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  • 1.
    Adolfsson, Stefan
    et al.
    Luleå tekniska universitet.
    Ericsson, Klas
    Luleå tekniska universitet.
    Grennberg, Anders
    Automatic detection of burn-through in GMA welding using a parametric model1996In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 10, no 5, p. 633-651Article in journal (Refereed)
    Abstract [en]

    This paper addresses the problem of automatic detection of burn-through in weld joints. Gas metal arc (GMA) welding with pulsed current is used, and welding voltage and current are recorded. As short-circuitings are common between the welding electrode and the work piece during burn-through, a short-circuit detector is developed to detect these events. To detect another specific characteristic of burn-through - this detector is combined with a square-law detector. This second detector is based on a non-linear modification of an autoregressive model with extra input (ARX-model) of the welding process. The results obtained from this compound detector indicate that it is possible to detect burn-through in the welds automatically. The work also indicates that it is possible to design an on-line monitoring system for robotic GMA welding.

  • 2. Ericsson, Stefan
    et al.
    Grip, Niklas
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    Johansson, Elin
    Luleå tekniska universitet.
    Persson, Lars-Erik
    Sjöberg, Ronny
    Nåiden Teknik AB.
    Strömberg, Jan-Olov
    Department of Mathematics/NADA, Royal Institute of Technology.
    Towards automatic detection of local bearing defects in rotating machines2005In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 19, no 3, p. 509-535Article in journal (Refereed)
    Abstract [en]

    In this paper we derive and compare several different vibration analysis techniques for automatic detection of local defects in bearings. Based on a signal model and a discussion on to what extent a good bearing monitoring method should trust it, we present several analysis tools for bearing condition monitoring and conclude that wavelets are especially well suited for this task. Then we describe a large-scale evaluation of several different automatic bearing monitoring methods using 103 laboratory and industrial environment test signals for which the true condition of the bearing is known from visual inspection. We describe the four best performing methods in detail (two wavelet-based, and two based on envelope and periodisation techniques). In our basic implementation, without using historical data or adapting the methods to (roughly) known machine or signal parameters, the four best methods had 9–13% error rate and are all good candidates for further fine-tuning and optimisation. Especially for the wavelet-based methods, there are several potentially performance improving additions, which we finally summarise into a guiding list of suggestion.

  • 3.
    Georgoulas, Georgios
    et al.
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus.
    Karvelis, Petros
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus.
    Loutas, Theodoros H.
    Applied Mechanics Lab, Department of Mechanical Engineering and Aeronautics, University of Patras.
    Stylios, Chrysostomos D.
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, TEI of Epirus, Artas, Kostakioi.
    Rolling element bearings diagnostics using the Symbolic Aggregate approXimation2015In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 60, p. 229-242Article in journal (Refereed)
    Abstract [en]

    Rolling element bearings are a very critical component in various engineering assets. Therefore it is of paramount importance the detection of possible faults, especially at an early stage, that may lead to unexpected interruptions of the production or worse, to severe accidents. This research work introduces a novel, in the field of bearing fault detection, method for the extraction of diagnostic representations of vibration recordings using the Symbolic Aggregate approXimation (SAX) framework and the related intelligent icons representation. SAX essentially transforms the original real valued time-series into a discrete one, which is then represented by a simple histogram form summarizing the occurrence of the chosen symbols/words. Vibration signals from healthy bearings and bearings with three different fault locations and with three different severity levels, as well as loading conditions, are analyzed. Considering the diagnostic problem as a classification one, the analyzed vibration signals and the resulting feature vectors feed simple classifiers achieving remarkably high classification accuracies. Moreover a sliding window scheme combined with a simple majority voting filter further increases the reliability and robustness of the diagnostic method. The results encourage the potential use of the proposed methodology for the diagnosis of bearing faults

  • 4.
    Georgoulas, Georgios
    et al.
    Department of Informatics and Telecommunications Technology, Technological Educational Institute of Epirus.
    Loutas, Theodoros H.
    bDepartment of Mechanical Engineering and Aeronautics, University of Patras.
    Stylios, Chrysostomos D.
    Department of Informatics and Telecommunications Technology, Technological Educational Institute of Epirus.
    Kostopoulos, V.
    Department of Mechanical Engineering and Aeronautics, University of Patras.
    Bearing fault detection based on hybrid ensemble detector and empirical mode decomposition2013In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 41, no 1-2, p. 510-525Article in journal (Refereed)
    Abstract [en]

