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Some machine condition monitoring techniques. 

Some machine condition monitoring techniques. 

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Conference Paper
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Advanced signal processing methods combined with automatic fault detection enable reliable condition monitoring for long periods of continuous operation. Root-mean-square and peak values obtained from vibration signals are useful features for detecting various faults. Unbalance, misalignment, bent shaft, mechanical looseness and some electrical fau...

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... condition monitoring can be based on various techniques (Figure 1) which contain numerous approaches, e.g. vibration condition monitoring can be classified into time and frequency domain techniques (Figure 2). ...

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... In the present day, however, mainly accelerometers are used to form a velocity signal by applying either analogue or numeric integration to its output. On the other hand, a velocity spectrum can be easily calculated directly from an acceleration spectrum [22][23][24] . Vibration velocity is usually measured in the frequency range from 10 Hz to 1000 Hz. ...
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The time derivatives of acceleration offer a great advantage in detecting impact-causing faults at an early stage in condition monitoring applications. Defective rolling bearings and gears are common faults that cause impacts. This article is based on extensive real-world measurements, through which large-scale machines have been studied. Numerous laboratory experiments provide additional insight into the matter. A practical solution for detecting faults with as few features as possible is to measure the root mean square (RMS) velocity according to the standards in the frequency range from 10 Hz to 1000 Hz and the peak value of the second time derivative of acceleration, ie snap. Measuring snap produces good results even when the upper cut-off frequency is as low as 2 kHz or slightly higher. This is valuable information when planning the mounting of accelerometers.
... x and ) 4 ( x . Statistical features are selected by taking into account the faults under consideration (8) . Frequency range of the measurements is important and several features are combined with dimensionless indices and the analysis can be further improved by taking into account nonlinear effects (9) . ...
... The same data and the same test rig have already been used in earlier studies (10) . Root-mean-square (rms) and peak values are most commonly used for these faults: low order derivatives can be compensated by using higher order moments (8) . ...
... More information is obtained by 4 SOL and 8 SOL . The strength of unbalance can be estimated by 8 SOL . (13) There are small changes in the SOL values when the rotation speed increases (Figure 2). ...
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Automatic fault detection with condition indices enables reliable condition monitoring to be combined with process control. Useful information on different faults can be obtained by selecting suitable features from generalised norms, which are defined by the order of derivation, the order of the norm and sample time. The nonlinear scaling based on generalised norms and skewness extends the idea of dimensionless indices to nonlinear systems and provides good results for the automatic generation of condition indices. Condition indices, which are used in the same way as the process measurements in process control, detect differences between normal and faulty conditions and provide an indication of the severity of the faults. Feature specific health indices, which are calculated as ratios of feature values in the reference condition and the faulty case, are used in selecting efficient features. In the multisensor vibration analysis, the number of sensors and features were drastically reduced. The number of features is further reduced by optimal orders for the derivatives and norms. The complexity of the models is simultaneously reduced. For the supporting rolls of a lime kiln, an efficient indication of faulty situations is achieved with two features. All the rolls can be analysed with the same approach throughout the data set. The results of both the applications are consistent with the vibration severity criteria: good, usable, still acceptable, and not acceptable. Three standard deviations obtained for the signal x(4) on three frequency ranges were needed to detect unbalance and bearing faults. The norms based on the signal x(4) provide the best results in all the frequency ranges.
... The central value alternatives are mean, median and mode. In vibration analysis, root mean square (rms) and peak values are the most commonly used features (24) . Dimensionless features are obtained by normalisation. ...
... For these faults, sensitivity of the features increases to some limit with the increasing order of derivative. More examples are presented in (24) . Higher order derivatives provide more sensitive solutions, ie the ratios of features between the faulty and non-faulty cases become higher. ...
... In (29) , the cavitation index is a stress index obtained by scaling the relative value of the norm (43): ) defined by (43), since only the parameters of Figure 1. Combined indices (24) the scaling function will change. Low stress corresponds to value -2 and not allowable stress to value 2. Operating conditions with high stress should be avoided. ...
