The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Radoslaw Zimroz - One of the best experts on this subject based on the ideXlab platform.
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Vibration Monitoring for local tooth damage in multistage gearboxes
Noise & Vibration Worldwide, 2016Co-Authors: Leonid M. Gelman, Radoslaw Zimroz, Jeffrey Birkel, Howard Leigh-firbank, Daniel M. Simms, B. Waterland, Glyn WhitehurstAbstract:An adaptive approach was applied for local tooth damage diagnostics in gearboxes. The expediency of adaptation was proved experimentally for the new diagnostic feature, the sum of normalized sideband amplitudes. The positive correlation between mesh amplitudes and their sideband amplitudes was found experimentally for the first time.Novel adaptive Vibration Condition Monitoring technology for local tooth damage in gearboxes was developed and experimentally validated.The experimental results showed an increase in effectiveness of the diagnostics when the adaptive technology was used.
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a new feature for Monitoring the Condition of gearboxes in non stationary operating Conditions
Mechanical Systems and Signal Processing, 2009Co-Authors: Walter Bartelmus, Radoslaw ZimrozAbstract:Abstract The paper introduces a new diagnostic feature, which can be used for Monitoring the Condition of planetary gearboxes in time-variable operating Conditions. The novel approach (originally presented in W. Bartelmus, R. Zimroz, Vibration Condition Monitoring of planetary gearbox under varying external load, Mechanical Systems and Signal Processing 23 (2009) 246–257) exploits the fact that a planetary gearbox in bad Condition is more susceptible (yielding) to load than the gearbox in good Condition. The diagnostic method based on the new diagnostic feature is very simple: one needs to capture signals for different external load values and calculate a simple spectrum based feature versus operating Conditions indicator (current or instantaneous rotation speed). In a certain range of operating Conditions the diagnostic relation (i.e. the dependence between the spectral features and the operating Conditions indicator) is linear. However, since a gearbox in bad Condition is more susceptible to load than the gearbox in good Condition the relation will be different for the two cases. Using a simple regression equation one can calculate the slope of the straight line, which expresses the new diagnostic feature. The method is very quick, technically simple, robust and intuitive. This approach has been used for diagnosing the very complex high-power planetary gearbox used in bucket wheel excavators.
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Vibration Condition Monitoring of planetary gearbox under varying external load
Mechanical Systems and Signal Processing, 2009Co-Authors: Walter Bartelmus, Radoslaw ZimrozAbstract:Abstract The paper shows that for Condition Monitoring of planetary gearboxes it is important to identify the external varying load Condition. In the paper, systematic consideration has been taken of the influence of many factors on the Vibration signals generated by a system in which a planetary gearbox is included. These considerations give the basis for Vibration signal interpretation, development of the means of Condition Monitoring, and for the scenario of the degradation of the planetary gearbox. Real measured Vibration signals obtained in the industrial environment are processed. The signals are recorded during normal operation of the diagnosed objects, namely planetary gearboxes, which are a part of the driving system used in a bucket wheel excavator, used in lignite mines. It is found that a planetary gearbox in bad Condition is more susceptible to load than a gearbox in good Condition. The estimated load time traces obtained by a demodulation process of the Vibration acceleration signal for a planetary gearbox in good and bad Conditions are given. It has been found that the most important factor of the proper planetary gearbox Condition is connected with perturbation of arm rotation, where an arm rotation gives rise to a specific Vibration signal whose properties are depicted by a short-time Fourier transform (STFT) and Wigner-Ville distribution presented as a time–frequency map. The paper gives evidence that there are two dominant low-frequency causes that influence Vibration signal modulation, i.e. the varying load, which comes from the nature of the bucket wheel digging process, and the arm/carrier rotation. These two causes determine the Condition of the planetary gearboxes considered. Typical local faults such as cracking or breakage of a gear tooth, or local faults in rolling element bearings, have not been found in the cases considered. In real practice, local faults of planetary gearboxes have not occurred, but heavy destruction of planetary gearboxes have been noticed, which are caused by a prolonged run of a planetary gearbox at the Condition of the arm run perturbation. It may be stated that the paper gives a new approach to the Condition Monitoring of planetary gearboxes. It has been shown that only a root cause analysis based on factors having an influence on the Vibration solves the problem of planetary gearbox Condition Monitoring.
