Modeling of Bearing Dynamics Using Combined EFEM-DEM Method

The objective of this investigation was to develop a 3D dynamic model to study the rotorbearing- housing system. To achieve the objective, an existing dynamic bearing model (DBM) was combined with a flexible bearing housing model and a flexible rotor model. The DBM is based on the discrete element method (DEM), in which all bearing components are assumed to be rigid and have six degrees of freedom. The 3D explicit finite element method (EFEM) was used to develop the flexible housing and rotor models. To couple the bearing outer race (OR) with housing, a novel algorithm was developed to detect contact conditions between the housing support and OR and then calculate contact forces based on the penalty method. A study of housing support geometry demonstrates that bearing support plays a large role on the dynamic performance of the bearing. Motion of bearing outer race is closely related to the geometry and deformation of the housing. The effect of elastomeric bushing support on bearing dynamics was also studied and then compared to the bearing housings made with linear-elastic material. The EFEM was used to model a cylindrical elastomeric bushing, which was then coupled with DBM. Constitutive relationship for the elastomeric material is based on the Arruda-Boyce model combined which uses a generalized Maxwell-element model to capture both hyperelastic and viscoelastic behaviors of the material. Comparison between the two types of housings illustrated that elastomeric materials as expected have large damping to reduce vibration and absorb energy which leads to a reduction in ball-race contact forces and friction. It was also shown that a desired bushing support performance can be achieved by varying bushing geometry. Simulations using the combined EFEM bushing and DBM model demonstrated that the elastomeric bushing provides better compliance to bearing misalignment as compared to a commonly used rigid support model. Modeling with a bearing surface dent showed that vibrations due to surface abnormalities can be significantly reduced using elastomeric bushing support. It has also been shown that choosing a proper bushing is an efficient way to tuning bushing vibration frequencies. The model was further developed to study the effects of rotor and support flexibilities on the performance of rotor-bearing-housing system. The system is composed of a flexible rotor and two supporting deep-groove ball bearings mounted in flexible bearing housings. In order to combine the dynamic bearing model with finite element rotor and support system, new contact algorithms were developed for the interactions between the various components in the system. The Total Lagrangian formulation approach was applied to decrease the computational effort needed for modeling the rotor-bearing-housing system. The combined model was then used to investigate the effects of bearing clearances and housing clearances. It was found that as the rotor is deformed due to external loading, the clearances have a significant impact on the bearing varying compliance motion and reaction moments. Results also show that the deformation of the flexible housing depends on the total force and moment generated within the bearing due to rotor deformation. The first critical speed of rotor was simulated to investigate the unbalance response of the rotor-bearing system. It was demonstrated that rotor critical speed has a significant effect on inner race displacement and reaction moment generated at bearing location. The dynamic behavior of the cage in a ball bearing was studied using experimental and analytical investigations. For the experimental investigation, a wireless sensor telemeter system was designed and developed to monitor the cage motions. The sensor, which was integrated on the bearing cage, is comprised of a commercially-available capacitor-inductor (LC) circuit. The LC circuit on the rotating cage was coupled to a transceiver which was stationary and positioned in close proximity to the cage. In order to achieve the objective of the analytical investigation, the explicit finite element method (EFEM) was used to simulate the bearing cage. The EFEM cage model was then combined with the dynamic bearing model to simulate the cage motion during operation. The results from the experimental measurement using the telemeter were then compared with the analytical modeling. The developed telemeter demonstrated the capability of the cage telemeter in detecting various bearing frequencies. These include: the cage frequency, shaft frequency, and ball pass frequency on outer race (BPFO) which was introduced by creating a spall on bearing outer race. Compared to standard accelerometers which are commonly used to measure vibrations on the bearing housing, the cage telemeter has shown advantage in sensing cage motions and detecting bearing defect regardless of the location of the damage. Analytical simulation using the EFEM cage model correlated well with the experimental results and provided more insight into the bearing cage dynamics

