Case report: Formation and recurrence of inflammatory pseudotumor after metal-on-metal hip arthroplasty.

The metal-on-metal (MoM) artificial hip joint is a prosthesis used in early hip arthroplasty, particularly for hip resurfacing and total hip arthroplasty. However, abrasion and corrosion of MoM bearings result in the production of metal ions, such as cobalt and chromium, thereby inducing several complications such as inflammatory pseudotumor, aseptic inflammation, and allergy to metal ions (delayed type IV hypersensitivity). In this case report, we present a patient who was hospitalized for recurrence of a mass in the right inguinal area. In 2010, the patient underwent right MoM total hip arthroplasty for right femoral head necrosis and exhibited a good postoperative recovery. In 2019, the patient experienced pain in the right hip with activity limitation without any evident triggers, and a palpable mass was observed in the right inguinal area. A large periprosthetic mass was resected under general anesthesia, and the patient recovered well after the operation. Based on post-surgery imaging and pathological examinations, the mass was diagnosed as a periprosthetic inflammatory pseudotumor. In 2021, the inflammatory pseudotumor recurred at the same site. He then underwent right total hip revision surgery under epidural anesthesia and recovered well after surgery. No recurrence was noted at moderate follow-up. The incidence of inflammatory pseudotumors is high in MoM hip arthroplasty. Early revision is necessary in patients who meet the indications for revision, while regular postoperative follow-up is crucial.

Research on bearing fault diagnosis based on improved genetic algorithm and BP neural network.

Health monitoring and fault diagnosis of rolling bearings are crucial for the continuous and effective operation of mechanical equipment. In order to improve the accuracy of BP neural network in fault diagnosis of rolling bearings, a feature model is established from the vibration signals of rolling bearings, and an improved genetic algorithm is used to optimize the initial weights, biases, and hyperparameters of the BP neural network. This overcomes the shortcomings of BP neural network, such as being prone to local minima, slow convergence speed, and sample dependence. The improved genetic algorithm fully considers the degree of concentration and dispersion of population fitness in genetic algorithms, and adaptively adjusts the crossover and mutation probabilities of genetic algorithms in a non-linear manner. At the same time, in order to accelerate the optimization efficiency of the selection operator, the elite retention strategy is combined with the hierarchical proportional selection operation. Using the rolling bearing dataset from Case Western Reserve University in the United States as experimental data, the proposed algorithm was used for simulation and prediction. The experimental results show that compared with the other seven models, the proposed IGA-BPNN exhibit superior performance in both convergence speed and predictive performance.

Application of Residual Structure Time Convolutional Network Based on Attention Mechanism in Remaining Useful Life Interval Prediction of Bearings.

In the context of Industry 4.0, bearings, as critical components of machinery, play a vital role in ensuring operational reliability. The detection of their health status is thus of paramount importance. Existing predictive models often focus on point predictions of bearing lifespan, lacking the ability to quantify uncertainty and having room for improvement in accuracy. To accurately predict the long-term remaining useful life (RUL) of bearings, a novel time convolutional network model with an attention mechanism-based soft thresholding decision residual structure for quantifying the lifespan interval of bearings, namely TCN-AM-GPR, is proposed. Firstly, a spatio-temporal graph is constructed from the bearing sensor signals as the input to the prediction model. Secondly, a residual structure based on a soft threshold decision with a self-attention mechanism is established to further suppress noise in the collected bearing lifespan signals. Thirdly, the extracted features pass through an interval quantization layer to obtain the RUL and its confidence interval of the bearings. The proposed methodology has been verified using the PHM2012 bearing dataset, and the comparison of simulation experiment results shows that TCN-AM-GPR achieved the best point prediction evaluation index, with a 2.17% improvement in R2 compared to the second-best performance from TCN-GPR. At the same time, it also has the best interval prediction comprehensive evaluation index, with a relative decrease of 16.73% in MWP compared to the second-best performance from TCN-GPR. The research results indicate that TCN-AM-GPR can ensure the accuracy of point estimates, while having superior advantages and practical significance in describing prediction uncertainty.

Adaptive Low-Rank Tensor Estimation Model Based Multichannel Weak Fault Detection for Bearings.

