Condition Monitoring Of Machine Tools And Machining Processes Using .
Condition Monitoring of Machine Toolsand Machining Processes using InternalSensor SignalsJARI REPOLicentiate thesisStockholm, Sweden, 2010
TRITA IIP 10-01ISSN 1650-1888ISBN 978-91-7415-600-3School of Industrial Engineering and ManagementSE-100 44 StockholmSWEDENAkademisk avhandling som med tillstånd av Kungliga Tekniska högskolanframlägges till offentlig granskning för avläggande av teknologie licentiatexamen i produktionsteknik fredagen den 9 april 2010 klockan 10.00 i sal M312,Kungl Tekniska högskolan, Brinellvägen 68, Stockholm. Jari Repo, Mars 2010Tryck: Universitetsservice US AB
AbstractCondition monitoring of critical machine tool components and machining processes is a key factor to increase the availability of the machine tool and achieving a more robust machining process. Failures in the machining process andmachine tool components may also have negative effects on the final producedpart. Instabilities in machining processes also shortens the life time of thecutting edges and machine tool.The condition monitoring system may utilise information from several sourcesto facilitate the detection of instabilities in the machining process. To avoidadditional complexity to the machining system the use of internal sensors isconsidered. The focus in this thesis has been to investigate if informationrelated to the machining process can be extracted directly from the internalsensors of the machine tool.The main contibutions of this work is a further understanding of the directresponse from both linear and angular position encoders due the variations inthe machining process. The analysis of the response from unbalance testingof turn tables and two types of milling processes, i.e. disc-milling and slotmilling, is presented. It is shown that operational frequencies, such as cutterfrequency and tooth-passing frequency, can be extracted from both active andinactive machine axes, but the response from an active machine axis involvesa more complex analysis. Various methods for the analysis of the responsesin time domain, frequency domain and phase space are presented.Keywords: Condition monitoring, machine tool, machining process, milling,position encoders, signal analysisv
AcknowledgementThe major part of the work presented in this thesis is carried out within theDLP-E project at Volvo Aero Corporation in Trollhättan, Sweden, duringthe years 2007-2009. The project was supported financially by VINNOVA1through the MERA2 research programme, which is gratefully acknowledged.First of all, I would like to thank my local supervisor Dr. Tomas Beno and cosupervisor Prof. Lars Pejryd at the University West for their excellent guidanceand encouragement during this work, even during the busiest periods. I alsothank Prof. Mihai Nicolescu for giving me the opportunity to become a PhDstudent at the Royal Institute and for reviewing of this thesis. Project leaderAndreas Rudqvist at Volvo Aero Corporation also deserves special thanks forhis devoted participation in the project and for driving the project forward.During this work I have had the opportunity to use modern equipment at theProduction Technology Centre in Trollhättan. Special thanks to Per Johansson, Tomas Gustavsson, Jörgen Berg, and Ulf Hulling, for your assistanceduring the experimental work. I also appreciate the support from my colleagues Mattias Ottosson and Hans Dahlin for solving some practical issues,and the support from Anna-Karin Christiansson. The discussions with NiklasEricsson at the University West and Arne Nordmark at the Royal Institute ofTechnology also gave valuable guidance in some of the theory, which is gratefully appreciated.Finally, I would like to thank my family for supporting me in this work. Veryspecial thanks to Linda and my children Robin and Ella for their patience andlove throughout this journey.Jari RepoTrollhättan, Mars, 201012The Swedish Governmental Agency for Innovation SystemsManufacturing Engineering Research Areavii
PublicationsThe following papers are appended to the thesis.Repo, J., Beno, T., Pejryd, L. (2009). New Aspects on Condition Monitoringof Machine Tools and Machining Processes. Proceedings of the 3’rd SwedishProduction Symposium (SPS’09), Göteborg, Sweden, 2-3 December 2009.Repo, J., Beno, T., Pejryd, L. (2010). Machine Tool and Process ConditionMonitoring Using Poincaré Maps. The International Conference on Competitive Manufacturing (COMA’10), Stellenbosch, South Africa, 3-5 February2010.ix