Fault Detection Analysis in Ball Bearings using Machine Learning Techniques

The Bearing element is very essential component of any rotating equipment. Any defect in the bearings lead to instable performance of the machinery. To avoid such malfunction and breakdown of the machinery equipment due to misalignment is review critically in this research paper and various machine learning techniques to tackle the issue is highlighted. This review article finds the basis for developing an effective system in order to reduce the breakdown of machinery or equipment. Conventional Machine Learning methods, like Artificial neural network, Decision Tree, Random Forest, Support Vector Machines(SVM) have been applied to detecting categorizing fault, while the application of Deep Learning methods has ignited great interest in the industry

Fault Detection Analysis in Ball Bearings using Machine Learning Techniques

The Bearing element is very essential component of any rotating equipment. Any defect in the bearings lead to instable performance of the machinery. To avoid such malfunction and breakdown of the machinery equipment due to misalignment is review critically in this research paper and various machine learning techniques to tackle the issue is highlighted. This review article finds the basis for developing an effective system in order to reduce the breakdown of machinery or equipment. Conventional Machine Learning methods, like Artificial neural network, Decision Tree, Random Forest, Support Vector Machines(SVM) have been applied to detecting categorizing fault, while the application of Deep Learning methods has ignited great interest in the industry

Motor Fault Diagnosis Using Higher Order Statistical Analysis of Motor Power Supply Parameters

Motor current signature analysis (MCSA) has been an effective method to monitor electrical machines for many years, predominantly because of its low instrumentation cost, remote implementation and comprehensive information contents. However, it has shortages of low accuracy and efficiency in resolving weak signals from incipient faults, such as detecting early stages of induction motor fault. In this thesis MCSA has been improved to accurately detect electrical and mechanical faults in the induction motor namely broken rotor bars, stator faults and motor bearing faults. Motor current signals corresponding to a healthy (baseline) and faulty condition on induction motor at different loads (zero, 25%, 50% and 75% of full load) were rearranged and the baseline current data were examined using conventional methods in frequency domain and referenced for comparison with new modulation signal bispectrum. Based on the fundamental modulation effect of the weak fault signatures, a new method based on modulation signal bispectrum (MSB) analysis is introduced to characterise the modulation and hence for accurate quantification of the signatures. This method is named as (MSB-SE). For broken rotor bar(BRB), the results show that MSB-SE suggested in this research outperforms conventional bispectrum CB significantly for all cases due its high performance of nonlinear modulation detection and random noise suppression, which demonstrates that MSB-SE is an outstanding technique whereas (CB) is inefficient for motor current signal analysis [1] . Moreover the new estimators produce more accurate results at zero, 25%, 50%, 75% of full load and under broken rotor bar, compared with power spectrum analysis. Especially it can easily separate the half BRB at a load as low as 25% from baseline where PS would not produce a correct separation. In case of stator faults, a MSB-SE is investigated to detect different severities of stator faults for both open and short circuit. It shows that MSB-SE has the capability to accurately estimate modulation degrees and suppress the random and non-modulation components. Test results show that MSB-SE has a better performance in differentiating spectrum amplitudes due to stator faults and hence produces better diagnosis performance, compared with that of power spectrum (PS). For motor bearing faults, tests were performed with three bearing conditions: baseline, outer race fault and inner race fault. Because the signals associated with faults produce small modulations to supply component and high noise levels, MSB-SE is used to detect and diagnose different motor bearing defects. The results show that bearing faults can induce detectable amplitude increases at its characteristic frequencies. MSB-SE peaks show a clear difference at these frequencies whereas the conventional power spectrum provides change evidences only at some of the frequencies. This shows that MSB has a better and reliable performance in detecting small changes from the faulty bearing for fault detection and diagnosis. In addition, the study also shows that current signals from motors with variable frequency drive controller have too much noise and it is unlikely to discriminate the small bearing fault component. This research also applies a mathematical model for the simulation of current signals under healthy and broken bars condition in order to further understand the characteristics of fault signature to ensure the methodologies used and accuracy achieved in the detection and diagnosis results. The results show that the frequency spectrum of current signal outputs from the model take the expected form with peaks at the sideband frequency and associated harmonics

An Assessment on the Non-Invasive Methods for Condition Monitoring of Induction Motors