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Adaptive Vibration Condition Monitoring technology for local tooth damage in gearboxes
Insight, 2005Co-Authors: Leonid M. Gelman, Radoslaw Zimroz, Jeffrey Birkel, Howard Leigh-firbank, Daniel M. Simms, B. Waterland, Glyn WhitehurstAbstract:An adaptive approach was applied for local tooth damage diagnostics in gearboxes. The expediency of adaptation was proved experimentally for the new diagnostic feature, the sum of normalised sideband amplitudes. The positive correlation between mesh amplitudes and their sideband amplitudes was found experimentally for the first time. Novel adaptive Vibration Condition Monitoring technology for local tooth damage in gearboxes was developed and experimentally validated. The experimental results showed an increase in effectiveness of the diagnostics when the adaptive technology was used.
Czeslaw Cempel - One of the best experts on this subject based on the ideXlab platform.
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Descriptive parameters and contradictions in triz methodology for Vibration Condition Monitoring of machines
2019Co-Authors: Czeslaw CempelAbstract:TRIZ methodology is a promising innovative tool to obtain various problem solutions, which are close to so called ideal final result IFR. There are some introductory papers of present author like [Skoryna 10],[Cempel 12], [Cempel 14]. But it seems to be a need to make such an approach from different sides in order to see if some new knowledge and technology will emerge. In doing this we need at first to define the ideal final result (IFR). As a next we need to define a set of engineering parameters to describe the problems of Vibration Condition Monitoring (VCM) in terms of TRIZ parameters, and also a set of inventive principles possible to apply on the way to IFR. This means we should present the machine VCM problem by means of engineering descriptive parameters and contradiction matrix of TRIZ. The paper undertakes this important applicational problem and brings some new insight into system and machine VCM problems. It follows from the paper that one can find 17 contradictions and use one set of inventive principles to solve specified contradiction of VCM problem, and also another set of principles to enhance obtained solution.
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Application of TRIZ approach to machine Vibration Condition Monitoring problems
Mechanical Systems and Signal Processing, 2013Co-Authors: Czeslaw CempelAbstract:Abstract Up to now machine Condition Monitoring has not been seriously approached by TRIZ 1 users, and the knowledge of TRIZ methodology has not been applied there intensively. However, there are some introductory papers of present author posted on Diagnostic Congress in Cracow (Cempel, in press [11]), and Diagnostyka Journal as well. But it seems to be further need to make such approach from different sides in order to see, if some new knowledge and technology will emerge. In doing this we need at first to define the ideal final result ( IFR ) of our innovation problem. As a next we need a set of parameters to describe the problems of system Condition Monitoring (CM) in terms of TRIZ language and set of inventive principles possible to apply, on the way to IFR. This means we should present the machine CM problem by means of contradiction and contradiction matrix. When specifying the problem parameters and inventive principles, one should use analogy and metaphorical thinking, which by definition is not exact but fuzzy, and leads sometimes to unexpected results and outcomes. The paper undertakes this important problem again and brings some new insight into system and machine CM problems. This may mean for example the minimal dimensionality of TRIZ engineering parameter set for the description of machine CM problems, and the set of most useful inventive principles applied to given engineering parameter and contradictions of TRIZ.
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Optimization of dimensionality of symptom space in machine Condition Monitoring
Mechanical Systems and Signal Processing, 2010Co-Authors: Czeslaw Cempel, Maciej TabaszewskiAbstract:Abstract With the modern tools of metrology we can measure almost all variables in the phenomenon field of a working machine, and some of measuring quantities can be symptoms of machine Condition. On this basis we can form the symptom observation matrix for Condition Monitoring. From the other side we know that contemporary complex machines can have many modes of failure/damage, so called faults. The paper presents the method of extraction of fault information from the symptom observation matrix by means of singular value decomposition, in the form of generalized fault symptoms. However, at the beginning of Monitoring we do not know the sensitivity of potential symptoms to the given machine faults and to its overall Condition. Hence, some method of symptom observation matrix optimization leading to redundancy minimization is presented first time in this paper. This gives the possibility to assess the diagnostic contribution of every primary measured symptom. Also in the paper some possibility to assess symptom limit value, based on symptom reliability is considered. These concepts are illustrated by symptom observation matrix processing with the special program and the data are taken directly from the machine Vibration Condition Monitoring area.
