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Separating Different Vibration Sources in Complex Fault Detection

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Any attempt to detect different types of machine faults reliably at an early stage requires advanced signal processing methods. It is well known that vibration measurements provide a good basis for condition monitoring and fault detection. The complexity of signal processing techniques depends on the type of fault. In many cases root mean square and peak values are useful features for fault detection. Unbalance, misalignment, bent shaft, mechanical looseness and some electrical faults, for example, can be detected using features of displacement and velocity. Advanced filter settings and higher order derivatives provide additional possibilities if faults cause high frequency vibrations or impacts. This paper presents results from investigations about various faults, e.g. unbalance, misalignment, resonance, cage fault, absence of lubrication in a ball bearing and their combinations. To detect the faults, also higher order derivatives, lp norms and dimensionless measurement index MIT are utilised.
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... The motor and the driven shaft are coupled by means of a claw clutch with a four-tooth elastic element (spider). More information on the test rig can be found in (7,8) , where the same test rig has been used. ...
... After that three fault states were measured, and finally all the faults occurred simultaneously. More information on the testing arrangement can be found in earlier studies by Lahdelma et al. (7) 3. Signal processing This paper mainly discusses the signal processing of acceleration signals by means of derivation, so that the order of derivative can be a complex number. The order of derivative of the vibration signal is very important in fault detection. ...
... Measurements were carried out widely on the test rig ( Figure 1) but signals from only one horizontal accelerometer of bearing 1 and only one rotational frequency (8 Hz) were investigated. Some of these signals have been studied earlier by authors et al. (4,7) . Signal processing, such as derivation and filtering, was performed in the frequency domain and an ideal filter was used for band pass filtering the signals to a frequency range from 5 Hz to 10 kHz. ...
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There is a wide range of signal processing methods in the field of machine condition monitoring, which are used in feature extraction and fault diagnosis. Derivation and integration are very common and important operations in vibration signal processing. Often a signal that has been measured using an accelerometer is integrated to obtain velocity or displacement signal. These commonly used three signals and an infinite number of other signals can be obtained by means of fractional order derivation. Especially in challenging fault and process cases, signals whose order of derivation is a real or complex number could be clearly more sensitive to fault detection than the commonly used signals. This paper discusses the application of complex order derivation to acceleration signals in order to detect various faults at an early stage.
... The motor and the driven shaft are coupled by means of a claw clutch with a four-tooth elastic element (spider). More information on the test rig can be found in (Lahdelma et al., 2011;Lahdelma and Laurila, 2012), where the same test rig has been used. ...
... After that three fault states were measured, and finally all the faults occurred simultaneously. More information on the testing arrangement can be found in earlier studies by Lahdelma et al. (2011) 3. SIGNAL PROCESSING This paper mainly discusses the signal processing of acceleration and sound signals by means of differentiation or derivation, so that the order of derivative can be a complex number. The order of derivative of the vibration signal is very important in fault detection. ...
... Measurements were carried out widely on the test rig (Figure 1) but signals from only one horizontal accelerometer of bearing 1 and sound level meter with only one rotational frequency (8 Hz) were investigated. Some of these signals have been studied earlier by authors et al. (Lahdelma et al., 2011;Lahdelma, 2013, 2014a). Signal processing, such as differentiation and filtering, was performed in the frequency domain and an ideal filter was used for band pass filtering the signals to a frequency range from 5 Hz to 10 kHz. ...
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The reliable condition monitoring of machines and the early detection of faults play an important role in condition-based maintenance. Information on the condition of machines is needed in different forms but finally it has to be in as simple form as possible so that it can be utilised in the decision-making by the maintenance and management personnel. In the field of machine condition monitoring, there is a wide range of signal processing methods, which are used in feature extraction and fault diagnosis. Differentiation and integration are very common and important operations in vibration signal processing. The order of derivative is typically an integer number but it can also be any fractional number. Especially in the challenging fault and process cases, signals whose order of derivative is a real or complex number could be clearly more sensitive in fault detection than the commonly used signals: displacement, velocity and acceleration. In this paper, the application of complex order differentiation of acceleration and sound signals was discussed. With the help of them, it is possible to find good and sensitive new types of methods and indicators for different fault detection situations.
... Excessive misalignment of a claw clutch does not cause any major increase in low-frequency vibrations, which can be detected reliably using displacement and velocity measurements. In this case it is better to use signals and features, which are sensitive to impact-like faults and high frequency vibrations (5) . Therefore, the acceleration signal and higher order derivatives were utilised in this study. ...
... The type of the coupling is ROTEX GS 14, manufactured by KTR. More information about the test rig can be found in (5) , where the same test rig has been used. ...