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Advanced signal processing methods combined with automatic fault detection enable reliable condition monitoring for long periods of continuous operation. Rapid changes in acceleration become emphasised upon derivation of the signal x(2). Higher order derivatives, especially x(4), work very well in the whole range from slowly to very fast rotating rolling bearings. Real order derivatives x(α) provide additional possibilities. If α < 0, we can also talk about the fractional integration of displacement. It has interesting application potential for faults that occur in frequencies below the rotating frequency. The aim of the analysis is to introduce new features whose sensitivity is sufficient to detect faults from the absolute values of the dynamic part of the signals x(α) at an early stage, ie when the faults are still very small. Generalised moments τMαp and norms, ‖τMαp‖p = (τMαp)1/p = [1/NΣi=1N|xi(α)|p]1/p, can be defined by the order of derivation (α), the order of the moment (p) and sample time (τ), where α and p are real numbers. The number of signal values N = τNs, where Ns is the number of signal values taken in a second. The norm ‖τMαp‖p has the same dimensions as the corresponding signals x(α). Both the order of derivation and the order of the moment improve sensitivity to impacts. There are many alternative ways to normalise the moments: absolute mean deviation is an easy solution and normalisation by standard deviation generalises the moments to standardised variables. Dimensionless vibration indices obtained using different orders α and p can be combined in measurement indices. The analysis can be further improved by taking into account non-linear effects by monotonously increasing the scaling functions. Several features can be combined in condition indices and, in some cases, only one feature is needed if the orders p and α are chosen properly.
... The integration of displacement introduced in (7) and complex order derivatives introduced in (10) offer additional possibilities for signal processing. In vibration analysis, root mean square (rms) and peak values are the most commonly used features (11) . Dimensionless features are obtained by normalisation. ...
... A summary of our long experience about the applicability of features is presented in (11) . Some faults, such as unbalance, misalignment, bent shaft and mechanical looseness, can be detected by means of displacement x (0) and velocity x (1) . ...
... Table 1. Examples of signals and features in fault detection (11) Nature of fault Signal Features (4) peak, rms, crest factor, kurtosis, l p norm ...
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A combined signal processing and feature extraction approach can be based on generalised moments and norms, which are defined by the order of derivation (α), the order of the moment (p) and sample time (τ), where α and p are real numbers. The analysis can be further improved by taking into account nonlinear effects by monotonously increasing scaling functions. Several features can be combined in condition indices and, in some cases, only one feature is needed if the orders p and α are chosen properly. The aim of the application analysis is to test new features and whether their sensitivity is sufficient to detect faults from the absolute values of the dynamic part of the signals x(α) at an early stage when the faults are still very small. For faults causing impacts, the sensitivity of the features is clearly improved by higher and real order derivation, and low order norms can be used if the order of derivation is sufficient. Correspondingly, subharmonic vibrations can be amplified by integration. A limit of sensitivity is reached on a certain order α, and on each order α an optimal sensitivity is chosen by the order p. Both the orders can be chosen fairly flexibly from the optimal area. Sample time, which connects the features to the control application, is process-specific. The analysis methods allow the use of lower frequency ranges, and a multisensor approach can also be used. The approach is well suited for rotating process equipment with speeds ranging from very slow to very fast. In this article, the types of slow rotating equipment are a lime kiln, a washer and the scraper of a digester, all from pulp mills. A centrifuge and a turbo compressor are examples of very fast rotating machines. Features can also be combined in stress indices, which react to harmful process conditions. For example, strong cavitation, cavitation-free cases and even short-term cavitation are clearly detected in a Kaplan water turbine. Several features, also from different frequency ranges, can be combined in condition and stress indices.
... The history of fractional integrals and derivatives is discussed in (1) . Feature selection depends very much on the problem (2) . Widely used root mean square (rms) values are important in many applications, but the importance of the peak values increases in slowly rotating machines. ...
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... On the other hand it is well known that the early detection of bearing faults, as well as cavitation can be detected more efficiently with the acceleration signal. Often higher order derivatives provide more sensitive solutions, i.e. the ratios of calculated features between the faulty and nonfaulty cases become higher [7,9,11]. ...
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... Generalised moments were calculated for a specific sample time in [4]. The root mean square (rms) values and peak values are most widely used features, see [5] for more application experiences. Generalised norms, which were introduced in [6], have been used in scaled form in condition indices [7]. ...
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Early detection of fluctuations in operating conditions and fault detection can be done with similar methods. Signal processing is needed for the condition monitoring measurements, and interpolation is some process measurements and especially laboratory analysis. Effective time delays are very important in process data. Feature extraction uses statistical analysis, and the methods can be based on generalised norms and moments. Intelligent condition and stress indices are calculated from these features by nonlinear scaling. The new scaling approach, which also uses the norms and moments, improves sensitivity to small fluctuations. In the condition monitoring cases, the condition indices are consistent with the vibration severity criteria, which originate from VDI 2056. Only one norm was needed in the cavitation analysis, and the resulting index can be used in power control. The same methodology provides good results in detecting fluctuations of flavour ingredients in brewing, in predicting web break sensitivity in paper machines and in intelligent analysers of the process conditions in wastewater treatment. Linguistic equation (LE) models of the normal case suit for detecting fluctuations, and case-based reasoning (CBR) is used if specific case models can be developed. The overall procedure includes following steps: (1) select informative features, (2) scale the features, (3) calculate intelligent indices, and (4) combine indices in models.
