New Damage Accumulation Model for Spall Propagation Mechanism in Bearing Raceways.

The aim of this study was to investigate the spall propagation mechanism in ball bearing raceways using physics-based models. Spalling is one of the most common types of bearing failures that can lead to catastrophic failure. This research takes a step forward toward developing a prognostic tool for ball bearings. It is first necessary to understand the spall progression process in order to formulate a constitutive law of spall deterioration and to estimate the amount of remaining useful life. Fragment formation in the vicinity of the spall edge was found to consist of surface and sub-surface cracks that eventually coalesce, and a fragment is released from the raceway, based on naturally-developed spalls. Here, we describe a physics-based model, integrating a dynamic model with a finite element one to simulate this process. A continuum damage mechanics (CDM) approach and fracture mechanics tools were embedded into the finite element model to simulate the damage propagation. The formation of cracks in the vicinity of the spall (surface and sub-surface cracks) were studied using this effective stress CDM model, and the propagation of the cracks was examined using two approaches: a fracture mechanics approach and an accumulated inelastic hysteresis energy CDM approach. The latter also predicts the overall process of a single fragment release. The simulation results of the spall propagation models are supported by experimental results of spalls from both laboratory experimental bearings and an in-service Sikorsky CH-53 helicopter swashplate bearing. The results obtained show that the impact of the ball on the spall edge affects the crack propagation and the appearance of the surface and sub-surface cracks. Both release the residual stresses and cause crack propagation until a fragment is released.

Neurodevelopmental outcomes in a cohort of children with congenital Zika syndrome at 12 and 24 months of age.

BACKGROUND: Early child development is a critical stage of life that influences social, educational and health outcomes worldwide. A few years after Zika epidemic, families of children born with congenital Zika syndrome (CZS) continue to face uncertainties when it comes to the development of their children. The present study sought to analyse the developmental trajectories of a subset of children born with CZS in the first 24 months of life. METHODS: Thirty-five children with CZS were assessed with the Bayley-III Scales at 12 and 24 months of age from November 2016 to December 2018 in a rehabilitation centre in Brazil. Inclusion criteria included children with established diagnosis of CZS. Exclusion criteria included the presence of arthrogryposis, prematurity, irregular follow-up, clinical complications or other causes of microcephaly. Children born with CZS who evolved with cerebral palsy (CP) were classified according to the Gross Motor Function Classification System (GMFCS) at 2 years of age. RESULTS: At 12 months of age mean composite scores on the Bayley cognitive, communication and motor scores were 57.71 (SD 7.11), 57.94 (SD 14.34) and 49.26 (7.20), respectively. At 24 months of age, composite scores were 57.43 (SD 7.11), 53.60 (SD 12.29) and 48.83 (7.76). In addition, 31 (88.57%) out of 34 children diagnosed with CP were classified as GMFCS levels IV and V. CONCLUSION: Zika virus congenital infection is a risk factor for functional impairments across all developmental domains having a direct and substantial negative impact in early child development.

Pedigree-Based Description of Three Traditional Hungarian Horse Breeds.

The Mezohegyes Stud was founded in 1784 where three different horse breeds were developed: the Furioso-North Star, the Gidran, and the Nonius. These breeds were based on the same mare population, but each breed had different utilization purposes. Our aim was to analyze the pedigree information of these three indigenous breeds. The genealogical information was traced back from the actual breeding population back to the founder animals, and the final database contained more than 47,000 horses. The reference populations were defined as the registered breeding animals in 2019. The complete generation equivalent was 16.45 for the Gidran breed, 15.18 for Furioso-North Star, and 12.64 for Nonius, respectively. Due to the utilization of English Thoroughbred during the breeding history, the average maximum generations were close to 36 generations for each breed. The average relatedness was approximately 4%. The average Wright's inbreeding coefficient was the highest for the Nonius breed (5.59%). Kalinowski's decomposition of inbreeding showed that inbreeding is originated mainly from the past; the current fixation of alleles was higher for the Nonius horse breed. There was a reasonable bottleneck effect for each breed. The estimated effective population sizes suggest that there is no problem with the maintaining of Mezohegyes horse breeds.

Transfer Across Different Machines by Transfer Function Estimation.

A digital twin is a promising evolving tool for prognostic health monitoring. However, in rotating machinery, the transfer function between the rotating components and the sensor distorts the vibration signal, hence, complicating the ability to apply a digital twin to new systems. This paper demonstrates the importance of estimating the transfer function for a successful transfer across different machines (TDM). Furthermore, there are few algorithms in the literature for transfer function estimation. The current algorithms can estimate the magnitude of the transfer function without its original phase. In this study, a new approach is presented that enables the estimation of the transfer function with its phase for a gear signal. The performance of the new algorithm is demonstrated by measured signals and by a simulated transfer function.

Experimental Investigation of the Spall Propagation Mechanism in Bearing Raceways.

This article investigates the spall propagation mechanism for ball bearing raceways by focusing on an experimental investigation of cracks that evolve in the vicinity of the spall edge. Understanding the spall propagation mechanism is an important step towards developing a physics-based prognostic tool for ball bearings. This research reflects an investigation of different spall sizes that propagate naturally both in laboratory experiments and in the field. By using a combined model of a rigid body dynamic model and a finite element model that simulates the rolling element-spall edge interaction, our results shed light on the material behavior (displacements, strains, and stresses) that creates an environment for crack formation and propagation. With the support of the experimental results and the rolling element-spall edge interaction model results, three stages of the mechanism that control fragment release from the raceway were identified. In Stage one, sub-surface cracks appear underneath the spall trailing edge. In Stage two, cracks appear in front of the trailing edge of the spall and, in Stage three, the cracks propagate until a fragment is released from the raceway. These stages were observed in all the tested bearings. In addition, other phenomena that affect the propagation of the cracks and the geometry of the fragment were observed, such as blistering and plastic deformation. We include an explanation of what determines the shape of the fragments.

Probability-Based Algorithm for Bearing Diagnosis with Untrained Spall Sizes.

Bearing spall detection and predicting its size are great challenges. Model-based simulation is a well-known traditional approach to physically model the influence of the spall on the bearing. Building a physical model is challenging due to the bearing complexity and the expert knowledge required to build such a model. Obviously, building a partial physical model for some of the spall sizes is easier. In this paper, we propose a machine-learning algorithm, called Probability-Based Forest, that uses a partial physical model. First, the behavior of some of the spall sizes is physically modeled and a simulator based on this model generates scenarios for these spall sizes in different conditions. Then, the machine-learning algorithm trains these scenarios to generate a prediction model of spall sizes even for those that have not been modeled by the physical model. Feature extraction is a key factor in the success of this approach. We extract features using two traditional approaches: statistical and physical, and an additional new approach: Time Series FeatuRe Extraction based on Scalable Hypothesis tests (TSFRESH). Experimental evaluation with well-known physical model shows that our approach achieves high accuracy, even in cases that have not been modeled by the physical model. Also, we show that the TSFRESH feature-extraction approach achieves the highest accuracy.