    Aiming at more efficient fault diagnosis, this research work presents an integrated anomaly detection approach for seeded bearing faults. Vibration signals from normal bearings and bearings with three different fault locations, as well as different fault sizes and loading conditions are examined. The Empirical Mode Decomposition and the Hilbert Huang transform are employed for the extraction of a compact feature set. Then, a hybrid ensemble detector is trained using data coming only from the normal bearings and it is successfully applied for the detection of any deviation from the normal condition. The results prove the potential use of the proposed scheme as a first stage of an alarm signalling system for the detection of bearing faults irrespective of their loading condition.

  • 5.
    Grip, Niklas
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mathematical Science.
    Sabourova, Natalia
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Structural and Fire Engineering.
    Tu, Yongming
    School of Civil Engineering, Southeast University, Nanjing.
    Sensitivity-based model updating for structural damage identification using total variation regularization2017In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 84, no A, p. 365-383Article in journal (Refereed)
    Abstract [en]

    Sensitivity-based Finite Element Model Updating (FEMU) is one of the widely accepted techniques used for damage identification in structures. FEMU can be formulated as a numerical optimization problem and solved iteratively making automatic updating of the unknown model parameters by minimizing the difference between measured and analytical structural properties. However, in the presence of noise in the measurements, the updating results are usually prone to errors. This is mathematically described as instability of the damage identification as an inverse problem. One way to resolve this problem is by using regularization. In this paper, we compare a well established interpolation-based regularization method against methods based on the minimization of the total variation of the unknown model parameters. These are new regularization methods for structural damage identification. We investigate how using Huber and pseudo Huber functions in the definition of total variation affects important properties of the methods. For instance, for well-localized damages the results show a clear advantage of the total variation based regularization in terms of the identified location and severity of damage compared with the interpolation-based solution.For a practical test of the proposed method we use a reinforced concrete plate. Measurements and analysis were performed first on an undamaged plate, and then repeated after applying four different degrees of damage.

  • 6.
    Johnsson, Roger
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Cylinder pressure reconstruction based on complex radial basis function networks from vibration and speed signals2006In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 20, no 8, p. 1923-1940Article in journal (Refereed)
    Abstract [en]

    Methods to measure and monitor the cylinder pressure in internal combustion engines can contribute to reduced fuel consumption, noise and exhaust emissions. As direct measurements of the cylinder pressure are expensive and not suitable for measurements in vehicles on the road indirect methods which measure cylinder pressure have great potential value. In this paper, a non-linear model based on complex radial basis function (RBF) networks is proposed for the reconstruction of in-cylinder pressure pulse waveforms. Input to the network is the Fourier transforms of both engine structure vibration and crankshaft speed fluctuation. The primary reason for the use of Fourier transforms is that different frequency regions of the signals are used for the reconstruction process. This approach also makes it easier to reduce the amount of information that is used as input to the RBF network. The complex RBF network was applied to measurements from a 6-cylinder ethanol powered diesel engine over a wide range of running conditions. Prediction accuracy was validated by comparing a number of parameters between the measured and predicted cylinder pressure waveform such as maximum pressure, maximum rate of pressure rise and indicated mean effective pressure. The performance of the network was also evaluated for a number of untrained running conditions that differ both in speed and load from the trained ones. The results for the validation set were comparable to the trained conditions.

  • 7.
    Josefsson, A.
    et al.
    Blekinge Institute of Technology.
    Magnevall, M.
    Blekinge Institute of Technology.
    Ahlin, K.
    Blekinge Institute of Technology.
    Broman, Göran
    Luleå University of Technology, Department of Business Administration, Technology and Social Sciences, Innovation and Design.
    Spatial location identification of structural nonlinearities from random data2012In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 27, p. 410-418Article in journal (Refereed)
    Abstract [en]