The ability to forecast motor mechanical faults at incipient stages is vital to reducing maintenance costs, operation downtime and safety hazards. This paper synthesized the progress in the research and development in condition monitoring and fault diagnosis of induction motors. The motor condition monitoring techniques are mainly classified into two categories that are invasive and non-invasive techniques. The invasive techniques are very basic, but they have some implementation difficulties and high cost. The non-invasive methods, namely MCSA, PVA and IPA, overcome the disadvantages associated to invasive methods. This book chapter reviews the various non-invasive condition monitoring methods for diagnosis of mechanical faults in induction motor and concludes that the instantaneous power analysis (IPA) and Park vector analysis (PVA) methods are best suitable for the diagnosis of small fault signatures associated to mechanical faults. Recommendations for the future research in these areas are also presented

Slip in radial cylindrical roller bearings and its influence on the formation of white etching cracks

Ungünstige Betriebsbedingungen und unzureichende Radialkräfte führen zu Schlupf in Radial-Zylinderrollenlagern. Das kann zu verschiedenen Ausfallmechanismen, wie Anschmierung, Fressschäden, Abplatzen und auch sogenannten White Etching Cracks (WEC) führen, die bereits nach einem Bruchteil der berechneten Lagerlebensdauer auftreten können. In dieser Arbeit wird durch systematische Tests der Einfluss der Lagerbetriebsbedingungen (Radialkraft, Drehzahl und Öldurchfluss), sowie der Ausführung des Lagers (Lager- und Käfigtyp, Material, Führung und Toleranzklassen) auf die Entstehung des Wälzkörper- und Satzschlupfs untersucht. Die Wälzkörper vollrolliger Lager in der lastfreien Zone werden verzögert und sind in der Beschleunigungsphase einem vollständigen Rollenschlupf ausgesetzt. Für mit Käfig ausgestattete Zylinderrollenlager übertrifft der einteilige den zweiteiligen Käfig durch eine geringere Schlupfneigung, besonders unter begrenztem Öldurchfluss. Rollengeführte Käfige zwingen die Rollen dazu mit dem rotierenden Innenring zu interagieren, wodurch weniger Schlupf verursacht wird. Polyamid als Käfigwerkstoff bietet Gewichteinsparungen, wird aber bei hohen Lagertoleranzen nicht empfohlen, da es deformiert und somit einen höheren Schlupf bewirkt. Bei moderaten Lagertoleranzen tragen mehr Rollen zur Belastungsübertragung bei. Das kann zu höherem Rollenschlupf als bei hohen Toleranzen führen, da verringerte Traktionskräfte auf die Rollen wirken. Wird ein Lager jedoch durch eine enge Toleranzklasse (TC) vorgespannt, kann der Schlupf unter jeglichen Betriebsbedingungen verhindert werden. Der Einfluss von Schlupf auf die Bildung von WEC am Innen- und Außenring eines Zylinderrollenlagers wurde in insgesamt vier Dauerlaufversuchen mit einem zweiphasigen Belastungsschema untersucht. Während der Niedriglastphase wird das Lager bei erhöhtem Schlupf betrieben und danach einer hohen Lastphase ausgesetzt, während der Ermüdungsrissfortschritt von WEC auftreten kann. Es wurde festgestellt, dass Betriebsbedingungen mit hohem Schlupf weniger kritisch für die WEC-Bildung (an beiden Lagerringen) sind. Die sehr geringe Radialkraft, die in der Niedriglastphase aufgebracht wird, um einen hohen Schlupf zu ermöglichen, führt zu einer geringen Flächenpressung, die nicht WEC-kritisch ist. Ein weiterer Grund ist die längere Regenerationszeit zwischen zwei Überrollungen, die bei einem hohen Sollschlupf auftreten. Kritischer sind die dynamischen Kraftverhältnisse für die stehenden Lagerringe. Sie würden den Rollenschlupf unter der wechselnden Lastzonenbreite akkumulieren, was WEC-kritischer ist. Obwohl die vollrolligen Lager einen hohen Satzschlupf und 100%igen Rollenschlupf in der Lastzone erleiden, zeigten sie auch nach mehr als 3400 Teststunden, unter den für Käfiglager sehr kritischen Prüfbedingungen, keine Anzeichen eines WEC-Ausfalls.Unfavorable operating conditions and inadequate radial force cause slip to occur in radial cylindrical roller bearings. This can also lead to several failure mechanisms such as smearing, scuffing, spalling, and White Etching cracks (WEC) that can occur at a small percentage of the calculated bearing life. In this work, through systematic testing, the influence of the bearing operating conditions (radial force, speed, and lubricant flow rate) as well as the bearing’s design (bearing type, cage type, material, guidance as well as the clearance class) on the development of the roller- and the rolling set slip was studied. The rollers of the full complement bearing stall in the load-free zone, and they suffer from a 100% roller slip at the acceleration zone. For caged bearings, a single-part cage outperforms the two-part cages by having lower slip tendency under restricted oil flow rates. Cages that are roller-guided force the rollers to interact more with the rotating inner ring and thus suffer from an overall lower slip. For the cage material, Polyamide cages offer weight savings. However they are not recommended under elaborated clearance as they would deform and cause high slip. Under moderate clearance, more rollers contribute to the load transfer. This leads to higher roller slip than under higher clearance level due to the decrease in traction forces acting on each roller. However, preloading a bearing by using the TC clearance class while using a tight fitting for both rings my lead to the elimination of the slip under any operating conditions. The influence of slip on the formation of WEC on the inner and outer rings of a cylindrical roller bearing was studied by conducting a total of four endurance tests using a two-phase loading scheme. In the low-load phase, a slip-rich environment is introduced to the bearing during which lubricant smearing can take place. After that, a high load phase is introduced to the bearing during which, fatigue crack propagation of WEC is enabled. It was found that high-slip operating conditions are less critical for the WEC formation on both bearing rings. The very low radial force that must be used in the low load phase to allow such a high slip to occur would result in a low contact pressure that is not WEC-critical. Another reason is the longer regeneration time between two overrollings occurring at a high set slip. Dynamic force conditions are more critical for the stationary bearing rings. They would accumulate the roller slip under the changing load zone width which is more WEC-critical. Although suffering from high set slip and 100% roller slip in the load zone, full complement bearings didn’t show any sign of WEC failure even after testing them for more than 3400 hours under very critical testing conditions for caged bearings

