Low-speed Bearings Research Articles

Reduction in power loss and increased safety of thrust collar bearings through profiling of the treads – Application of rolling bearing profiles and crowning on thrust collar bearings

Thrust collar bearings can be used to increase the efficiency of megawatt-scaled turbo drivetrains by transferring the compressor thrust from the highspeed shaft to the low-speed shaft, where it can be efficiently supported in the low-speed bearings. This also allows the bearing on the high-speed shaft to be replaced by bearing types with lower power loss. In the following, measures to reduce the thrust collar power loss are investigated. In particular, the effect of different tread profiling is addressed. It is shown that the usual profiling of tribologically similar rolling bearings is no better suited to thrust collars than fully crowned profiles.

Influence of Grease Contamination on Acoustic Emission Monitoring of Low-speed Roller Bearings

Large-scale low-speed rolling element bearings form crucial connections in offshore installations such as heavy-lifting cranes, single point mooring systems, and wind turbines. Due to the stochastic nature of wind and waves, the applied loads on these bearings are hardly predictable. Furthermore, the remote nature of the offshore environment requires a high standard of operational safety and reliability, reinforcing the need for advanced inspection and monitoring methods. Acoustic emission (AE) is a continuous monitoring technique for condition assessment of low-speed bearings, as it may detect the stress waves associated with developing degradation within the rolling elements. This paper presents an investigation on the influence of grease contamination on acoustic emission (AE) monitoring of low-speed bearings. It discusses several experiments that have been conducted in various regimes of particle contaminated lubrication, and proposes a strategy for condition monitoring. The presented experiments have been conducted with differing bearing geometries in multiple testing environments. The contamination particles have been both naturally developed and artificially introduced. The results total of 9 experimental cases are discussed and compared. All experiments follow the same data-acquisition principles, utilising arrays of three AE transducers sensitive in a range between 40-580 kHz. Data is recorded while operating the bearing at low speed under load. Abrasive wear of the rolling elements and crushing of the particles are concluded to be the main sources of AE due to particle contamination. Processing consists of waveform cross-correlation clustering and feature analysis. The results suggest that hard abrasive particles make significant contribution to the AE signals, which is associated with abrasive wear of the rollers and raceways. Comparisons with the contribution of softer and finer particles are presented as well.

Synchronous averaging with sliding narrowband filtering for low-speed bearing fault diagnosis

The health condition of low-speed rolling bearing, such as the main bearing in wind turbines which bears the heavy dead weight and operates under variable speeds, has a big impact on the safe operation of the machinery. Therefore, damage detection of low-speed bearings plays a key role in the health management of large-scale rotating machinery. However, in popular vibration based bearing damage detection algorithms, due to the fact that the additional vibration features incurred by the low-speed operated bearing damage are typically weak in amplitude and low in frequency contents, the additional responses caused by damage are difficult to be isolated by conventional algorithms. Especially, in the cases of variable speed operations, the smearing effect caused by Fourier transform makes it more difficult to extract the damage features by spectrum analysis based methods. To deal with these issues, we developed a damage detection procedure specially designed for bearings operated at low and variable speeds. According to the dynamic properties of the vibration signals incurred by a low-speed bearing with damage, an envelope analysis method based on synchronous averaging with sliding narrow band-pass filters is designed and developed for extracting damage features in the low frequency range. The fundamental theory used in the method is derived first. Then, a damage detection signal processing procedure is constructed based on the elaborated theory. The feasibility and advantages of the proposed methodology are validated by numerical simulations as well as the measured data from a wind turbine field example.

Quantitative Analysis of Domain Discrepancies Between General- Speed and Very Low-Speed Bearings and Its Applications to Fault Diagnosis of Very Low-Speed Bearings Using Transfer Learning

This paper proposes a fault diagnosis framework for very low-speed bearings using transfer learning. To handle large domain discrepancies between general-speed and very lowspeed bearings, the quantitative analysis is performed using operating information and geometries of bearings. From this analysis, the domain discrepancy can be quantitively compared under various speed conditions. Furthermore, a transfer learning technique is proposed to reduce the analyzed discrepancies. The domain discrepancy can be significantly aligned by integrating the operating information and geometries of bearings into transfer learning. Also, the proposed framework is not tailored for the specific algorithm; this means that the framework can be applied to any transfer learning technique regardless of the architecture. The proposed method is validated using two bearing datasets under general-speed and very low-speed conditions. The results show that the domain discrepancy can be quantitatively measured for transfer learning. Additionally, the proposed fault diagnosis framework outperforms the existing methods by aligning domain discrepancies of bearing datasets under large different speeds.