    With growing demands on product performance and growing complexity of engineering structures, efficient tools for analyzing their dynamic behavior are essential. Linear techniques are well developed and often utilized. However, sometimes the errors due to linearization are too large to be acceptable, making it necessary to take nonlinear effects into account. In many practical applications it is common and reasonable to assume that the nonlinearities are highly local and thus only affect a limited set of spatial coordinates.The purpose of this paper is to present an approach to finding the spatial location of nonlinearities from measurement data, as this may not always be known beforehand. This information can be used to separate the underlying linear system from the nonlinear parts and create mathematical models for efficient parameter estimation and simulation.The presented approach builds on the reverse-path methodology and utilizes the coherence functions to determine the location of nonlinear elements. A systematic search with Multiple Input/Single Output models is conducted in order to find the nonlinear functions that best describe the nonlinear restoring forces. The obtained results indicate that the presented approach works well for identifying the location of local nonlinearities in structures. It is verified by simulation data from a cantilever beam model with two local nonlinearities and experimental data from a T-beam experimental set-up with a single local nonlinearity. A possible drawback is that a relatively large amount of data is needed. Advantages of the approach are that it only needs a single excitation point that response data at varying force amplitudes is not needed and that no prior information about the underlying linear system is needed.

  • 8.
    Martin del Campo Barraza, Sergio
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Sandin, Fredrik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Strömbergsson, Daniel
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Machine Elements.
    A dictionary learning approach to monitoring of wind turbine drivetrain bearings2017In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216Article in journal (Other academic)
    Abstract [en]

    Condition monitoring and predictive maintenance are central for efficient operation of wind farms due to the challenging operating conditions, rapid technology development and high number of aging wind turbines. In particular, preventive maintenance planning requires early detection of faults with few false positives. This is a challenging problem due to the complex and weak signatures of some faults, in particular of faults occurring in some of the drivetrain bearings. Here, we investigate recently proposed condition monitoring methods based on unsupervised dictionary learning using vibration data recorded from three wind turbines over about four years of operation, thereby contributing novel test results based on real world data. Results of former studies addressing condition--monitoring tasks using dictionary learning indicate that unsupervised feature learning is useful for diagnosis and anomaly detection purposes. However, these studies are based on data from test rigs operating under controlled conditions. Furthermore, most former studies focus on classification tasks using relatively small sets of labeled data, which are useful for quantitative method comparisons but gives little information about how useful these approaches are in practice. In this study dictionaries are learned from gearbox vibrations in three different turbines known to be in healthy conditions, and the dictionaries are subsequently propagated over a few years of monitoring data when faults are known to occur. We calculate the dictionary distance between the initial and propagated dictionaries and find time periods of abnormal dictionary adaptation starting six months before a drivetrain bearing replacement and one year before the resulting gearbox replacement. When repeating that experiment with a dictionary that initially is learned from the vibration of another type of rotating machine, the corresponding difference of dictionary distances is three times lower and do not appear abnormal. We also investigate the distance between dictionaries learned from geographically nearby turbines of the same type in healthy conditions and find that the features learned are similar, and that a dictionary learned from one turbine can be useful for monitoring of another similar turbine.

  • 9.
    Mishra, Madhav
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Odelius, Johan
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Thaduri, Adithya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Nissen, Arne
    Trafikverket, Luleå.
    Rantatalo, Matti
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Particle filter-based prognostic approach for railway track geometry2017In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 96, p. 226-238Article in journal (Refereed)
    Abstract [en]

    Track degradation of ballasted railway track systems has to be measured on a regular basis, and these tracks must be maintained by tamping. Tamping aims to restore the geometry to its original shape to ensure an efficient, comfortable and safe transportation system. To minimize the disturbance introduced by tamping, this action has to be planned in advance. Track degradation forecasts derived from regression methods are used to predict when the standard deviation of a specific track section will exceed a predefined maintenance or safety limit. This paper proposes a particle filter-based prognostic approach for railway track degradation; this approach is demonstrated by examining different railway switches. The standard deviation of the longitudinal track degradation is studied, and forecasts of the maintenance limit intersection are derived. The particle filter-based prognostic results are compared with the standard regression method results for four railway switches, and the particle filter method shows similar or better result for the four cases. For longer prediction times, the error of the proposed method is equal to or smaller than that of the regression method. The main advantage of the particle filter-based prognostic approach is its ability to generate a probabilistic result based on input parameters with uncertainties. The distributions of the input parameters propagate through the filter, and the remaining useful life is presented using a particle distribution.