... Horizontal displacement was performed by means of two dial gauges. More information about the testing arrangement can be found in earlier studies by Lahdelma et al. (5) . ...
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Coupling misalignment is a fairly common fault in rotating machines and causes excessive loads to bearings and other machine components. In general, misalignment and its severity can be detected reliably using vibration measurements. Misalignment is typically detected on the basis of the velocity spectrum. An increase in amplitude at rotational frequency and its harmonics are an indication of misalignment or a bent shaft. However, the behaviour of a claw clutch (jaw coupling) is very different as compared with a conventional elastic coupling. This paper presents the results of research where the detection of claw clutch misalignment is investigated based on acceleration signals. Real order derivatives and lp norms are calculated from the signals. In a test rig, which is used in this study, different degrees of misalignment are generated by moving a motor in the horizontal direction with steps of 0.1 mm. The tests were carried out at five rotational frequencies. Studies show that after a certain level of misalignment, the claw clutch gives rise to impacts that induce high frequency vibrations.
... The motor and the driven shaft are coupled by means of a claw clutch with a four-tooth elastic element (spider). More information on the test rig can be found in (6,7) , where the same test rig has been used. ...
... After that three fault states were measured, and finally all the faults occurred simultaneously. More information on the testing arrangement can be found in earlier studies by Lahdelma et al. (6) . ...
... Vibration signals from all the eight accelerometers in the test rig and six different rotational frequencies in each fault state were investigated in this paper. Some of these signals have been studied earlier by authors et al. (6) in which the signals and spectra under different fault states have been presented more accurately. Here, the signals were examined mostly based on advanced feature calculation. ...
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Reliable machine condition monitoring and the early detection of faults play an important role in asset management. Information on the condition of machines in a clear, simple form is very valuable for the maintenance and management personnel of the company. This kind of information can be obtained by combining features of vibration signals and the desired number of features from other physical measurements into a dimensionless MIT index. The resulting value gives us information on the condition of the machine. The inverse of MIT index is called the SOL health index. This paper presents the results of investigations concerning the detection of many simultaneous faults using vibration and sound measurements. The signals measured have been processed using real order derivation, and effective features have been calculated from these signals. Based on these features, MIT indices have been calculated, which reliably shows if there are problems with the condition of the machine.
... Unstable running of an electric motor [26,62,67] Bearing faults [26,[66][67][68][69]72,[84][85][86][87][88][89][90]93,94,98] Bent shaft [88] Misalignment [88,91,92] Cavitation [95][96][97] Poor lubrication [91] A laboratory experiment is examined in which automatic diagnostics were used with acceleration and jerk signals. The outer race of a rolling bearing contained the faults shown in Table 6 [69,87,94] . ...
... Unstable running of an electric motor [26,62,67] Bearing faults [26,[66][67][68][69]72,[84][85][86][87][88][89][90]93,94,98] Bent shaft [88] Misalignment [88,91,92] Cavitation [95][96][97] Poor lubrication [91] A laboratory experiment is examined in which automatic diagnostics were used with acceleration and jerk signals. The outer race of a rolling bearing contained the faults shown in Table 6 [69,87,94] . ...
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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.
... It is a simple operation, where the DFT is performed first and frequency components in the stop band of the filter are replaced with zeros, and the inverse DFT is applied after. The techniques presented above have been applied before for example in (14,15,16) . ...
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Periodically repeating shocks are a quite common indication of certain defect in machinery. Detecting these shocks in early stage, before the defect is severe enough to cause failure, can provide a huge advantage in maintenance planning. The earliest possible warning of a defect may be highly important, especially in targets where failure can lead to a vast loss of production or a safety risk. Shock-like vibrations are, however, usually rather faint when the defect is a minor one. This simply means that the shocks may often be too low in magnitude to be easily detected. In this paper, we use different techniques based on real order derivative to detect gear defects. Higher real order derivative, discrete Fourier transform, and Hilbert transform are discussed.
... The motor and the driven shaft are coupled by means of a claw clutch with a four-tooth elastic element. More information on the test rig and the original measurements can be found in (31) . We have studied the detection of the offset misalignment of a claw clutch in (32) . ...
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The processing of measured signals plays a very important role when information is needed on a physical phenomenon or machine faults. The differentiation of a signal is one of the basic methods used in many signal processing applications. The order of derivative is typically an integer though it can also be any real or complex number. This paper discusses the application of real and complex order fractional differentiation. The aim is to bring out different applications of these derivatives, especially in the field of condition monitoring.
... Detecting faults is a very common topic, and instructions on this are provided in handbooks (8) . When there are multiple faults, however, the situation is more complicated in this respect, but separating different sources of vibration has proved to be possible (9) . ...
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