... These indices provide useful information on different faults, and even more sensitive solutions can be obtained by selecting suitable features. (1) Generalised moments and norms include many well-known statistical features as special cases and provide compact new features capable of detecting faulty situations (2) . Intelligent models extend the idea of dimensionless indices to nonlinear systems. ...
... . lim Generalised norms introduced in (2) have been used for feature extraction the absolute values of signals velocity ...
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Full-text available
Automatic fault detection with condition indices enables reliable condition monitoring to be combined with process control. Useful information on different faults can be obtained by selecting suitable features. Generalised norms can be defined by the order of derivation, the order of the moment and sample time. These norms have the same dimensions as the corresponding signals. The nonlinear scaling used in the linguistic equation approach extends the idea of dimensionless indices to nonlinear systems. Condition indices are obtained from short samples by means of the scaled values and linear equations. Indices, which are used in the same way as the process measurements in process control, detect differences between normal and faulty conditions and provide an indication of the severity of the faults. The generalised norms represent the norms from the minimum to the maximum in a smoothly increasing way. The new nonlinear scaling methodology based on generalised norms and skewness provides good results for the automatic generation of condition indices. Additional sensitivity is achieved for the values which differ only slightly from the centre values. For cavitation, the new approach provides four levels from cavitation-free to clear cavitation. For the supporting rolls of a lime kiln, it provides an efficient indication of faulty situations. Sensitivity is also improved for small fluctuations. All the supporting rolls can be analysed using the same approach throughout the data set. The results of both the applications are consistent with the vibration severity criteria: good, usable, still acceptable, and not acceptable. Warning and alarm limits can be defined and fault types can also be identified with fuzzy set systems and specialised condition indices.
... In condition monitoring statistical methods have been widely used for investigation, where measured data are time series. Extensive literature is available on diagnostic techniques using RMS, Kurtosis, Crest Factor and histograms and other statistical moments (10,11,12,13) . ...
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This paper presents some aspects concerning the problems of adaptive monitoring systems. Each automatic monitoring system has to be adapted if it is installed in a new environment. Characteristic of solving the monitoring task, the number of fault classes and free parameters in the internal classier are potential switchers to adjust the system. We discuss general problems in the field, such as fault simulation, provide the necessary definitions of different levels of adaptivity, describe the state of the art and give some hints about how the implementation of intelligent data pre-processing can improve the transfer of data from an existing system to a new one. As an application we use the detection of fault in a roller bearing using the derivative x(4) to obtain a higher sensitivity in the monitoring system.
... Vibration indices based on several higher derivatives in different frequency ranges were already introduced in 1992 [2]. Fractional integrals and derivatives are discussed in [7]. Higher and real order derivatives in processing vibration measurements and feature extraction by generalised moments and l p norms have been discussed in [8,9,10,11]. ...
... For bearing faults, displacement and velocity should be replaced by acceleration or higher derivatives. For a very low rotation speed, the rms values are not sensitive for bearing faults, because the effect of few weak impacts is small in the sum (10) where N is a large number. For high rotation speeds, frequent strong impacts affect rms values significantly. ...
Conference Paper
Full-text available
Advanced signal processing methods combined with automatic fault detection enable reliable condition monitoring for long periods of continuous operation. Root-mean-square and peak values obtained from vibration signals are useful features for detecting various faults. Unbalance, misalignment, bent shaft, mechanical looseness and some electrical faults, for example, can be detected using features of displacement and velocity. Higher order derivatives provide additional possibilities for detecting faults that introduce high-frequency vibrations or impacts. Real order derivatives increase the number of signal alternatives. New generalised moments and norms related to lp space have been used for diagnosing faults in a roller contact on a rough surface. Kurtosis provides a strong indication if the order of derivation, α, is at least 4. For peak values, the change is smaller but already starts at α = 3. The generalised moment and norm can be defined by the order of derivation, the order of the moment, p, and sample time, τ. Reliable results can be obtained by relative norms if α and p are in the range between 4 and 6. For the impact of a small scratch, sensitivity is further improved with short sample times, but several sequential samples are required to guarantee the detection of impacts. Then the order p can also be reduced.