Rolling element bearings localized fault diagnosis using signal differencing and median filtration

With the increase complexity of bearings’ processing algorithms and the growing trend of using computationally demanding algorithms, it is advantageous to provide analysts with a simple to use and implement algorithm. In this spirit, this paper combines simple functions to provide machine condition analysts with the capacity to diagnose bearing faults without all the complexity and jargon that comes with existing methods. The paper proposes a simplified surveillance and diagnostic algorithm for diagnosing localized faults in rolling element bearings using measured raw vibration signals. The proposed algorithm is based on analyzing the frequency content obtained from applying a median filter on the squared derivative signal (first or higher derivatives) of the vibration signal. The combination of signal differencing and median filters provides a squared envelope signal, which can be used directly to diagnose faults. Signal differencing gives a measure of jerk forces and lifts the high frequency content of the signal. To select the optimum order of differentiation, Kurtosis and maximum correlated kurtosis (MCK) are proposed. Median filter usage represents a better alternative of normal low pass filtration. This completely suppresses impulses with large magnitudes, which may interfere with the diagnosis. The length of the median filter (odd number 3, 5, 7 etc.) is selected as such to include the first 10 harmonics of the defect frequency. Simulated signals are used to demonstrate the efficiency of the proposed algorithm and give insights into the choices of the differentiation and smoothening orders. The proposed processing algorithm gives a first measure (surveillance) for detecting localized faults in rolling element bearings in a very simple way and can be employed in online learning and diagnosis systems. Results obtained from applying the algorithm on complex vibration signals from two types of gearboxes are compared with a well-established semi-automated technique with good correspondence

Fault Diagnosis of Wind Turbine Gearboxes Using Enhanced Tacholess Order Tracking

Health monitoring of wind turbines is crucial to support their sustainability and economic operation. Gearbox failures contribute significantly to the cost of wind turbine maintenance. These failures are typically monitored by Vibration sensors attached to external housing. Non-stationary vibration is induced by the variable-speed operation of the wind turbine. In this paper, a new technique is introduced to enable the extraction of a speed reference from the vibration signal, which can be utilized to remove speed fluctuations and diagnose faults in the gearbox. High speed variations of the shaft, i.e., tacho signal, have been extracted from the vibration signal of an accelerometer mounted on the casing of the gearbox. The epicyclic mesh frequency has been used to construct the speed reference by an enhanced order tracking technique comprising auto-adjustable phase demodulation (APD). APD is aimed at improving fault detection efficiency by accommodating high speed fluctuations. The effectiveness of the proposed APD performance has been successfully demonstrated by using vibration measurements from commercial in-service wind turbines

Quantifying bearing fault severity using time synchronous averaging jerk energy

Flight control surfaces have the crucial task of allowing the pilot to control the aircraft. Currently, these are generally controlled by hydraulic actuators. As a part of More Electric Aircraft (MAE) trend, some redundant hydraulic actuators are being partially replaced by Electromechanical Actuators (EMA). In order to ensure the ultimate safety of EMA, reliable vibration-based diagnosis capabilities are potentially needed to minimize the catastrophic risk of EMA failure initiated by critical sub-components such as rolling-element bearing. In this paper a new technique to estimate the fault severity of a defective bearing is presented. The technique is based on analyzing the jerk energy gradient of the synchronously averaged fault impact. The averaged signal is extracted by using time synchronous averaging (TSA) triggered by the fault race frequency (FRF) for the detection task and triggered by shaft speed for the quantifying task. FRF is defined, in this work, as the bearing fault frequency of interest divided by the number of rolling elements, which eventually denotes the cage frequency in the case of an outer race fault and the difference between the shaft speed and the cage frequency in the case of an inner race fault. Detailed monitoring of the TSA-based energy is developed for the EMA bearings noting in particular that this can be utilized in other low-speed applications. The effectiveness of the proposed technique is demonstrated and discussed on several seeded faults

Vibration response characterisation and fault-size estimation of spalled ball bearings

Research efforts have increased to investigate the ability to quantify localised bearing faults, ie spalls. These efforts revolve around extending the useful service life of the bearing after the detection of spalls. A number of studies have investigated a linear correlation between the size of spalls and three geometric points that may be recognised in the vibration response: the entry into the spall; the exit from the spall; and a third impact point between the first two. The time difference between these points, calculated using different signal processing techniques, has been widely exploited for quantifying spall size. Currently, there are two main challenges: the first is to enhance the entry point, which typically has weak excitation; the second is to distinguish the impact and the exit points investigated in the literature based on the spall size. However, for practical applications, there is no prior rough estimation of the fault size (ie small or large) and a method is needed for the interpretation of responses. This paper provides insights into the movement of the rolling element within the spall region and shows that the rolling element strongly strikes the bearing races at a minimum of two points. A new technique is then presented to quantify the spall and determine the inherent scaling factor without comparison to any reference data. The technique is based on evaluating two root-mean-square (RMS) energy envelopes, one for the vibration signal and one for a numerical differentiation of this signal. A geometric scaling factor is then used to give a generalised quantification for the small and large spalls. Serviceable estimations of spall size have been achieved for several seeded faults measured on two dissimilar test-rigs provided by the German Aerospace Centre (DLR) and the University of New South Wales (UNSW)