List of SymbolsapαLC(ε)d(ε)Δϕ(t)ftoothfzf0Fsϕ(t)ϕu (t)I(τ )kλ1mmunωNRLtTsτUUa , Ub , Urvcx [x1 , x2 , . . . , xn ]x, y, z, S1zzAxial depth of cut [mm]Lissajous angle [rad]Correlation sumCorrelation dimensionModulation signal [rad]Tooth-passing frequency [Hz]Feed per tooth [mm/tooth]Main frequency [Hz]Sampling frequency [Hz]Phase of an analytic signal [rad]Unwrapped phase [rad]Mutual information functionDiscrete time, signal segment indexLargest Lyapunov exponentEmbedding dimensionUnbalance mass [kg]Spindle speed [rpm]Angular velocity [rad/s]Number of samplesLissajous radius [V]Continuous time [s]Sampling interval [s]Reconstruction delayArbitrary voltage signal [V]Differentially measured voltage signals [V]Cutting speed [m/min]State vectorMachine feed axis (x, y, z) and spindle axis (S1)Number of cutting insertsComplex/analytic signalxi
AbbreviationsACFAutocorrelation functionCBMCondition-based maintenanceCMSCondition monitoring systemCNCComputer numerical controlDACDigital-to-analogue converterDAQData acquisitionDFTDiscrete Fourier transformFATFactory acceptance testFFTFast Fourier transformFNNFalse nearest neighbourHHTHilbert-Huang transformHTHilbert transformIATInstallation acceptance testI/OInput/outputMIMutual information functionRMSRoot mean squareSNRSignal-to-noise ratioTCMTool mondition monitoringTCMS Tool condition monitoring systemTFATime-frequency analysisxiii
ContentsAbstract . . . .AcknowledgementPublications . . .List of Symbols .Abbreviations . .vviiixxixiii1 Introduction1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . .1.2 Aim and scope . . . . . . . . . . . . . . . . . . . . . . . . . .1.3 Research approach . . . . . . . . . . . . . . . . . . . . . . . .33672 Principles of condition monitoring2.1 Acceptance testing of machine tool components . . . . . . . .2.2 Role of condition monitoring systems . . . . . . . . . . . . . .2.3 Sensorless condition monitoring . . . . . . . . . . . . . . . . .2.3.1 Internal drive signals . . . . . . . . . . . . . . . . . . .2.3.2 Encoder signals . . . . . . . . . . . . . . . . . . . . . .2.4 Position encoders in CNC machine tools . . . . . . . . . . . .2.5 Principles for measuring of the position encoder output signals2.5.1 Drive modules . . . . . . . . . . . . . . . . . . . . . . .2.5.2 Counter card . . . . . . . . . . . . . . . . . . . . . . .2.5.3 Data acquisition . . . . . . . . . . . . . . . . . . . . . .991113131414161617183 Signal analysis methods3.1 Characteristics of the output signals from position encoders forCNC machine tools . . . . . . . . . . . . . . . . . . . . . . . .3.1.1 General considerations regarding the analysis of positionencoder signals . . . . . . . . . . . . . . . . . . . . . .3.1.2 Estimation of the SNR from measured signals . . . . .19I Introductory Chapters192324xv
3.23.33.43.53.63.73.1.3 Filtering effects on the encoder signals . . . . . . . . .Fourier analysis . . . . . . . . . . . . . . . . . . . . . . . . . .Lissajous curves . . . . . . . . . . . . . . . . . . . . . . . . . .3.3.1 Using Lissajous figures as vibration amplitude estimator3.3.2 Formation of Lissajous figures from samples . . . . . .Hilbert transform . . . . . . . . . . . . . . . . . . . . . . . . .3.4.1 Separation of the modulation signal from the unwrappedphase . . . . . . . . . . . . . . . . . . . . . . . . . . . .3.4.2 Scaling of the unwrapped phase . . . . . . . . . . . . .Hilbert-Huang transform . . . . . . . . . . . . . . . . . . . . .Nonlinear time series analysis . . . . . . . . . . . . . . . . . .3.6.1 Mutual information . . . . . . . . . . . . . . . . . . . .3.6.2 Embedding dimension . . . . . . . . . . . . . . . . . .3.6.3 Chaotic invariants . . . . . . . . . . . . . . . . . . . . .3.6.4 Poincaré sections . . . . . . . . . . . . . . . . . . . . .Selection of signal analysis methods . . . . . . . . . . . . . . .4 Exeperimental work4.1 Linear encoder response to rotating unbalance . . . . . . . . .4.1.1 Description . . . . . . . . . . . . . . . . . . . . . . . .4.1.2 Signal analysis . . . . . . . . . . . . . . . . . . . . . .4.1.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . .4.2 Machining of aerospace component - industrial trial . . . . . .4.2.1 Description . . . . . . . . . . . . . . . . . . . . . . . .4.2.2 Segmentation of the measured signals . . . . . . . . . .4.2.3 Vibration amplitude estimation from the Lissajous figure4.2.4 Analysis of the rotary encoder signals . . . . . . . . . .4.2.5 Phase space reconstruction . . . . . . . . . . . . . . . .4.2.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . .4.3 Slot-milling with various number of cutting inserts . . . . . . .4.3.1 Description . . . . . . . . . . . . . . . . . . . . . . . .4.3.2 Segmentation of the measured signals . . . . . . . . . .4.3.3 Noise characterisation and SNR estimation . . . . . . .4.3.4 Measuring of the Lissajous angle from the inactive feedaxis signals . . . . . . . . . . . . . . . . . . . . . . . .4.3.5 Spectral analysis . . . . . . . . . . . . . . . . . . . . .4.3.6 Nonlinear analysis of the active feed axis modulationsignal . . . . . . . . . . . . . . . . . . . . . . . . . . .4.3.7 Phase plane analysis . . . . . . . . . . . . . . . . . . .4.3.8 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 57586065676770717273757879
