Spectral analysis

Due to the nature of rotating machines analysing the frequency spectrum of measured time series variables can provide additional information that can be used to indicate the presence of a fault condition. The time series variables are converted to frequency data by applying fast Fourier transforms (FFT) to the data. Vibration, flux, acoustic and current spectra have all been proven to be sensitive to one or more fault types [17], [28], [32], [33].

Depending on the type and location of the fault a ‘characteristic frequency’ can be calculated for a given running speed; examples of bearing [24], air-gap [24], rotor [34], and stator fault [28] detection using spectral components can be found in the literature.

Mechanical faults such as eccentric air-gap and bearing defects cause a change in the forces on the shaft and rotor during rotation, for example, if a defect occurs on the outer race of the bearing a repeated impact will occur as each rolling element within the bearing interacts with the spall or pit in the raceway [35]. This leads to a periodic variation in load torque, the frequency of which is dependent on the location of the fault be that inner race, outer race, cage or rolling element. The characteristic frequencies can be calculated for several different fault types and, as an example, the equations used to calculate bearing related characteristic frequencies in the vibration spectrum are given below [29]:

{ } _(2.1)

24

{ } _(2.3)

{ ( ) } _(2.4)

where is the cage fault frequency, the inner raceway fault frequency, is the rotational frequency of the inner race with respect to the outer race, the outer raceway fault frequency, the ball fault frequency, the ball diameter,

the pitch diameter, the number of rolling elements and the contact angle between the ball and raceway. Electrical faults, such as stator winding issues or broken rotor bars, can also be detected via vibration spectrum since these factors cause an asymmetry in the machine fluxes which causes unbalanced magnetic forces across the air-gap of the machine which in turn translate into vibrations that propagate through the machine [28].

In addition to causing variations in machine vibration the four major fault types also effect the flux and current of the machine and can therefore be detected using the spectra of these variables. In terms of electrical faults, both the stator and rotor faults lead to a change in the electrical parameters of the machine (inductance and resistance) and therefore faults in these areas can be readily detected in the current or flux spectrum via predictable characteristic frequencies in a similar manner to the vibration based methods [29], [26], [36]. The effect of mechanical faults can also be observed in the current spectrum due to the air-gap modulation that takes place under these conditions (air-gap eccentricity or bearing faults). Air-gap eccentricity faults cause a periodic variation in the air-gap between the rotor and stator which is dependent on the type of eccentricity that is present; static eccentricity, dynamic eccentricity or a combination of the two. The air-gap variation during shaft rotation causes a non-

25

uniform airgap permeance between stator and rotor which modulates the flux density across the air-gap [37]. This variation in flux density goes on to alter the current flowing in each of the phase windings. In fact, any vibrations and imbalances that are present in the motor will modulate the rotating flux to some extent and lead to a modulated current signal [27].

Thus by converting current signals into the frequency domain a number of frequency components relating to each of the four major fault types can be detected. This has led to the development of one of the most popular IM FDD techniques; Motor Current Signature Analysis (MCSA) [2], [6], [38], [39]. This is a specific type of spectrum (signature) analysis which is focussed specifically on frequency components in the line currents supplying the motor. This method has been proved successful in industry and case studies can be found in the literature proving the accuracy of this technique in detecting a range of faults [27], [40].

The studies into MCSA indicate that any change in the operating characteristics of the motor will be reflected in the motor current signal. It is for this reason that MCSA can offer the same detection capabilities as vibration analysis. Traditionally, spectral components of a signal are identified using the Fast Fourier Transform (FFT). This method works well when the motor is in steady- state. Case studies indicate that it has been possible to detect broken rotor bars, air-gap eccentricity and shorted turns in the stator using FFT of the stator current [39], [31], [38]. Pre-processing can be used to increase the robustness of an MCSA detection algorithm. Pre-processing usually involves filtering out information from the current signal that does not aid the detection of certain

26

faults [18]. This leaves behind only the components (or features) of the signal that relate to the fault, making the algorithm more robust.

One drawback of signature analysis is that the motor operating speed and detailed knowledge of the machine’s construction is required for most of the fault frequency calculations. Another consideration when using FFT is that the motor needs to be sufficiently loaded and operating in steady-state in order for fault frequencies to be reliably detected. This is problematic for machines that may be lightly loaded or operate transiently (e.g. wind turbines) [41]. When an induction machine is unloaded or lightly loaded the motor slip is small causing fault frequencies to be masked by the supply frequency or supply frequency harmonics [42], [43]. Additionally, the FFT transform sums frequency components over the entire time period therefore there is no indication of which frequencies were present at a certain moment in time. This makes FFT unsuitable for the spectral analysis of transients, e.g. start-up or variable loads. Motor vibration spectra are also used heavily in the field of induction motor monitoring and when compared to current analysis often provides more reliable data from which FDD activities can be performed. Bearing defects will generally appear clearly in the vibration spectrum [44] whereas they may have very little impact on the current spectrum [45]; thus, for the bearing fault, vibration spectrum based detection is often preferred [24], [32]. The difficulties in obtaining information on bearing faults from the current spectrum is perhaps highlighted by a review of MCSA applications [36] indicating that despite the fact there is considerable research interest in the area of current signature analysis very few of these papers feature bearing fault detection. Research into the area of MCSA for bearing faults continues but vibration spectral analysis

27

continues to find the most widespread use due to the improved sensitivity of this method to bearing faults [29].

Finally, the rotational speed spectrum has been used for IM FDD. Angular speed variations have been proven to be sensitive to broken rotor bar faults [31]. The rotor fault leads to a pulsating torque due to flux differences dependent on rotor angular position. The frequency spectra of these angular speed differences are reported to indicate broken rotor bar faults with accuracy greater than that of MCSA. To date, this method has only been applied to rotor bar faults.