Multichannel signals contain an abundance of fault characteristic information on equipment and show greater potential for weak fault characteristics extraction and early fault detection. However, how to effectively utilize the advantages of multichannel signals with their information richness while eliminating interference components caused by strong background noise and information redundancy to achieve accurate extraction of fault characteristics is still challenging for mechanical fault diagnosis based on multichannel signals. To address this issue, an effective weak fault detection framework for multichannel signals is proposed in this paper. Firstly, the advantages of a tensor on characterizing fault information were displayed, and the low-rank property of multichannel fault signals in a tensor domain is revealed through tensor singular value decomposition. Secondly, to tackle weak fault characteristics extraction from multichannel signals under strong background noise, an adaptive threshold function is introduced, and an adaptive low-rank tensor estimation model is constructed. Thirdly, to further improve the accurate estimation of weak fault characteristics from multichannel signals, a new sparsity metric-oriented parameter optimization strategy is provided for the adaptive low-rank tensor estimation model. Finally, an effective multichannel weak fault detection framework is formed for rolling bearings. Multichannel data from the repeatable simulation, the publicly available XJTU-SY whole lifetime datasets and an accelerated fatigue test of rolling bearings are used to validate the effectiveness and practicality of the proposed method. Excellent results are obtained in multichannel weak fault detection with strong background noise, especially for early fault detection.

A Deep Learning Method for Bearing Cross-Domain Fault Diagnostics Based on the Standard Envelope Spectrum.

Intelligent fault diagnostics based on deep learning provides a favorable guarantee for the reliable operation of equipment, but a trained deep learning model generally has low prediction accuracy in cross-domain diagnostics. To solve this problem, a deep learning fault diagnosis method based on the reconstructed envelope spectrum is proposed to improve the ability of rolling bearing cross-domain fault diagnostics in this paper. First, based on the envelope spectrum morphology of rolling bearing failures, a standard envelope spectrum is constructed that reveals the unique characteristics of different bearing health states and eliminates the differences between domains due to different bearing speeds and bearing models. Then, a fault diagnosis model was constructed using a convolutional neural network to learn features and complete fault classification. Finally, using two publicly available bearing data sets and one bearing data set obtained by self-experimentation, the proposed method is applied to the data of the fault diagnostics of rolling bearings under different rotational speeds and different bearing types. The experimental results show that, compared with some popular feature extraction methods, the proposed method can achieve high diagnostic accuracy with data at different rotational speeds and different bearing types, and it is an effective method for solving the problem with cross-domain fault diagnostics for rolling bearings.

Bearing-Fault-Feature Enhancement and Diagnosis Based on Coarse-Grained Lattice Features.

In view of the frequent failures occurring in rolling bearings, the strong background noise present in signals, weak features, and difficulties associated with extracting fault characteristics, a method of enhancing and diagnosing rolling bearing faults based on coarse-grained lattice features (CGLFs) is proposed. First, the vibrational signals of bearings are subjected to adaptive filtering to eliminate background noise. Second, frequency-domain transformation is performed, and a coarse-grained approach is used to continuously segment the spectrum. Within each segment, amplitude-enhancement operations are executed, transforming the data into a CGLF graph that enhances fault characteristics. This graph is then fed into a Swin Transformer-based pattern-recognition network. Third and finally, a high-precision fault diagnosis model is constructed using fully connected layers and Softmax, enabling the diagnosis of bearing faults. The fault recognition accuracy reaches 98.30% and 98.50% with public datasets and laboratory data, respectively, thereby validating the feasibility and effectiveness of the proposed method. This research offers an efficient and feasible fault diagnosis approach for rolling bearings.

A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets.

In this paper, a Monte Carlo (MC)-based extended Kalman filter is proposed for a two-dimensional bearings-only tracking problem (BOT). This problem addresses the processing of noise-corrupted bearing measurements from a sea acoustic source and estimates state vectors including position and velocity. Due to the nonlinearity and complex observability properties in the BOT problem, a wide area of research has been focused on improving its state estimation accuracy. The objective of this research is to present an accurate approach to estimate the relative position and velocity of the source with respect to the maneuvering observer. This approach is implemented using the iterated extended Kalman filter (IEKF) in an MC-based iterative structure (MC-IEKF). Re-linearizing dynamic and measurement equations using the IEKF along with the MC campaign applied to the initial conditions result in significantly improved accuracy in the estimation process. Furthermore, an observability analysis is conducted to show the effectiveness of the designed maneuver of the observer. A comparison with the widely used UKF algorithm is carried out to demonstrate the performance of the proposed method.