Bearing spall size quantification based on geometric interpretation of vibration envelope energy

Research efforts have increased to investigate the ability to quantify localized bearing faults, i.e., spalls. These efforts revolve around extending the useful service life of the bearing after the detection of spalls. A number of studies have investigated a linear correlation between the size of spalls and three geometric points that may be recognized in the vibration response: the entry into the spall, the exit from the spall, and a third impact point between the first two. The time difference between these points, calculated using different signal processing techniques, has been widely exploited for quantifying spall size. Currently, there are two main challenges: the first is to enhance the entry point, which typically has weak excitation; the second is to distinguish the impact and the exit points investigated in the literature based on the spall size. However, for practical applications, there is no prior rough estimation of the fault size (i.e., small or large), and a method is needed for interpretation of responses. This paper provides insights into the movement of the rolling element (e.g. ball) within the spall region and shows that the rolling element strongly strikes the bearing races at a minimum of two points. Then, a new technique is presented to quantify the spall and determine the inherent scaling factor without comparison to any reference data. The technique is based on evaluating two root-mean-square (RMS) energy envelopes, one for the vibration signal and one for a numerical differentiation of this signal. A geometric scaling factor is then used to give a generalized quantification for the small and large spalls. Serviceable estimations of spall size have been achieved for several seeded faults measured on two dissimilar test rigs provided by German Aerospace Centre (DLR) and the University of New South Wales (UNSW)

Automated vibration-based fault size estimation for ball bearings using Savitzky–Golay differentiators

Vibration-based fault diagnosis has been utilized as a reliable method for identifying ball bearings health since the 1970s. Recently, there has been an increased research effort to develop methods for fault quantification with the aim of estimating the fault size to allow the service life of a ball bearing to be extended beyond the detection stage. These studies have shown that the vibration signal from a localized spall (e.g. fatigue defect) in a ball bearing exhibits features corresponding to two main events, namely, the entry into and the exit from the spall. The time span between these two events is correlated with the spall size. Studies have shown that the entry into the spall is the more challenging event to identify, which often requires extensive signal processing techniques. This paper introduces an automated vibration-based technique for estimating the size of a spall in a ball bearing under axial loading conditions similar to those of linear electro-mechanical actuators. This technique is based on the extraction of the entry/exit events from the vibrational jerk, which are numerically determined from accelerometer data. The differentiation of the acceleration data to estimate jerk signal is performed using a variant of Savitzky–Golay (SG) differentiators, which provide enhancement for the detection of the entry and exit points. Sensible spall size estimations have been achieved for 24 different scenarios of fault sizes, rotor speeds and loads measured on a test rig provided by DLR (German Aerospace Center)

Diagnostics, prognostics and fault simulation for rolling element bearings

Vibration signals generated from spalled elements in rolling element bearings (REBs) are investigated in this thesis.A novel signal-processing algorithm to diagnose localized faults in rolling element bearings has been developed and tested on a variety of signals. The algorithm is based on Spectral Kurtosis (SK), which has special qualities for detecting REBs faults. The algorithm includes three steps. It starts by pre-whitening the signal's power spectral density using an autoregressive (AR) model. The impulses, which are contained in the residual of the AR model, are then enhanced using the minimum entropy deconvolution (MED) technique, which effectively deconvolves the effect of the transmission path and clarifies the impulses. Finally the output of the MED filter is decomposed using complex Morlet wavelets and the SK is calculated to select the best filter for the envelope analysis. Results show the superiority of the developed algorithm and its effectiveness in extracting fault features from the raw vibration signal.The problem of modelling the vibration signals from a spalled bearing in a gearbox environment is discussed. This problem has been addressed through the incorporation of a time varying, non-linear stiffness bearing model into a previously developed gear model. It has the new capacity of modeling localized faults and extended faults in the different components of the bearing. The simulated signals were found to have the same basic characteristics as measured signals, and moreover were found to have a characteristic seen in the measured signals, and also referred to in the literature, of double pulses corresponding to entry into and exit from a localized fault, which could be made more evident by the MED technique. The simulation model is useful for producing typical fault signals from gearboxes to test new diagnostic algorithms, and also prognostic algorithms.The thesis provides two main tools (SK algorithm and the gear bearing simulation model), which could be effectively employed to develop a successful prognostic model