Acoustic emission monitoring of naturally developed damage in large-scale low-speed roller bearings

This article presents an approach to identify naturally developed damage in low-speed bearings using waveform-similarity-based clustering of acoustic emissions (AEs) under fatigue loading. The approach is motivated by the notation that each recorded AE signal from a particular damage is defined by the convolution of the source signal, transfer function of the propagation path and transfer function of the utilised sensor, and may thusly be used to identify consistent AE sources, for example due to crack growth. A sequential clustering procedure is proposed, that is based on waveform cross-correlation. The supporting theoretical background of waveform similarity, rooted in an analytical formulation of waveform propagation and transmission in complex structures, is discussed. The presented methodology is evaluated through application to AE data obtained in a low-speed run-to-failure experiment utilising a densely instrumented purpose-built linear bearing segment. The implemented sensor system comprises arrays of three types of AE transducers, that is relatively low - (40–100 kHz), mid - (95–180 kHz) and high-frequency (180–580 kHz), that are situated on both the raceways and supporting substructures of either side of the bearing. Over the course of 225,000 cycles of extension and retraction, wear has been developed. A total of about ∼2,300,000 AE signals have been recorded. Analysis of the recorded data suggests the rate of degradation increases from around 70,000 cycles onwards. Highly consistent structures of clusters indicative of a localised defect in the raceway have been identified from around 170,000 cycles onwards. These clusters are characterised by hit-rates in the range of 1–2 hits per cycle and an average similarity of 93%, they comprise about half the AE activity for the periods they have been identified for. These results highlight that the proposed cross-correlation-based clustering of AE waveforms and identification of multi-channel formations in said clusters compose a suitable methodology for assessment of damage in low-speed roller bearings.

Compound faults diagnosis method for wind turbine mainshaft bearing with Teager and second-order stochastic resonance

Low rotational speed and heavy load lead to the weak vibration energy and complexity of signals captured on wind turbine mainshaft bearing. Compared with single fault diagnosis, compound faults diagnosis is a challengeable task for the wind turbine mainshaft bearing. To extract fault features accurately, one fault diagnosis method based on Teager energy operator and second-order stochastic resonance (TSSR) is proposed. Firstly, Teager energy operator (TEO) is employed to achieve the purpose of enhancing impact energy in vibration signal. Then, under the assistance of residual noises, second-order stochastic resonance (SSR) is utilized for processed signal to enhance the fault features. Meanwhile, the modified signal-to-ratio (MSNR) index is carried out to select optimal parameters of SSR system automatically. Finally, the fault characteristic frequencies are identified by fast Fourier transform (FFT) spectrums of the SSR output. The test-rig artificial faulty bearing signal and wind turbine mainshaft bearing signal from wind farm are employed to illustrate the superiority and effectiveness of the proposed method. The contribution of the method provides a certain reference for the heavy-load and low-speed bearings fault diagnosis in other fields.

Exploiting the Synergy between Concentrated Polymer Brushes and Laser Surface Texturing to Achieve Durable Superlubricity.

Friction continues to account for the bulk of energy losses in mechanical systems, with an estimated 23% of the world’s total energy consumption used to overcome friction. Concentrated polymer brushes (CPBs) have recently attracted significant scientific and industrial attention, given their ability to achieve superlubricity (i.e., coefficients of friction below 0.01); however, understanding the mechanistic interactions underlying their wear performance has been largely overlooked. Herein, we employ a custom-built optical test apparatus to investigate the inter-dependencies between CPBs and laser-produced surface texture (LST), assessing for the first time the friction, film thickness, and wear behavior in situ and simultaneously. Recent developments in picosecond laser etching allowed us to graft CPBs atop the finest laser-etched matrix of micron-sized dimples reported in literature to date. At low sliding speeds, combined CPB–LST reduces the coefficient of friction to 0.0006, while increasing the CPB durability by up to 34% through a lateral support mechanism offered by the textured micro-features. Furthermore, the imaging results shed light on CPB failure mechanisms. Both these mechanisms of lateral support and failure propagation impact the wear resistance of CPBs and are important in the development of CPBs for future applications (e.g., in low-speed bearings functioning under controlled abrasive wear conditions).