Novel technology based on the spectral kurtosis and wavelet transform for rolling bearing diagnosis

A novel diagnosis technology combining the benefits of spectral kurtosis and wavelet transform is proposed and validated for early defect diagnosis of rolling element bearings. A systematic procedure for feature calculation is proposed and rules for selection of technology parameters are explained. Experimental validation of the proposed method carried out for early detection of the inner race defect. A comparison between frequency band selection through wavelets and spectral kurtosis is also presented. It has been observed that the frequency band selected using spectral kurtosis provide better separation between healthy and defective bearings compared to the frequency band selection using wavelet. In terms of Fisher criterion the use of spectral kurtosis has a gain of 2.75 times compared to the wavelet

An automated procedure for detection and identification of ball bearing damage using multivariate statistics and pattern recognition

This paper suggests an automated approach for fault detection and classification in roller bearings, which is based on pattern recognition and principal components analysis of the measured vibration signals. The signals recorded are pre-processed applying a wavelet transform in order to extract the appropriate high frequency (detailed) area needed for ball bearing fault detection. This is followed by a pattern recognition (PR) procedure used to recognise between signals coming from healthy bearings and those generated from different bearing faults. Four categories of signals are considered, namely no fault signals (from a healthy bearing) inner race fault, outer race fault and rolling element fault signals. The PR procedure uses the first six principal components extracted from the signals after a proper principal component analysis (PCA). In this work a modified PCA is suggested which is much more appropriate for categorical data. The combination of the modified PCA and the PR method ensures that the fault is automatically detected and classified to one of the considered fault categories. The method suggested does not require the knowledge/ determination of the specific fault frequencies and/or any expert analysis: once the signal filtering is done and the PC's are found the PR method automatically gives the answer if there is a fault present and its type