5 Conclusions and future work5.1 Conclusions of experimental work . . . . . . . . . . . . . . . .8384References87MATLAB script est alpha91II Included PapersNew Aspects on Condition Monitoring of Machine ToolsMachining Processes1Introduction . . . . . . . . . . . . . . . . . . . . . . . . .2Machine tool internal sensor signals . . . . . . . . . . . .2.1Linear and rotary encoders for motion control . .2.2Encoder output signals . . . . . . . . . . . . . . .2.3Additional information from the encoder signals .3Experimental setup . . . . . . . . . . . . . . . . . . . . .3.1Excitation of the machine tool structure . . . . .3.2Experiments with unbalance . . . . . . . . . . . .3.3Measurement setup . . . . . . . . . . . . . . . . .4Time series analysis . . . . . . . . . . . . . . . . . . . . .4.1Measured time signals . . . . . . . . . . . . . . .4.2Fourier analysis applied to the time series . . . .4.3Poincaré analysis applied to measured time series5Results and discussion . . . . . . . . . . . . . . . . . . .6Conclusions . . . . . . . . . . . . . . . . . . . . . . . . 10References113Machine Tool and Process Condition Monitoring Using PoincaréMaps1Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . .2Theoretical framework . . . . . . . . . . . . . . . . . . . . . .2.1Dynamical systems . . . . . . . . . . . . . . . . . . . .2.2Phase space representation . . . . . . . . . . . . . . . .2.3Phase space reconstruction . . . . . . . . . . . . . . . .2.4Estimating the reconstruction delay . . . . . . . . . . .2.5Estimating the embedding dimension . . . . . . . . . .2.6Chaotic invariants . . . . . . . . . . . . . . . . . . . . .2.7Poincaré sections . . . . . . . . . . . . . . . . . . . . .2.8Visualising of Poincaré maps . . . . . . . . . . . . . . .117118119119119120120121122124124xvii
345Experimental studies . . . . . . . . . . .3.1Preprocessing of the time series .3.2Reconstruction of the phase spaceResults and discussion . . . . . . . . . .Conclusions . . . . . . . . . . . . . . . .Referencesxviii.125125127128128131
Introductory Chapters1
Chapter 1Introduction1.1BackgroundMachine tools are composed of several subsystems, such as structures, electrical drive systems, controllers and actuators, which are all involved whenperforming the desired machining operations. The mechanical structure ofthe machine tool is often designed to be extremely rigid to withstand theforces created during the machining operation. Multitask machine tools aredesigned to perform several different machining operations such as turning,milling, drilling etc. in the same setup, which requires more degrees of freedomthan dedicated machine tools. The additional number of degrees of freedomhowever, comes with a price - some of the rigidity is sacrificed. Multitaskmachine tools are not used for their rigidity, but for their capacity of handlinglarge and geometrically advanced components and for their flexibility to allow manufacturing in a single setup, i.e. without the need of refixturing thecomponent.The availability and utilisation of machine tools are key factors which have adirect influence on the economy of the manufacturing company. Non-workingmachine tools due to scheduled and unscheduled maintenance, process or machine tool component failure etc., have a negative effect on both availabilityand utilisation, which should be avoided as far as possible. The robustnessof the machining process is also a key factor in reaching an economicallyfavourable production simulation.3