Experimental Study of the Mechanical Properties of Full-Scale Rubber Bearings at 23 °C, 0 °C, and -20 °C.

In this study, the effects of ambient temperature on the horizontal mechanical performance of isolated rubber bearings were investigated using high-speed reciprocating loading methods. A comprehensive series of 54 experimental trials are performed on the full-scale (900 mm-diameter) isolation rubber bearings, encompassing a range of temperatures (-20 °C, 0 °C, and 23 °C), shear pressures (50%, 100%, and 250%), and frequencies (0.20 Hz, 0.25 Hz, and 0.30 Hz). Because the compression-shear tests were conducted at high velocities and pressures (specifically, vertical compressive stress of 15 MPa), the equipment used in these tests was capable of generating substantial inertial and frictional forces. Appropriate correction methodologies for the precise determination of mechanical performance metrics for bearings are presented. Then, a comprehensive investigation of the effects of various loading conditions on the characteristic strength, post-yield stiffness, horizontal equivalent stiffness, and equivalent damping ratio of LRB900 (lead-core rubber bearings 900 mm-diameter) and LNR900 (linear natural rubber bearings 900 mm-diameter) is conducted. The empirical results show a discernible relationship between these characteristics and ambient temperature as the number of loading cycles increases, except for the equivalent damping ratio. Finally, empirical fitting formulations incorporating the influence of ambient temperature are presented for each performance indicator. These formulas are intended to assist designers in performing seismic design analyses by allowing them to take into consideration the effects of ambient temperature comprehensively.

Fault Diagnosis Method for Rolling Bearings Based on Grey Relation Degree.

Aiming at the difficult problem of extracting fault characteristics and the low accuracy of fault diagnosis throughout the full life cycle of rolling bearings, a fault diagnosis method for rolling bearings based on grey relation degree is proposed in this paper. Firstly, the subtraction-average-based optimizer is used to optimize the parameters of the variational mode decomposition algorithm. Secondly, the vibration signals of bearings are decomposed by using the optimized results, and the feature vector of the intrinsic mode function component corresponding to the minimum envelope entropy is extracted. Finally, the grey proximity and similarity relation degree based on standard distance entropy are weighted to calculate the grey comprehensive relation degree between the feature vector of vibration signals and each standard state. By comparing the results, the diagnosis of different fault states and degrees of rolling bearings is realized. The XJTU-SY dataset was used for experimentation, and the results show that the proposed method achieves a diagnostic accuracy of 95.24% and has better diagnosis performance compared to various algorithms. It provides a reference for the fault diagnosis of rolling bearings throughout the full life cycle.

A surface study of failed planetary wind turbine gearbox Bearings to investigate the causes of the Bearing premature failure issue.

The advanced software used in designing Wind Turbine Gearboxes (WTGs) does not solve the premature failure of the gearbox bearings, which still fail within 5%-20% of their design life by flaking. This issue increases the maintenance, downtime, and early replacement costs that limit the investment in the wind energy field. The majority of the previous research have focused on bearing subsurface investigation and the microstructural changes associated with the failure patterns. Conversely, surface investigation can elucidate significant information about the loading levels and contributors to the premature bearing failure. In this study, two bearings from a planetary stage of a failed multi-megawatt wind turbine gearbox underwent surface investigations and analyses. The analyses include indentations, hardness, roughness, and severe damage regions. The study shows that the contact loading stress exceeds the recommended and more than the compressive yield stresses of the bearing materials. The loading distribution on the bearing inner race during the gearbox operation is quite different from the theoretical loading. The transient loadings throughout the service reduce the Wind Turbine Gearbox Bearings (WTGBs) service life. Furthermore, the significant effects of skewing and slipping have been confirmed. Accordingly, the, lubricant filtration system and the design of the planetary stage are recommended to be improved to extend the fatigue life of the wind turbine gearbox bearings.

Remaining Useful Life Prediction of Rolling Bearings Based on ECA-CAE and Autoformer.