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Optimization of symptom observation matrix in Vibration Condition Monitoring
2009 8th International Conference on Reliability Maintainability and Safety, 2009Co-Authors: Czeslaw CempelAbstract:In diagnostics of complex machines, for their Condition assessment we often use many dasiawould bepsila symptoms at the beginning, especially at the diagnostic startup of a new machine. The discrete observation of this dasiawould bepsila symptom vector creates so called symptom observation matrix (SOM). Using next the singular value decomposition (SVD) for the given SOM, one can extract the generalized fault symptoms, describing the fault evolution in a given case, and also diagnostic contribution of measured symptoms. Using the symptom reliability concepts further, and the grey system forecast methodology, it is possible to asses the generalized symptoms limit value. In this way one can establish the needed dimensionality of symptom observation matrix, and moreover assess the residual system life. However, doing this we have to establish new criteria for the dimensionality of SOM, based not on the number of symptoms in use, but the quality of diagnostic decision. This concept was verified in the paper using the data taken from real cases of Vibration Condition Monitoring practice.
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decomposition of the symptom observation matrix and grey forecasting in Vibration Condition Monitoring of machines
International Journal of Applied Mathematics and Computer Science, 2008Co-Authors: Czeslaw CempelAbstract:With the tools of modern metrology we can measure almost all variables in the phenomenon field of a working machine, and many of the measured quantities can be symptoms of machine Conditions. On this basis, we can form a symptom observation matrix (SOM) intended for Condition Monitoring and wear trend (fault) identification. On the other hand, we know that contemporary complex machines may have many modes of failure, called faults. The paper presents a method of the extraction of the information about faults from the symptom observation matrix by means of singular value decomposition (SVD), in the form of generalized fault symptoms. As the readings of the symptoms can be unstable, the moving average of the SOM is applied with success. An attempt to assess the diagnostic contribution of a primary symptom is made, and also an approach to assess the symptom limit value and to connect the SVD methodology with neural nets is considered. Finally, a Condition forecasting problem is discussed and an application of grey system theory (GST) to symptom prognosis is presented. These possibilities are illustrated by processing data taken directly from the machine Vibration Condition Monitoring area.
Walter Bartelmus - One of the best experts on this subject based on the ideXlab platform.
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a new feature for Monitoring the Condition of gearboxes in non stationary operating Conditions
Mechanical Systems and Signal Processing, 2009Co-Authors: Walter Bartelmus, Radoslaw ZimrozAbstract:Abstract The paper introduces a new diagnostic feature, which can be used for Monitoring the Condition of planetary gearboxes in time-variable operating Conditions. The novel approach (originally presented in W. Bartelmus, R. Zimroz, Vibration Condition Monitoring of planetary gearbox under varying external load, Mechanical Systems and Signal Processing 23 (2009) 246–257) exploits the fact that a planetary gearbox in bad Condition is more susceptible (yielding) to load than the gearbox in good Condition. The diagnostic method based on the new diagnostic feature is very simple: one needs to capture signals for different external load values and calculate a simple spectrum based feature versus operating Conditions indicator (current or instantaneous rotation speed). In a certain range of operating Conditions the diagnostic relation (i.e. the dependence between the spectral features and the operating Conditions indicator) is linear. However, since a gearbox in bad Condition is more susceptible to load than the gearbox in good Condition the relation will be different for the two cases. Using a simple regression equation one can calculate the slope of the straight line, which expresses the new diagnostic feature. The method is very quick, technically simple, robust and intuitive. This approach has been used for diagnosing the very complex high-power planetary gearbox used in bucket wheel excavators.
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Vibration Condition Monitoring of planetary gearbox under varying external load
Mechanical Systems and Signal Processing, 2009Co-Authors: Walter Bartelmus, Radoslaw ZimrozAbstract:Abstract The paper shows that for Condition Monitoring of planetary gearboxes it is important to identify the external varying load Condition. In the paper, systematic consideration has been taken of the influence of many factors on the Vibration signals generated by a system in which a planetary gearbox is included. These considerations give the basis for Vibration signal interpretation, development of the means of Condition Monitoring, and for the scenario of the degradation of the planetary gearbox. Real measured Vibration signals obtained in the industrial environment are processed. The signals are recorded during normal operation of the diagnosed objects, namely planetary gearboxes, which are a part of the driving system used in a bucket wheel excavator, used in lignite mines. It is found that a planetary gearbox in bad Condition is more susceptible to load than a gearbox in good Condition. The estimated load time traces obtained by a demodulation process of the Vibration acceleration signal for a planetary gearbox in good and bad Conditions are given. It has been found that the most important factor of the proper planetary gearbox Condition is connected with perturbation of arm rotation, where an arm rotation gives rise to a specific Vibration signal whose properties are depicted by a short-time Fourier transform (STFT) and Wigner-Ville distribution presented as a time–frequency map. The paper gives evidence that there are two dominant low-frequency causes that influence Vibration signal modulation, i.e. the varying load, which comes from the nature of the bucket wheel digging process, and the arm/carrier rotation. These two causes determine the Condition of the planetary gearboxes considered. Typical local faults such as cracking or breakage of a gear tooth, or local faults in rolling element bearings, have not been found in the cases considered. In real practice, local faults of planetary gearboxes have not occurred, but heavy destruction of planetary gearboxes have been noticed, which are caused by a prolonged run of a planetary gearbox at the Condition of the arm run perturbation. It may be stated that the paper gives a new approach to the Condition Monitoring of planetary gearboxes. It has been shown that only a root cause analysis based on factors having an influence on the Vibration solves the problem of planetary gearbox Condition Monitoring.