A Fault Diagnosis Method of Rolling Mill Bearing at Low Frequency and Overload Condition Based on Integration of EEMD and GA-DBN

To solve the problems of difficult fault signal recognition and poor diagnosis effect of different damage in the same position in rolling mill bearing at low speed, a fault diagnosis method of rolling mill bearing based on integration of EEMD and DBN was proposed. The vibration signals in horizontal, axial, and vertical directions were decomposed and reconstructed by EEMD, and frequency domain analysis was carried out by using refined spectrum. Then, the signal's time-frequency domain index, rolling force, and torque component feature vector were input into genetic algorithm (GA) to optimize DBN model classification. In order to verify the effectiveness of the method, the experimental study was carried out on the two-high experimental rolling mill. The results show that EEMD combined with thinning spectrum can solve the problem of fault feature extraction well. Compared with time-frequency domain characteristic input, the prediction accuracy of DBN model is obviously improved. And the accuracy of GA-DBN model is higher, and the accuracy is 98.3%, and the time taken to diagnose is significantly reduced. Finally, the fault classification of different parts of bearings and the fault diagnosis of different damage in the same part are realized, which provides a good theoretical basis for the fault diagnosis of low-speed bearings and has important engineering significance.

Entropy Indicators: An Approach for Low-Speed Bearing Diagnosis.

To increase the competitiveness of wind energy, the maintenance costs of offshore floating and fixed wind turbines need to be reduced. One strategy is the enhancement of the condition monitoring techniques for pitch bearings, because their low operational speed and the high loads applied to them make their monitoring challenging. Vibration analysis has been widely used for monitoring the bearing condition with good results obtained for regular bearings, but with difficulties when the operational speed decreases. Therefore, new techniques are required to enhance the capabilities of vibration analysis for bearings under such operational conditions. This study proposes the use of indicators based on entropy for monitoring a low-speed bearing condition. The indicators used are approximate, dispersion, singular value decomposition, and spectral entropy of the permutation entropy. This approach has been tested with vibration signals acquired in a test rig with bearings under different health conditions. The results show that entropy indicators (EIs) can discriminate with higher-accuracy damaged bearings for low-speed bearings compared with the regular indicators. Furthermore, it is shown that the combination of regular and entropy-based indicators can also contribute to a more reliable diagnosis.

GEARLESS ELECTRIC DRIVE OF ROTOR OF ELECTROMAGNETIC SEPARATOR

The aim of the work is to increase the reliability and durability of the electromagnetic rotor type separator due to application of a gearless electric drive for rotor. In the process of high-gradient magnetic separation, the drive must ensure stable rotation of the massive rotor at low speeds, smooth transients of the entire electromechanical system during the starting, stopping and changing of the rotor speed. And also, it is necessary to provide a quick response of the electromechanical system in emergency situations. A characteristic feature of a gearless electric drive is that its individual elements, such as the shaft, bearings, rotor, are built directly into the construction of the separator, the motor itself does not exist separately. The article presents the basic expressions for arc-mounted electric motor and its mechanical characteristics. The advantages of a gearless electric drive and ways of increasing the efficiency of work in a system with a rotor of an electromagnetic separator are illustrated. Thanks to the use of a gearless electric drive for the rotor of an electromagnetic separator, the entire kinematic chain of gears and a high-speed electric motor are eliminated, and only two low-speed bearings remain in which the rotor rotates. Since the rotation speed of the rotor is low, about 10 rpm, the service life of such bearings will be quite large. Reliability indicators of arcforming machines are similar to conventional transformers. From the design of the drive, all rubbing and, consequently, wearing parts are eliminated and there are no elements and devices in it that require routine maintenance during operation. Thus, the development of an arc-core electric drive for the rotor of an electromagnetic separator makes it possible to obtain qualitatively new indicators of reliability and durability. For remote mining and processing plants, where valuable and expensive concentrates are mined, the issuesof improving reliability and uptime are the most important.