Machine tool structural components, such as guideways, bearings and ballscrews, are subjected to gradual wear, which may be long-term. Testing ofmachine tool components on a regular basis is therefore important to reducethe risk of severe failures and breakdowns. Generally, the testing proceduresrequire that the machine tool must temporarily be taken out of service, thusreducing the availability of the machine tool. The testing is often carried out asa part of maintenance programs, but testing may also be needed when failures,such as unexpected collisions, have occured. Maintenance of machine tools isimportant to ensure high consistency between the produced parts, especiallywhen i) machine tools and spare parts are expensive ii) part consistency iscritical iii) downtime cost is extremely high.Traditional methods to test different machine tool components include DoubleBall-Bar (DBB), Laser Doppler Vibrometry (LDV) and Laser Interferometry.These methods require mounting of additional equipment to perform the measurements, which is relatively time consuming. Appropriate maintenance activities, i.e. corrective actions, are then undertaken based on the results fromthe measurements. It can however be questioned when to motivate the use ofsuch detailed assessments of the machine tool because of the waste of valuable production time. The preferred way is to use quick test of some criticalmachine tool component to indicate if more advanced test methods must beused. The main drawback with the traditional methods for testing of machinetools is that these tests are performed off-process and are not considering thespecific cutting parameters, and the spindle is not running.The positional accuracy of machine tools is dependent on the function of critical components, such as the guideways, ball screws, bearings and spindleshaft. Any deterioration, such as wear or misalignment, of these components,increases the risk of scrap production and later machine tool failures. Wearof spindle components has a strong influence on the performance of the spindle. Typical indicators of poor performance are i) increased temperature inthe spindle housing due to wear of spindle bearings and ii) increased powerconsumption and iii) rotational asymmetry (run out) caused by misalignmentof the spindle axis or iv) significantly increased vibration amplitudes.To increase the availability and utilisation of machine tools, a maintenancefunction based on the actual condition (or health) of the machine tools istherefore desirable. A condition monitoring system, CMS, capable of inprocess monitoring of the actual condition of the machine tool and machiningprocess, may not only provide early indications of problems, but may alsoactivate necessary control functions to perform the appropriate corrective ac4
tion, e.g. temporarily halting the machining process, updating the machiningprocess parameters, or call for human assistance. This type of active control mechanism has a substantial potential to increase the robustness of themachining process.The aim with condition monitoring is early detection of disturbances in themachining process and wear of machine tool components. A machining systemis the interaction between the between the machining process and the elasticstructure of the machine tool. The cutting forces are created through theinteraction between the cutting tool and the workpiece.Tool wear has been excessively researched in the past and have focused ontool wear detection, tool breakage detection, and the estimation of remainingtool life. Various techniques have been applied, with and without additionalsensors. Sensor based tool condition monitoring, TCM, are mainly basedon measuring of the cutting force components using a multi-channel table dynamometer or rotating dynamometer, vibration amplitude using multi-channelaccelerometers, audible sound from the machining process, and high-frequencysound or acoustic emission, AE. Sensorless TCM are mainly based on measuring of internal drive signals, such as the feed motor current, spindle motorcurrent and spindle power. Combined measuring of multiple quantities is alsopossible.The use of external sensors is however not always practical since it adds complexity to the overall machining arrangement - various number and types ofsensors must be mounted in the close vicinity of the machining process, makingthem subjected to the heat, chips and coolant, which may affect the lifetimeof the sensors and also quality of the measurements. The wirering of thesensors is another issue that must be considered especially in more advancedmachining operations. External sensors also require additional maintenanceand calibration in order to function properly.A potentially attractive way to achieve a more robust solution to conditionmonitoring, compared with the traditional approach using external sensors,is to use the internal sensors and signals which are already available in themachine tool. Assuming that more information which is relevant to the monitoring task actually can be extracted from the signals, the complexity of themonitoring system may therefore be significantly reduced. Finding signaturesof specific phenomena, such as disturbances due to wear of critical machinetool components, and disturbance in the machining process due to tool wearor breakage, may also provide deeper insight into the health of machine tools5