In response to the need for multiple complete bearing degradation datasets in traditional deep learning networks to predict the impact on individual bearings, a novel deep learning-based rolling bearing remaining life prediction method is proposed in the absence of fully degraded bearng data. This method involves processing the raw vibration data through Channel-wise Attention Encoder (CAE) from the Encoder-Channel Attention (ECA), extracting features related to mutual correlation and relevance, selecting the desired characteristics, and incorporating the selected features into the constructed Autoformer-based time prediction model to forecast the degradation trend of bearings' remaining time. The feature extraction method proposed in this approach outperforms CAE and multilayer perceptual-Attention Encoder in terms of feature extraction capabilities, resulting in reductions of 0.0059 and 0.0402 in mean square error, respectively. Additionally, the indirect prediction approach for the degradation trend of the target bearing demonstrates higher accuracy compared to Informer and Transformer models, with mean square error reductions of 0.3352 and 0.1174, respectively. This suggests that the combined deep learning model proposed in this paper for predicting rolling bearing life may be a more effective life prediction method deserving further research and application.

Rolling bearing fault diagnosis based on Gramian angular difference field and improved channel attention model.

Fault diagnosis of rolling bearings is a critical task, and in previous research, convolutional neural networks (CNN) have been used to process vibration signals and perform fault diagnosis. However, traditional CNN models have certain limitations in terms of accuracy. To improve accuracy, we propose a method that combines the Gramian angular difference field (GADF) with residual networks (ResNet) and embeds frequency channel attention module (Fca) in the ResNet to diagnose rolling bearing fault. Firstly, we used GADF to convert the signals into RGB three-channel fault images during data preprocessing. Secondly, to further enhance the performance of the model, on the foundation of the ResNet we embedded the frequency channel attention module with discrete cosine transform (DCT) to form Fca, to effectively explores the channel information of fault images and identifies the corresponding fault characteristics. Finally, the experiment validated that the accuracy of the new model reaches 99.3% and the accuracy reaches 98.6% even under an unbalanced data set, which significantly improves the accuracy of fault diagnosis and the generalization of the model.

Evaluation of an early-stage prototype polyurethane femoral head implant for hip arthroplasty.

Introduction: Thi study evalautes a new bone-preserving femoral head cover that mimics the articular cartilage of the femoral head. Methods: A specially developed polyurethane (PU) was evaluated in biocompatibility (cytotoxicity test) and mechanical response to tensile loading. In the cytotoxicity test, steam sterilized (SS) and ethylene oxide sterilized (EtO) PU samples were incubated separately in a cell culture medium. The seeded cell line MG-63 was then added to these sample-incubated cell culture mediums. One negative control group and one positive control group were also evaluated. The cells in each group were cultured for seven days before being quantified using the alamarBlue assay. In the mechanical test, the femoral head cover implants were separated into three groups of three samples. Each group represented a different implant insertion idea: direct insertion (uc sample) and another two insertion modes (is and ss samples) representing implants with enclosure mechanisms. The test consisted of distance-controlled cyclic tensile loadings followed by a failure test. Results: The cytotoxicity test results show no significant difference in fluorescence intensity between the negative control, the three SS groups, and one EtO group (P > 0.05). However, the other two EtO groups exhibit significantly lower fluorescence intensity compared with the negative control (P < 0.05). In the mechanical test, the is samples have the highest cyclic loading force at 559.50 ± 51.41 N, while the uc samples exhibit the highest force in the failure test at 632.16 ± 50.55 N. There are no significant differences (P > 0.05) among the uc, is, and ss groups in terms of stiffness. Conclusion: The cytotoxicity test and the mechanical experiment provide initial assessments of the proposed PU femoral head cover implant. The evaluation outcomes of this study could serve as a foundation for developing more functional design and testing methods, utilizing numerical simulations, and developing animal/clinical trials in the future.

Anomaly-informed remaining useful life estimation (AIRULE) of bearing machinery using deep learning framework.

A rolling bearing is a crucial element within rotating machinery, and its smooth operation profoundly influences the overall well-being of the equipment. Consequently, analyzing its operational condition is crucial to prevent production losses or, in extreme cases, potential fatalities due to catastrophic failures. Accurate estimates of the Remaining Useful Life (RUL) of rolling bearings ensure manufacturing safety while also leading to cost savings.•This paper proposes an intelligent deep learning-based framework for remaining useful life estimation of bearings on the basis of informed detection of anomalies.•The paper demonstrates the setup of an experimental bearing test rig and the collection of bearing condition monitoring data such as vibration data.•Advanced hybrid models of Encoder-Decoder LSTM demonstrate high forecasting accuracy in RUL estimation.

Ten Year Outcomes of Contemporary Total Hip Arthroplasty in Adolescent and Young Adult Patients are Favorable.

