of the studies are data driven, and the vibration-based ML (VML) model is generally developed on a typical machine. The developed VML model may not predict faults accurately if applied on other identical machines or a machine with different operation conditions or both. Therefore, the current research is on the development of a VML model by optimising the vibration parameters based on the dynamics of the machine. The developed model is then blindly tested at different machine operation conditions to show the robustness and reliability of the proposed VML model.
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Bachschmid, N., P. Pennacchi, A. Vania, G. A. Zanetta und L. Gregori. „Identification of Rub and Unbalance in 320 MW Turbogenerators“. International Journal of Rotating Machinery 9, Nr. 2 (2003): 97–112. http://dx.doi.org/10.1155/s1023621x03000095.
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This article presents two experiences of applying a model-based fault-identification method to real machines. The first case presented is an unbalance identification in a 320 MW turbogenerator unit operating in a fossil power plant. In the second case, concerning a machine of the same size but from a different manufacturer, the turbine has been affected by a rub in the sealings. This time, the fault is modeled by local bows. The identification of the faults is performed by means of a model-based identification technique in a frequency domain, suitably modified in order to take into account simultaneous faults. The theoretical background of the applied method is briefly illustrated and some considerations are also presented about the best choice of the rotating speed set of the run-down transient to be used for an effective identification and about the appropriate weighting of vibration measurements at the machine bearings.
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Espinoza Sepulveda, Natalia, und Jyoti Sinha. „Comparison of machine learning models based on time domain and frequency domain features for faults diagnosis in rotating machines“. MATEC Web of Conferences 211 (2018): 17009. http://dx.doi.org/10.1051/matecconf/201821117009.
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The development of technologies for the maintenance industry has taken an important role to meet the demanding challenges. One of the important challenges is to predict the defects, if any, in machines as early as possible to manage the machines downtime. The vibration-based condition monitoring (VCM) is well-known for this purpose but requires the human experience and expertise. The machine learning models using the intelligent systems and pattern recognition seem to be the future avenue for machine fault detection without the human expertise. Several such studies are published in the literature. This paper is also on the machine learning model for the different machine faults classification and detection. Here the time domain and frequency domain features derived from the measured machine vibration data are used separated in the development of the machine learning models using the artificial neutral network method. The effectiveness of both the time and frequency domain features based models are compared when they are applied to an experimental rig. The paper presents the proposed machine learning models and their performance in terms of the observations and results.
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Cheng, Junsheng, Dejie Yu, Jiashi Tang und Yu Yang. „Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery“. Shock and Vibration 16, Nr. 1 (2009): 89–98. http://dx.doi.org/10.1155/2009/519502.
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Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD) techniques, the SVD technique based on empirical mode decomposition (EMD) is applied to the fault feature extraction of the rotating machinery vibration signals. The EMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMFs) by which the initial feature vector matrices could be formed automatically. By applying the SVD technique to the initial feature vector matrices, the singular values of matrices could be obtained, which could be used as the fault feature vectors of support vector machines (SVMs) classifier. The analysis results from the gear and roller bearing vibration signals show that the fault diagnosis method based on EMD, SVD and SVM can extract fault features effectively and classify working conditions and fault patterns of gears and roller bearings accurately even when the number of samples is small.
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Yu, Rui, Rui Xiang und Shi Wei Yao. „Extreme Learning Machine for Fault Diagnosis of Rotating Machinery“. Advanced Materials Research 960-961 (Juni 2014): 1400–1403. http://dx.doi.org/10.4028/www.scientific.net/amr.960-961.1400.
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The authors present extreme learning machine (ELM) as a novel mechanism for diagnosing the faults of rotating machinery, which is reflected from the power spectrum of the vibration signals. Extreme learning machine was originally developed for the single-hidden layer feedforward neural network (SLFN) and then extended to the generalized SLFN. We obtained the fault feature table of rotating machinery by wavelet packet analysis of the power spectrum, then trained and diagnosed the fault feature table with extreme learning machine. Diagnostic results show that the extreme learning machine method achieves higher diagnostic accuracy than the probabilistic neural network (PNN) method, exhibiting superior diagnostic performance. In addition, the diagnosis of fault feature table adding noise signal indicates the extreme learning machine method provides satisfactory generalization performance.