Fault Classification of Low-Speed Bearings Based on Support Vector Machine for Regression and Genetic Algorithms Using Acoustic Emission

This work under consideration makes use of support vector machines (SVM) for regression and genetic algorithms (GA) which may be referred to as SVMGA, to classify faults in low-speed bearings over a specified speed range, with sinusoidal loads applied to the bearing along the radial and axial directions. GA is used as a heuristic tool in finding profound solution to the difficult problem of solving the highly non-linear situation through the application of the principles of evolution by optimizing the statistical features selected for the SVM for regression training solution. It is used to determine the training parameters of SVM for regression which can optimize the model and hence without the forehand knowledge of the probabilistic distribution can form new features from the original dataset. Using SVM for regression, the non-linear regression and fault recognition are achieved. Classification is performed for three classes. In this work, the GA is used to first optimize the statistical features for the best performance before they are used to train the SVM for regression. Experimental studies using acoustic emission caused by bearing faults showed that SVMGA with a Gaussian kernel function better achieves classification on the bearings operated at low speed, regardless of the load type and, under different fault conditions, compared to the exponential kernel function and the other many kernel functions which also can be used for the same conditions. This study accomplished the effective classification of different bearing fault patterns especially at low speeds and at varying load conditions using support vector machines (SVM) for regression and genetic algorithms (GA) referred to as SVMGA.

Fault feature extraction of low speed roller bearing based on Teager energy operator and CEEMD

The fault signals of low-speed rolling elements bearing are non-stationary and non-linear, and consequently it is difficult to extract the fault characteristics by the traditional time and frequency domains analysis methods. Furthermore, the vibration signals suffer from severe signal attenuation and noise corruption during the signal transmission process. In order to effectively enhance and extract the fault characteristics from weak bearing signal, it requires effective signal processing strategies or high sensitive sensors to detect the low energy bearing vibration signals. In this paper, one such signal processing method is proposed to detect fault characteristics combined Teager energy operator and Complementary Ensemble Empirical Mode Decomposition (CEEMD). In this method, firstly Teager energy operator is used to strengthen the signal after wavelet noise reduction since it has good temporal resolution and adaptive ability for signal transient changes, and has unique advantages in detecting signal impact characteristics. Then CEEMD algorithm is carried out to extract bearing fault through Intrinsic Mode Function (IMF) decomposition. The proposed method is validated by a scaling model test rig of a wind turbine. The results validate that the method can effectively extract the fault characteristics of low-speed bearings and identify the bearing fault.

Bearing Failure Analysis on Gearbox Forced Draft Fan at LNG Plant

Boilers Forced Draft Fan has a gearbox reducer to reduce speed from steam turbine as a driver and fan as the driven. Based on Predictive Maintenance Group at Machinery and Heavy Equipment Section, it was found that gearbox has high vibration and noise when boilers load on 195 Ton/Hr (Max Load 295 Ton/hr). Vibration Analysis indicates that the source of vibration comes from bearings with peak value 0.572 in/sec at gearbox high speed outboard vertical and 0.593 in/sec at gearbox high speed outboard horizontal (Max Allowance 0.50 in/sec). Lubrication analysis using X-ray Flourescence method, to see wear particle on lubricating oil showed that Tin(Sn) content is very high 203.62 mg/L (maximum allowance for Tin(Sn) at gearbox is 20mg/L), but wear debris are not found in lubricating oil. Gearbox disassembly found that high speed and low speed bearings suffered severe corrosion. Corrosion at bearings are caused by water contamination on lubrication oil, it comes from steam leak due to carbon ring failure at steam turbine which is steam turbine bearing lubrication and gearbox lubrication get into one lubrication system. After Bearings replaced with a new one, vibration analysis shown improved vibration conditions 0.084 in/sec at gearbox high speed outboard vertical and 0.066 in/sec at gearbox high speed outboard horizontal. In conclucion, high vibration and noise at gearbox reducer were caused by bearings failure due to severe corrosion, and water contamination on lubrication oil for gearbox due to carbon ring failure on steam turbine.

Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines

This paper proposes a reliable multiple combined fault diagnosis scheme for bearings using heterogeneous feature models and an improved one-against-all multiclass support vector machines (OAA-MCSVM) classifier. Distinct feature extraction methods are simultaneously applied to an acoustic emission (AE) signal to extract unique fault features for diagnosing bearing defects. These fault features are composed of time domain, frequency domain statistical parameters, and complex envelope spectrum analysis. Generally, a high-dimensional feature vector is used to train the standard OAA-MCSVM classifier for diagnosis and identification of bearing defects. However, this classification method ignores individual classifier competence when results from multiple classes are agglomerated for the final decision, and therefore, yields undecided and overlapped feature spaces where classification accuracy is severely degraded. To solve this unreliability problem, this paper introduces a dynamic reliability measure (DReM) technique for individual support vector machines (SVMs) in the one-against-all (OAA) framework. This DReM accounts for the spatial variation of the classifier's performance by finding the local neighborhood of a test sample in the training samples space and defining a new decision function for the OAA-MCSVM. The efficacy of the proposed OAA-MCSVM classifier with DReM is tested for identifying single and multiple combined faults in low-speed bearings. The experimental results demonstrate that the proposed classifier technique is superior to three state-of-the-art algorithms, yielding 6.19–16.59% improvement in the average classification performance.

Time–frequency envelope analysis-based sub-band selection and probabilistic support vector machines for multi-fault diagnosis of low-speed bearings

This paper proposes a highly reliable multi-fault diagnosis scheme for low-speed rolling element bearings using an effective time–frequency envelope analysis and a Bayesian inference based one-against-all support vector machines (probabilistic-OAASVM) classifier. The proposed method first performs a wavelet packet transform based envelope analysis on an acoustic emission signal to select sub-bands of the signal that contain the most intrinsic and pertinent information about the defects. Frequency- and time-domain fault features are extracted only from selected sub-bands for fault classification. Traditional one-against-all SVMs (OAASVM), a widely used multi-class pattern recognition technique, employ an arbitrary combination of a series of binary classifiers yielding overlapped feature spaces, where a data sample can be unclassifiable. To address this limitation, we formulate the feature space of OAASVM as an appropriate Gaussian process prior (GPP) and interpret OAASVM results as a posterior probability estimation procedure using Bayesian inference under this GPP. The efficacy of the proposed probabilistic-OAASVM classifier is verified for low-speed rolling element bearings under various conditions. Experimental results indicate that the proposed method outperforms the state-of-the-art algorithms for multi-fault classification of low-speed bearings, yielding a 4.95–20.67% improvement in the average classification accuracy.

Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis

This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposed GA-based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

High-Performance and Energy-Efficient Fault Diagnosis Using Effective Envelope Analysis and Denoising on a General-Purpose Graphics Processing Unit

This paper proposes an effective envelope analysis-based methodology for machinery condition monitoring and validates its efficacy by identifying bearing failures with 1-s acoustic emission (AE) signals sampled at 1 MHz. The proposed condition monitoring methodology of low-speed bearings consists of denoising to improve the signal-noise ratio of the acquired AE signal by employing a soft-thresholding technique with adaptively estimated positive and negative noise levels and an effective envelope analysis to detect the periodic impacts of the AE signals inherent in bearing defects by utilizing a 2-D visualization technique based on the improved residual frequency component-to-peak ratios. Despite the fact that the proposed method shows satisfactory performance for bearing condition monitoring, its computational complexity limits its use in real-time applications. To improve the performance and reduce the energy consumption of the proposed method, this paper proposes an efficient parallel implementation of the proposed method on a general-purpose graphics processing unit (GPGPU) by exploiting the memory hierarchy and the massive parallelism inherent in the proposed method. Experimental results indicate that the proposed GPGPU-based approach achieves an at least 68.9× speed improvement compared to the same sequential implementation on well-known Texas Instruments digital signal processors (TI DSPs). In addition, the proposed GPGPU approach reduces the energy consumption by at least 66% compared to TI DSPs.