BACKGROUND: The purpose of this study was to assess 10-year patient-reported outcome measures, complications, polyethylene wear-rates, and implant survivorships in patients ≤30 years of age treated with contemporary total hip arthroplasty (THA). METHODS: We retrospectively assessed 121 patients (144 hips) who underwent THA at age ≤30 years (mean 23 [range, 11 to 30]) at an average follow-up duration of 10.7 years (range, 8 to 17). Highly-crosslinked polyethylene acetabular liners were used in all cases. Femoral heads were ceramic (74%) or cobalt-chrome (26%). There were 52 hips (36%) that had previous surgery and 31 hips (22%) were in patients who had associated major systemic comorbidities. We analyzed the modified Harris Hip scores, University of California Los Angeles Activity Scores, major complications, polyethylene wear-rates, and implant survivorships. RESULTS: At final follow-up, the average modified Harris Hip scores improved from 47 (±15.1) to 81 (±19.5) with an average 34-point improvement. The University of California Los Angeles scores improved from 4.0 (±2.3) to 6.0 (±2.4). The major complication rate was 5.6%. There were 6 hips (4.2%) that were revised. Indications for revision included instability (3, 2.1%), late infection (1, 0.7%), liner dissociation (1, 0.7%), and acetabular loosening (1, 0.7%). Mean linear (0.0438 mm/y) and volumetric (29.07 mm3/y) wear rates were low. No periprosthetic osteolysis was detected in any hip. Survivorship free from revision for any reason was 97.2, 95.8, and 95.8% at 5, 10, and 15 years. CONCLUSIONS: Contemporary THA in patients ≤30 years of age is associated with marked clinical improvements at 10-year follow-up and encouraging survivorship estimates at 15 years.

Lower Revision Rates and Improved Stability With a Monoblock Ceramic Acetabular Cup.

BACKGROUND: Monoblock ceramic cups are designed to accommodate large-diameter femoral heads. This has the potential to offer the advantages of an increased range of motion and enhanced joint stability. These features could benefit younger and high-demand patients in need of total hip arthroplasty. The aim of this study was to assess the survival rate and the reasons for revision of the DeltaMotion cup. METHODS: Data from the AOANJRR were analyzed for all patients who had undergone a primary conventional THA performed between January 1, 2001 and December 31, 2021. Only prostheses with ceramic/ceramic, ceramic/XLPE, metal/XLPE, or CM/XLPE bearing surfaces were included. The primary outcome measure was the cumulative percent revision for all causes. Secondary outcome measures were revision for dislocation/instability, ceramic breakage, or noise. A subanalysis for cup size was also performed. RESULTS: There were 486,946 primary conventional THA procedures undertaken for any reason. Of these, 4,033 used the DeltaMotion cup and 482,913 were modular designs. The DeltaMotion cup had the lowest CPR for all diagnoses compared to the modular bearings at all time points, had a significantly lower revision rate for prosthesis dislocation and no revisions for squeaking compared to other modular bearings. There were 175 ceramic breakages recorded in the modular bearing group and 1 ceramic breakage in the DeltaMotion group. CONCLUSIONS: The DeltaMotion cup had a low rate of all-cause revision, and for dislocation, ceramic breakage, and noise. Although this cup is no longer manufactured, ongoing follow-up of newer monoblock ceramic cups will determine their suitability for younger and more active patients.

Fault anomaly detection method of aero-engine rolling bearing based on distillation learning.

In this study, we address the issue of limited generalization capabilities in intelligent diagnosis models caused by the lack of high-quality fault data samples for aero-engine rolling bearings. We provide a fault anomaly detection technique based on distillation learning to address this issue. Two Vision Transformer (ViT) models are specifically used in the distillation learning process, one of which serves as the teacher network and the other as the student network. By using a small-scale student network model, the computational efficiency of the model is increased without sacrificing model accuracy. For feature-centered representation, new loss and anomaly score functions are created, and an enhanced Transformer encoder with the residual block is proposed. Then, a rolling bearing dynamics simulation method is used to obtain rich fault sample data, and the pre-training of the teacher network is completed. For anomaly detection, the training of the student network is completed based on the proposed loss function and the pre-trained teacher network, using only the vibration acceleration samples obtained from the normal state. Finally, the trained completed network and the designed anomaly score function are used to achieve the anomaly detection of rolling bearing faults. The experimental validation was carried out on two sets of test data and one set of real vibration data of a whole aero-engine, and the detection accuracy reached 100 %. The results show that the proposed method has a high capability of rolling bearing fault anomaly detection.