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Bachschmid, N., P. Pennacchi, A. Vania, G. A. Zanetta und L. Gregori. „Identification of Rub and Unbalance in 320-MW Turbogenerators“. International Journal of Rotating Machinery 10, Nr. 4 (2004): 265–81. http://dx.doi.org/10.1155/s1023621x04000284.
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This article presents two experiences of application of a model-based fault identification method on real machines. The first case presented is an unbalance identification on a 320-MW turbogenerator unit operating in a fossil power plant. In the second case, concerning a machine of the same size but of a different manufacturer, the Low Pressure (LP) turbine was affected by a rub in the sealings and this time, the fault was modeled by local bows. The identification of the faults is performed by means of a model-based identification technique in frequency domain, suitably modified in order to take into account simultaneous faults. The theoretical background of the applied method is briefly illustrated and some considerations also are presented about the best choice of the rotating speed set of the run-down transient to be used for an effective identification and about the appropriate weighting of vibration measurements at the machine bearings.
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Li, Meng, Yanxue Wang und Chuyuan Wei. „Intelligent Fault Diagnosis of Machines Based on Adaptive Transfer Density Peaks Search Clustering“. Shock and Vibration 2021 (07.04.2021): 1–11. http://dx.doi.org/10.1155/2021/9936080.
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Intelligent fault diagnosis technology of the rotating machinery is an important way to guarantee the safety of industrial production. To enhance the accuracy of autonomous diagnosis using unlabelled mechanical faults data, a novel intelligent diagnosis algorithm has been developed for rotating machinery based on adaptive transfer density peak search clustering. Combined with the wavelet packet energy feature extraction algorithm, the proposed algorithm can enhance the computational accuracy and reduce the computational time consumption. The proposed adaptive transfer density peak search clustering algorithm can adaptively adjust the classification parameters and mark the categories of unlabelled experimental data. Results of bearing experimental analysis demonstrated that the proposed technique is suitable for machinery fault diagnosis using unlabelled data, compared with other traditional algorithms.
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Wang, Yu Rong, und Tian Xing Wu. „Vibration Signal Extraction of Rotating Machines Based on the Analysis of Degree of Cylcostationary“. Advanced Materials Research 546-547 (Juli 2012): 188–93. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.188.
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When early faults occur in rotating machine, the vibration signal being extraction the often contain heavy background noise .In this paper the analysis of degree of cyclostationarity (DCS) was proposed to vibration signal extraction of rotating machine. This method can overcome the shortcoming of Hilbert transformation that will be influenced by many factors, such as end effect, sampling frequency and the added noise in signal. The research results show that the DCS can not only extract vibration signal but also suppress the added noise in the signal. Therefore more accurate fault signal will be extracted and detected through this new method.
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Garcia-Perez, Arturo, Rene J. Romero-Troncoso, Eduardo Cabal-Yepez, Roque A. Osornio-Rios und Jose A. Lucio-Martinez. „Application of high-resolution spectral analysis for identifying faults in induction motors by means of sound“. Journal of Vibration and Control 18, Nr. 11 (18.10.2011): 1585–94. http://dx.doi.org/10.1177/1077546311422925.
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Induction motors are critical components for most industries. Induction motor failures may yield an unexpected interruption at the industry plant. Several conventional vibration and current analysis techniques exist by which certain faults in rotating machinery can be identified. Ever since the first motor was built, plant personnel have listened to the noises emanating from machines; with enough experience, a listener may make a fairly accurate estimate of the condition of a machine. Although there are several works that deal with vibration and current analysis for monitoring and detection of faults in induction motors, the analysis of sound signals has not been sufficiently explored as an alternative non-invasive monitoring technique. The contribution of this investigation is the development of a condition monitoring strategy than can make a reliable assessment of the presence of specific fault condition in an induction motor with a single fault present through the analysis of a sound signal. The proposed methodology is based on the multiple-signal classification algorithm for high-resolution spectral analysis. Results show that the proposed methodology of sound analysis could improve standard techniques for induction motor fault detection, enhancing detectability.
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Elnady, M., J. Sinha und S. Oyadiji. „FAULTS DIAGNOSIS USING ON-SHAFT VIBRATION MEASUREMENT IN ROTATING MACHINES“. International Conference on Applied Mechanics and Mechanical Engineering 15, Nr. 15 (01.05.2012): 1–19. http://dx.doi.org/10.21608/amme.2012.36923.
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