and further understanding about the dynamics of machining processes due tothe choice of machining parameters. The information can then be used athigher level to support a condition based maintenance, CBM, function withinthe manufacturing company.An issue often experienced in larger manufacturing companies with machineparks comprising multiple machine tools built on the same specification isthat even if these machine tools are expected to produce identical parts, theremay be some deviations in the dimensions of the produced parts, i.e. machinetools sometimes appear to behave more like individuals. This is detectedafter a dimensional check of the produced part and additional rework maybe needed to achieve the desired dimensions. Very often it is not alwayspossible with the currently used test methods to pin point the root cause ofthe problem. In general, this has a negative effect on the productivity in thatspecial versions of the numerical code and compensation schemes must bedeveloped and maintained for each of the supposedly identical machine tools.This additional complexity with variations among machine tools is howeverleft outside this thesis.1.2Aim and scopeThe aim of this work is to investigate the possibilities to use internal machinetool signals for condition monitoring of machine tools and machining processes.This is important in order to achieve more robust machining processes withoutadding complexity to the overall machining system. In this work, a 5-axismultitask machine and various material removal processes, such as millingand drilling, are considered.Condition monitoring involves measuring, processing and analysis of signals,the characterics of the measured signals must be known in order to selectappropriate methods for the processing and analysis of them. A major part inthis work is therefore to study the responses during various type of excitationsof the machine tool and present suitable strategies to extract the useful partfrom the signals.The main research question is therefore whether it is possible to extract usefulinformation related to the health of the machine tool and stability of machiningprocesses from the internal sensors of the machine tool. A related question is6
also are what type of phenoma can actually be detected.The quality of the information can be expressed in terms of its objectivity,repeatability, accuracy and errors, which must must also be considered inorder to evaluated the usefulness of the extracted information from the pointof view of machine tool availability.The sensitivity of the method is related to the minimum detectable change ofthe wear and level of disturbance, which needs to be investigated.The research questions for this thesis can be summarised as: Is it possible to detect deficient machine tool components using internalsensor signals? Is it possible to detect machining process instabilities using internal sensor signals? What signal analysis methods are suitable to extract the useful partfrom the internal sensors signals?1.3Research approachThe thesis has taken an experimental approach and is based on observationsobtained during machining in a modern 5-axis multitask machine tool. Variousexperiments have been performed which allow the systemtic study of certainphenomena. The main focus has been to study the time behaviour of theoutput signals due to vibration generated for various periodic excitation andvibration generated from rotating unbalance and vibrations generated fromimpacts.The characteristic behaviour of the encoder output signals was initially unknown and needed a thorough investigation before any further analysis of themcould be undertaken. To get a fundamental understanding of the behaviourof the signals, initial experiments with minimal complexity have been carriedout, including both non-machining and various machining tests. Several possibilities for the connection of the measurement equipment have been tried7
out until a final measurement chain were developed which allows high-qualityand reliable measurements without disturbing the overall machining system.Various numerical methods have been applied to the encoder signals in order toextract the useful part from the signals. A subset of these methods have thenbeen selected when characterising various machining processes. The analysishas been carried out offline.8