Sofia Koukoura - One of the best experts on this subject based on the ideXlab platform.
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On the use of AI based Vibration Condition Monitoring of wind turbine gearboxes
Journal of Physics: Conference Series, 2019Co-Authors: Sofia Koukoura, James Carroll, Alasdair McdonaldAbstract:Condition Monitoring (CM) systems are installed in wind turbines (WTs) in order to avoid component downtime and reduce maintenance costs. Vibration Monitoring is widely used for the WT gearbox, which is a component with a significant downtime. Given that the installed wind capacity grows, the volume of CM data increases, making manual interpretation of Vibration signals challenging. Therefore, there is a need for an efficient and automated maintenance decision support system. The aim to this paper is to propose an automated framework for gearbox incipient failure diagnosis. The framework utilises Vibration signals and performs health estimation and fault isolation based on signal processing and artificial intelligence (AI) techniques. The methodology is demonstrated through a case study of Vibration data from operating WTs with known gearbox failures. The study can be used to optimise wind turbine maintenance actions.
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Comparison of wind turbine gearbox Vibration analysis algorithms based on feature extraction and classification
IET Renewable Power Generation, 2019Co-Authors: Sofia Koukoura, James Carroll, Alasdair Mcdonald, Stephan WeissAbstract:Health state assessment of wind turbine components has become a vital aspect of wind farm operations in order to reduce maintenance costs. The gearbox is one of the most costly components to replace and it is usually monitored through Vibration Condition Monitoring. This study aims to present a review of the most popular existing gear Vibration diagnostic methods. Features are extracted from the Vibration signals based on each method and are used as input in pattern recognition algorithms. Classification of each signal is achieved based on its health state. This is demonstrated in a case study using historic Vibration data acquired from operational wind turbines. The data collection starts from a healthy operating Condition and leads towards a gear failure. The results of various diagnostic algorithms are compared based on their classification accuracy.
Konstantinos Gryllias - One of the best experts on this subject based on the ideXlab platform.
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Condition Monitoring of Wind Turbine Planetary Gearboxes Under Different Operating Conditions
Journal of Engineering for Gas Turbines and Power, 2020Co-Authors: Alexandre Mauricio, Shuangwen Sheng, Konstantinos GrylliasAbstract:Abstract Digitally enhanced services for wind power could reduce operations and maintenance costs as well as the levelized cost of energy. Therefore, there is a continuous need for advanced Monitoring techniques, which can exploit the opportunities of internet of things and big data analytics, revolutionizing the future of the energy sector. The heart of wind turbines is a rather complex epicyclic gearbox. Among others, extremely critical gearbox components, which are often responsible for machinery stops, are the rolling element bearings. The Vibration signatures of bearings are rather weak compared to other components, such as gears, and as a result, an extended number of signal processing techniques and tools have been proposed during the last decades, focusing toward accurate, early, and on time bearing fault detection with limited false alarms and missed detections. Envelope analysis is one of the most important methodologies, where an envelope of the Vibration signal is estimated usually after filtering around a frequency band excited by impacts due to the bearing faults. Different tools, such as Kurtogram, have been proposed in order to accurately select the optimum filter parameters (center frequency and bandwidth). Cyclic spectral correlation (CSC) and cyclic spectral coherence (CSCoh), based on cyclostationary analysis, have been proved as very powerful tools for Condition Monitoring. The Monitoring techniques seem to have reached a mature level in case a machinery operates under steady speed and load. On the other hand, in case the operating Conditions change, it is still unclear whether the change of the Monitoring indicators is due to the change of the health of the machinery or due to the change of the operating parameters. Recently, the authors have proposed a new tool called improved envelope spectrum via feature optimization-gram (IESFOgram), which is based on CSCoh and can automatically select the filtering band. Furthermore, the CSCoh is integrated along the selected frequency band leading to an improved envelope spectrum (IES). In this paper, the performance of the tool is evaluated and further extended on cases operating under different speeds and different loads. The effectiveness of the methodology is tested and validated on the National Renewable Energy Laboratory (NREL) wind turbine gearbox Vibration Condition Monitoring benchmarking dataset, which includes various faults with different levels of diagnostic complexity as well as various speed and load operating Conditions.