Low speed bearings fault detection and size estimation using instantaneous angular speed

The fault diagnosis and prognosis of low speed machines remains a difficult problem despite remarkable advances in the conditional monitoring domain. The Rolling-element bearing is a vital part of these machines and its failure is one of the main causes of machine breakdown. In order to have an efficient maintenance strategy, fault diagnosis of a bearing and time estimation of its remaining useful life is needed. However, conventional vibration analysis at very low speeds generally fails to detect vibrations issued from a faulty bearing due to its low energy, high and variable loading conditions and to the noisy environment generated by other mechanical components of low speed machines such as gearing systems. In this work, instantaneous angular speed (IAS)-based fault diagnosis is introduced in order to compensate for the shortcoming of conventional monitoring techniques since it is strictly synchronized to shaft rotation and much less dependent on the transfer path between the defect and the sensor contrary to vibration and acoustic emission analysis. At very low speeds and in the case of a seeded spall on the bearing’s race, the shaft IAS reveals the shaft dynamical behavior when the rolling element passes into the spall. It is proven that this behavior is different when entering the spall than when exiting. The determination of entrance and exit moments allows a precise fault size estimation which is a critical step for bearing prognosis. The proposed fault size estimation method is tested on different seeded spall widths at different low speeds. The results gave a satisfactory fault width estimation and show that IAS measurement is a promising tool for the health monitoring of very low speed machines.

Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm

In this paper, we propose a highly reliable fault diagnosis scheme for incipient low-speed rolling element bearing failures. The scheme consists of fault feature calculation, discriminative fault feature analysis, and fault classification. The proposed approach first computes wavelet-based fault features, including the respective relative wavelet packet node energy and entropy, by applying a wavelet packet transform to an incoming acoustic emission signal. The most discriminative fault features are then filtered from the originally produced feature vector by using discriminative fault feature analysis based on a binary bat algorithm (BBA). Finally, the proposed approach employs one-against-all multiclass support vector machines to identify multiple low-speed rolling element bearing defects. This study compares the proposed BBA-based dimensionality reduction scheme with four other dimensionality reduction methodologies in terms of classification performance. Experimental results show that the proposed methodology is superior to other dimensionality reduction approaches, yielding an average classification accuracy of 94.9%, 95.8%, and 98.4% under bearing rotational speeds at 20revolutions-per-minute (RPM), 80RPM, and 140RPM, respectively.

Gas-Expanded Lubricant Performance and Effects on Rotor Stability in Turbomachinery

Gas-expanded lubricants (GELs) are tunable mixtures of synthetic oil and carbon dioxide that enable dynamic control of lubricant viscosity during bearing operation. This control can help reduce bearing power loss and operating temperatures while also providing direct control over bearing stiffness and damping, which can enhance rotordynamic performance. In this work, the bearing and rotordynamic performance of two representative high-speed machines was evaluated when different lubricants, including GELs, were supplied to the machine bearings. The machines chosen for this analysis, an eight-stage centrifugal compressor and a steam turbine-generator system, represent a wide range of speed and loading conditions encountered in modern turbomachinery. The fluids compared for machine performance were standard petroleum-based lubricants, polyol ester (POE) synthetic oils, and POE-based GELs. The performance simulations were carried out using a thermoelastohydrodynamic bearing model, which provided bearing stiffness and damping coefficients as inputs to finite element rotordynamic models. Several bearing performance metrics were evaluated including power loss, operating temperature, film thickness, eccentricity, and stiffness and damping coefficients. The rotordynamic analysis included an evaluation of rotor critical speeds, unbalance response, and stability. Bearing performance results for the compressor showed a 40% reduction in power loss at operating speed when comparing the GEL to the petroleum-based lubricant. The GEL-lubricated compressor also exhibited lower operating temperatures with minimal effects on film thickness. GELs were also predicted to produce lower bearing stiffness when compared to standard fluids in the compressor. Rotordynamic results for the compressor showed that the fluid properties had only minor effects on the unbalance response, while GELs were found to increase the stability margin by 43% when compared with standard fluids. The results from the turbine-generator system also demonstrated increases in low-speed bearing efficiency with the use of GELs, though at higher speeds the onset of turbulent flow in the GEL case offset these efficiency gains. Rotordynamic results for this system showed a contrast with the compressor results, with the GELs producing lower stability margins for a majority of the modes predicted due to increased bearing stiffness in the high-speed turbine bearings and negative stiffness in the lightly loaded, low-speed pinion bearings. These results suggest that GELs could be beneficial in providing control over a wide range of machine designs and operating conditions and that some machines are especially well suited for the tunability that these fluids impart.