Chapter 2Principles of condition monitoringMaintaining the health of macine tools and establishing stable machining processes is of major importance to reduce the risk of malfunctioning equipmentand ensure that high quality parts are produced. This can be achieved by testingof critical machine tool components and online measuring and analysis of oneor more quantities from the machining process in order to adjust the processtowards more stable machining regions. From the initial acceptance tests ofmachine tools this chapter reviews some principles of condition monitoring ofthe machining process using various methods found in the literature.2.1Acceptance testing of machine toolcomponentsTesting of machine tool components is important through its life cycle to avoidsevere breakdowns during operation. The testing procedure itself is carriedout both at the suppliers shop and after the installation. Generally, a factory acceptance test, FAT, is carried out first at the suppliers shop beforethe delivery of the machine tool. After installation at the customers shop,an installation acceptance test, IAT, is performed as a final validation. Rearrangement of machine parks at the customers shop, which may affect thealignment of structural components, is another reason when acceptance testsshould be performed. For a 5-axis multitask machine tool, the acceptance testprocedure may include measuring of the following properties [1]:9
noise level during a well-defined and well-behaved machining operation, geometrical measurement, spindle speed, feed rate, idling power, radial and axial spindle vibrations (full spindle speed range), deflection of the machine tool structure, clamping force, i.e. the force that pulls the tool into the tool holder, and position accuracy of the linear and rotary axesThe duration of the FAT and IAT depends on the complexity of the machinetool and which properties are measured, but may take several days to complete. These tests are however performed only a few times during the life cycleof the machine tool.The linear and angular position axes are tested for positional accuracy, repeatability and backlash. Measuring of the alignment of the linear axes isnormally performed using LASER interferometry. The accuracy of the spindleshaft speed is measured using rotary encoder. The linear and circular interpolation capability of the machine tool is also measured to obtain the maximumdeviation from the programmed motion. The circularity test is normally performed using special measuring devices, such as the Renishaw Double BallBar, DBB.Machining of high quality parts is strongly dependent on that high relativeposition accuracy between the workpiece being machined and the cutting toolcan be achieved by the machine tool. The machine tool structure will howeverdeform over time due to thermal effects and wear of structural components,which makes the long-term behaviour of the machine tool difficult to predict.Deterioration of the machine tool condition may also affect its positional accuracy. Thus, failing in maintaining the positional accuracy may result in thatthe dimensions of the produced part will fall outside the part specification.The manufacturing company may therefore not entirely rely on initial acceptance tests, ATs, since the results from ATs will most probably only be validwithin a limited time window.10
2.2Role of condition monitoring systemsThe machining process is either continuous, such as turning or drilling, orintermittent, such as the milling operation. Continuous operations are performed with single cutting edge, removing material from one spindle revolutionto the next. Intermittent operations involve one or more cutting edges, removing material from one tooth to the other. In both cases, material is removedfrom the workpiece under the generation of chips. The machining operationsare controlled by various parameters, such as the spindle speed, depth of cutand feed rate, etc.If the correct machining parameters are set, the machining operation is expected to perform well and the final produced part will meet the final requirements given in the part specification. Depending on the machining processcharacteristics, the cutting tool may have a short or long life time. For costeffective production, the number of tool changes should be kept at minimumand the cutting tool inserts must be used close to the limiting tool life without violating the overall machining system. This requires in-depth knowledgeabout the tool wear rate and maximim tool life for the actual machining setupand machining conditions.In well behaved machining processes, the tool life can be more or