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Condition Monitoring of Wind Turbine Planetary Gearboxes Under Different Operating Conditions
Journal of Engineering for Gas Turbines and Power-transactions of The Asme, 2019Co-Authors: Alexandre Mauricio, Shuangwen Sheng, Konstantinos GrylliasAbstract:Abstract Digitally enhanced services for wind power could reduce Operations and Maintenance (O&M) costs as well as the Levelised Cost Of Energy (LCOE). Therefore, there is a continuous need for advanced Monitoring techniques which can exploit the opportunities of Internet of Things (IoT) and Big Data Analytics, revolutionizing the future of the energy sector. The heart of wind turbines is a rather complex epicyclic gearbox. Among others, extremely critical gearbox components which are often responsible for machinery stops are the rolling element bearings. The Vibration signatures of bearings are rather weak compared to other components, such as gears, and as a result an extended number of signal processing techniques and tools have been proposed during the last decades, focusing towards accurate, early, and on time bearing fault detection with limited false alarms and missed detections. Envelope Analysis is one of the most important methodologies, where an envelope of the Vibration signal is estimated usually after filtering around a frequency band excited by impacts due to the bearing faults. Different tools, such as Kurtogram, have been proposed in order to accurately select the optimum filter parameters (center frequency and bandwidth). Cyclic Spectral Correlation and Cyclic Spectral Coherence, based on Cyclostationary Analysis, have been proved as very powerful tools for Condition Monitoring. The Monitoring techniques seem to have reached a mature level in case a machinery operates under steady speed and load. On the other hand, in case the operating Conditions change, it is still unclear whether the change of the Monitoring indicators is due to the change of the health of the machinery or due to the change of the operating parameters. Recently, the authors have proposed a new tool called IESFOgram, which is based on Cyclic Spectral Coherence and can automatically select the filtering band. Furthermore, the Cyclic Spectral Coherence is integrated along the selected frequency band leading to an Improved Envelope Spectrum. In this paper, the performance of the tool is evaluated and further extended on cases operating under different speeds and different loads. The effectiveness of the methodology is tested and validated on the National Renewable Energy Laboratory (NREL) wind turbine gearbox Vibration Condition Monitoring benchmarking data set which includes various faults with different levels of diagnostic complexity as well as various speed and load operating Conditions.
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Vibration Based Condition Monitoring of Wind Turbine Gearboxes Based on Cyclostationary Analysis
Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy, 2018Co-Authors: Alexandre Mauricio, Konstantinos GrylliasAbstract:Wind industry experiences a tremendous growth during the last few decades. As of the end of 2016, the worldwide total installed electricity generation capacity from wind power amounted to 486,790 MW, presenting an increase of 12.5% compared to the previous year. Nowadays wind turbine manufacturers tend to adopt new business models proposing total health Monitoring services and solutions, using regular inspections or even embedding sensors and health Monitoring systems within each unit. Regularly planned or permanent Monitoring ensures a continuous power generation and reduce maintenance costs, prompting specific actions when necessary. The core of wind turbine drivetrain is usually a complicated planetary gearbox. One of the main gearbox components which are commonly responsible for the machinery breakdowns are rolling element bearings. The failure signs of an early bearing damage are usually weak compared to other sources of excitation (e.g. gears). Focusing towards the accurate and early bearing fault detection, a plethora of signal processing methods have been proposed including spectral analysis, synchronous averaging and enveloping. Envelope analysis is based on the extraction of the envelope of the signal, after filtering around a frequency band excited by impacts due to the bearing faults. Kurtogram has been proposed and widely used as an automatic methodology for the selection of the filtering band, being on the other hand sensible in outliers. Recently an emerging interest has been focused on modelling rotating machinery signals as cyclostationary, which is a particular class of non-stationary stochastic processes. Cyclic Spectral Correlation and Cyclic Spectral Coherence have been presented as powerful tools for Condition Monitoring of rolling element bearings, exploiting their cyclostationary behaviour. In this work a new diagnostic tool is introduced based on the integration of the Cyclic Spectral Coherence along a frequency band that contains the diagnostic information. A special procedure is proposed in order to automatically select the filtering band, maximizing the corresponding fault indicators. The effectiveness of the methodology is validated using the National Renewable Energy Laboratory (NREL) wind turbine gearbox Vibration Condition Monitoring benchmarking dataset which includes various faults with different levels of diagnostic complexity.