  • 10.
    Mohammed, Omar D.
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Rantatalo, Matti
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Dynamic Response and Time-Frequency Analysis for Gear Tooth Crack Detection2016In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 66-67, p. 612-624Article in journal (Refereed)
    Abstract [en]

    Vibration health monitoring is a non-destructive technique which can be applied to detect cracks propagating in gear teeth. This paper studies gear tooth crack detection by investigating the natural frequencies and by performing time-frequency analysis of a 6 DOF dynamic gear model. The gear mesh stiffness used in the model was calculated analytically for different cases of crack sizes. The frequency response functions (FRFs) of the model were derived for healthy and faulty cases and dynamic simulation was performed to obtain the time signal responses. A new approach involving a short-time Fourier transform (STFT) was applied where a fast Fourier transform (FFT) was calculated for successive blocks with different sizes corresponding to the time segments of the varying gear mesh stiffness. The relationship between the different crack sizes and the mesh-stiffness-dependent eigenfrequencies was studied in order to detect the tooth crack and to estimate its size.

  • 11.
    Mohammed, Omar D.
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Rantatalo, Matti
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Aidanpää, Jan-Olov
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Dynamic modelling of a one-stage spur gear system and vibration-based tooth crack detection analysis2015In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 54, no 1, p. 293-305Article in journal (Refereed)
    Abstract [en]

    For the purpose of simulation and vibration-based condition monitoring of a geared system, it is important to model the system with an appropriate number of degrees of freedom (DOF). In earlier papers several models were suggested and it is therefore of interest to evaluate their limitations. In the present study a 12 DOF gear dynamic model including a gyroscopic effect was developed and the equations of motions were derived. A one-stage reduction gear was modelled using three different dynamic models (with 6, 8 and 8 reduced to 6 DOF), as well as thedeveloped model (with 12 DOF), which is referred as the fourth model in this paper. The time-varying mesh stiffness was calculated, and dynamic simulation was then performed for different crack sizes. Time domain scalar indicators (the RMS, kurtosis and the crest factor) were applied for fault detection analysis. The results of the first model showa clearly visible difference from those of the other studied models, which were made more realistic by including two more DOF to describe the motor and load. Both the symmetric and the asymmetric disc cases were studied using the fourth model. In the case of disc symmetry, the results of the obtained response are close to those obtained from both the second and third models. Furthermore, the second model showed a slight influence from inter-tooth friction, andtherefore the third model is adequate for simulating the pinion’s y-displacement in the case of the symmetric disc. In the case of the asymmetric disc, the results deviate from those obtained in the symmetric case. Therefore, for simulating the pinion’s y-displacement, the fourth model can be considered for more accurate modelling in the case of the asymmetric disc.

  • 12.
    Mohammed, Omar D.
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Rantatalo, Matti
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Aidanpää, Jan-Olov
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Mechanics of Solid Materials.
    Kumar, Uday
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Vibration signal analysis for gear fault diagnosis with various crack progression scenarios2013In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 41, no 1-2, p. 176-195Article in journal (Refereed)
    Abstract [en]

    There are different analytical scenarios assumed for crack propagation in the gear tooth root. This paper presents an investigation of the performance of statistical fault detection indicators (the RMS and kurtosis) for three different series of crack propagation scenarios, to compare these scenarios from a fault diagnostics point of view. These scenarios imply different forms of cracks with propagation by a certain step of crack depth. The 1st scenario assumes a crack being extended through the whole tooth width with a uniform crack depth distribution, while the 2nd scenario assumes the crack being extended through the whole tooth width with a parabolic crack depth distribution, and finally in the 3rd scenario the crack is assumed to be propagating in both the depth and the length directions simultaneously. The time-varying gear mesh stiffness has been investigated using the program code developed in the present research, and the crack propagation can be modelled with any of the presented crack propagation scenarios. Dynamic simulation has been performed to obtain the residual signals of all the studied cases for each crack propagation scenario. The comparison of the statistical indicators applied to the residual signals shows that in the 1st scenario the faults are most easily detectable, since in this scenario there is a change in the indicators implying a dramatic decrease in the gear mesh stiffness. The fault detection in the 2nd scenario is more difficult, as the crack propagates with no significant reflection on the mesh stiffness loss. The 3rd proposed scenario should receive more attention in research because it could occur in reality in case of non-uniform load distribution. However, with this scenario it is difficult to perform early fault detection, since there is a very slight change in the statistical indicators at the beginning of the crack propagation. After which, these indicators show a significant change when the crack grows deeper which implies a serious crack propagation condition.