less accurately determined and the tool change interval may therefore be optimisedusing some tool wear criterion, such as the maximum flank wear. The simplecase will however require almost a gradual tool wear. For more complex materials and demanding machining processes, tool wear may become excessiveand sudden events, such as tool chipping and breakage, will most likely occur.The monitoring of the tool condition may therefore be very difficult since suchunexpected events occur within a relatively short time interval.The behaviour of the machining process is also dependent on the workpiecematerial, cutting tool material and geometry, actual machining process parameters and the condition of the overall machining system. Hardness variationsin the workpiece material, which can be traced back to the manufacturing ofthe workpiece material itself, is another factor which may increase the unpredictability of the machining process, leading to drastically shorter tool life,tool chipping and tool breakage.The cutting tool can be regarded as the limiting component of the machiningprocess and is the main reason why machining process condition monitoring11
Figure 2.1 – Role of condition monitoring systems.is mostly concerned with the actual state of the cutting tool.When the cutting tool is worn the cutting force and vibration amplitudes tendto increase and the machining process may become unstable. The main taskof the condition monitoring system, CMS, is to collect relevant data from themachining process, then process and analyse the data to detect symptoms oftroubles, but also to signal a control function to adjust the machining processparameters to a more stable machining region. Within the stable region,optimisation can be performed to meet some criterion, such as maximising thematerial removal rate, minimising the production cost, etc
Condition monitoring of critical machine tool components and machining pro-cesses is a key factor to increase the availability of the machine tool and achiev-ing a more robust machining process. Failures in the machining process and machine tool components may also have negative effects on the final produced part.
3 Condition monitoring systems in offshore wind 2 3.1 CMS based techniques & methods 2 3.2 Condition monitoring of offshore wind turbine main components 4 3.3 Typical measurement parameters of condition monitoring systems in an offshore wind turbine 5 3.4 Commercially available condition monitoring systems 7 4 Wave Energy Converters 8
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Machine tools are power-driven equipment designed to drill, bore, grind, or cut metal or other material. LISTING OF MACHINE TOOLS A complete list of machine tools including specialized machine tools currently authorized for issue is in Component List C 3405/70-lL. SPECIALIZED MACHINE TOOLS In view of the
telemetry 1.24 Service P threshold_migrator 2.11 Monitoring P tomcat 1.30 Monitoring P trellis 20.30 Service P udm_manager 20.30 Service P url_response 4.52 Monitoring P usage_metering 9.28 Monitoring vCloud 2.04 Monitoring P vmax 1.44 Monitoring P vmware 7.15 Monitoring P vnxe_monitor 1.03 Monitoring vplex 1.01 Monitoring P wasp 20.30 UMP P .
Current condition monitoring practice in wind turbine rotors involves tracking rotor imbalance, aerodynamic, surface roughness and overall performance and offline and online measurements of stress and strain. Keywords : wind energy, condition monitoring. GJRE-A Classification : FOR Code: 660202. Condition Monitoring of Wind Turbines A Revie w
[3]. Condition monitoring approaches have produced considerable savings by reducing unplanned outage of machinery, reducing downtime for repair and improving reliability and safety. Condition monitoring is a technique of sens-ing equipment health; operating information and analyzing this information to quantify the condition of equipment.
What is Media Monitoring and How Do You Use it Monitoring: a history of tracking media What is monitoring? Getting started with monitoring The Benefits and Uses of Monitoring Using media monitoring to combat information overload Tools to maximize monitoring and measurement efforts Using media monitoring to develop media lists
There are also four possible examples of themes which could be followed. Each has a set of readings with an introduction to them. This could either act as a prompt to whoever is preaching, or could be read when there is no preacher present, as sometimes happens in our rural groups of churches where each church holds its own service. There is a linked prayer and suggestions for the music .