Excellent 20-Year Results of Total Hip Arthroplasty With Highly Cross-Linked Polyethylene on Cobalt-Chromium Femoral Heads in Patients ≤50 Years.

BACKGROUND: Highly cross-linked polyethylene (HXLPE) acetabular bearing surfaces have appeared to offer excellent wear resistance, low incidence of wear-related osteolysis, and high implant survivorship at 10-year to 15-year follow-up. However, concerns over potential performance deterioration at longer-term follow-up remain - particularly in younger patients - and outcome data into the third decade have not been available. METHODS: We retrospectively assessed 62 patients (68 hips) who underwent primary total hip arthroplasty (THA) at age ≤50 years with a single manufacturer's cementless components, remelted HXLPE liner, and small diameter (26 and 28 millimeter) cobalt-chromium (CoCr) femoral heads at minimum 18-year follow-up. We assessed clinical outcomes (modified Harris Hip score, University of California Los Angeles Activity Score, polyethylene wear rates, radiographic findings (osteolysis, component loosening), and implant survivorship. RESULTS: At 20.6-year mean follow-up (range, 18 to 23 years) modified Harris Hip scores for surviving hips remained an average of 41 points above preoperative baseline (49 versus 90, P < .001) and UCLA scores 2.8 points above baseline (3.7 versus 6.4, P < .001). Wear analysis revealed a population linear wear rate of 0.0142 mm/y (standard deviation (SD), 0.0471) and volumetric wear rate of 10.14 mm3/y (SD, 23.41). Acetabular lysis was noted in 2 asymptomatic hips at 16.6 and 18.4 years. No components were radiographically loose. Survivorship free from wear-related revision was 100% at 20 years (97% free from any revision). CONCLUSION: The HXLPE-CoCr bearing couple with small femoral heads continues to be extremely effective 20 years after primary THA in the younger patient population.

Motor current and vibration monitoring dataset for various faults in an E-motor-driven centrifugal pump.

Induction motor driven pumps are a staple in many sectors of industry, and crucial equipment in naval ships. Such machines can suffer from a wide variety of issues, which may cause it to not perform its function. This can either be due to degradation of components over time, or due to incorrect installation or usage. Unexpected failure of the machine causes downtime and lowers the availability. In some cases, it can even lead to collateral damage. To prevent collateral damage and optimise the availability, many asset owners apply condition monitoring, measuring the dynamic response of the system while in operation. Two high-frequency measurement methods are widely accepted for the detection of faults in rotating machinery at an early stage: vibration measurements, and motor current and voltage measurements. These methods can also distinguish between different failure mechanisms and severities. The dataset described in this article presents experimental data of two centrifugal pumps, driven by induction motors through a variable frequency drive. Besides measurements of behaviour that is considered healthy (new bearings, well aligned), the machines have also been subjected to a variety of (simulated) faults. These faults include bearing defects, loose foot, impeller damage, stator winding short, broken rotor bar, soft foot, misalignment, unbalance, coupling degradation, cavitation and bent shaft. Most faults were implemented at multiple levels of severity for multiple motor speeds. Both vibration measurements, and current and voltage measurements were recorded for all cases. The dataset holds value for a wide range of engineers and researchers working on the development and validation of methods for damage detection, identification and diagnostics. Due to the extensive documentation of the presented data, labelling of the data is close to perfect, which makes the data particularly suitable for developing and training machine learning and other AI algorithms.

Experimental Parametric Study and Phenomenological Modeling of a Deformable Rolling Seismic Isolator.

This paper presents an extensive experimental study of a low-cost, high-performance seismic isolator comprising a deformable sphere rolling on concrete surfaces. Polyurethane spheres, with and without steel core, rolling on flat or spherical concrete plates, are investigated. Lateral cyclic tests under large displacements demonstrated a rolling friction coefficient between 3.7% and 7.1%. When tested in a shake table under 1170 ground motions, the isolators substantially reduced the acceleration transmitted to the superstructure (to less than 0.15 g) while maintaining reasonable peak and negligible residual displacements. A phenomenological model was calibrated on the lateral cyclic tests and predicted the shake table tests with reasonable accuracy.