  • 13.
    Tatar, Kourosh
    et al.
    Division of Mechanical Engineering, University of Gävle.
    Gren, Per
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.
    Estimation of the in-plane vibrations of a rotating spindle, using out-of-plane laser vibrometry measurements2016In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 72-73, p. 660-666Article in journal (Refereed)
    Abstract [en]

    A method for estimating the in-plane vibrations of a rotating spindle using out-of-plane laser vibrometry measurements is described. This method enables the possibility to obtain the two orthogonal radial vibration components of a rotating spindle. The method uses the fact that the laser vibrometer signal is a total surface velocity of the measurement point in the laser direction.Measurements are conducted on a rotating milling machine spindle. The spindle is excited in a controlled manner by an active magnetic bearing and the response is measured by laser vibrometer in one of the two orthogonal directions and inductive displacement sensors in two orthogonal directions simultaneously. The work shows how the laser vibrometry crosstalk can be used for resolving the in-plane vibration component, that is the vibrations in the laser vibrometer cross direction. The result is compared to independent measurement signals from the displacement sensors.The measurement method can be used for vibration measurements on rotating parts, for example, where there is lack of space for orthogonal measurements.

  • 14.
    Tatar, Kourosh
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.
    Rantatalo, Matti
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Gren, Per
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Fluid and Experimental Mechanics.
    Laser vibrometry measurements of an optically smooth rotating spindle2007In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 21, no 4, p. 1739-1745Article in journal (Refereed)
    Abstract [en]

    Laser doppler vibrometry (LDV) is a well-established non-contact method, commonly used for vibration measurements on static objects. However, the method has limitations when applied to rotating objects. The LDV signal will contain periodically repeated speckle noise and a mix of vibration velocity components. In this paper, the crosstalk between vibration velocity components in laser vibrometry measurements of a rotating dummy tool in a milling machine spindle is studied. The spindle is excited by an active magnetic bearing (AMB) and the response is measured by LDV in one direction and inductive displacement sensors in two orthogonal directions simultaneously. The work shows how the LDV crosstalk problem can be avoided if the measurement surface is optically smooth, hence the LDV technique can be used when measuring spindle dynamics.

  • 15.
    Zeng, Yigen
    Luleå tekniska universitet.
    Optimisation of vibration sensor location for an industrial ball mill1994In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 8, no 4, p. 475-482Article in journal (Refereed)
    Abstract [en]

    Ball mills play an important role in both energy consumption and metal wear in mineral processing plants. To maintain high operating efficiency, the material transportation inside the tumbling body has to be monitored in time. It is known that the vibration signal pattern varies corresponding to the operating state of the mill. Besides the basic vibration signature from the rotary drum and machine assembly, the tumbling of steel balls and the material are the major vibration sources. Since the steel balls and the material are unevenly distributed along the rotating axis the vibration sources are spread widely. The location of the vibration sensor has to be optimised to obtain representative signals for the process. Nine locations on the trunnion bearings and the bearing for the pinion axis have been investigated to select the best place for situating a vibration sensor. The vibration signal was picked up by an accelerometer in the form of time-domain waveform, which was firstly recorded by a DAT deck and then digitised by an oscilloscope. The digital signal processing and system identification were performed using software specially developed for an IBM compatible personal computer. The power spectra from different locations were studied and one best sensor location was recommended for picking up a representative signal from the ball mill. More sensors on different bearings are required for mapping the whole picture of the milling state.

  • 16. Zeng, Yigen
    et al.
    Forssberg, Eric
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Sustainable Process Engineering.
    Application of vibration signal measurement for monitoring grinding parameters1994In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 8, no 6, p. 703-713Article in journal (Refereed)
    Abstract [en]

    Vibration signal measurements are discussed for monitoring operating parameters in a laboratory-scale ball mill. The vibration signal was first picked up with an accelerometer, amplified by a vibrometer and then transmitted to a DAT recorder during the entire testing period. The signal on the DAT recorder was resampled and converted into IBM compatible personal computer readable data format using a digital oscilloscope. The vibration signal analyses included rms estimation, power spectral estimation, waterfall plot, principal component analysis and stepwise multiple regression analysis. Clear differences in the rms and the spectra are found for different grinding conditions. Three principal components described about 95% of the total variation in the spectra. Each principal component was related mainly to one to three major frequency bands. Close correlation was found between the vibration signal and grinding parameters. Therefore, an alternative method can be developed for monitoring the operating parameters in a ball mill.

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