Acute Kidney Injury in Monoclonal Gammopathies

Monoclonal gammopathies (MG) encompass a variety of disorders related to clonal expansion and/or malignant transformation of B lymphocytes. Deposition of free immunoglobulin (Ig) components (light or heavy chains, LC/HC) within the kidney during MG may result over time in multiple types and degrees of injury, including acute kidney injury (AKI). AKI is generally a consequence of tubular obstruction by luminal aggregates of LC, a pattern known as “cast nephropathy”. Monoclonal Ig LC can also be found as intracellular crystals in glomerular podocytes or proximal tubular cells. Proliferative glomerulonephritis with monoclonal Ig deposits is another, less frequent form of kidney injury with a sizable impact on renal function. Hypercalcemia (in turn related to bone reabsorption triggered by proliferating plasmacytoid B cells) may lead to AKI via functional mechanisms. Pharmacologic treatment of MG may also result in additional renal injury due to local toxicity or the tumor lysis syndrome. The present review focuses on AKI complicating MG, evaluating predictors, risk factors, mechanisms of damage, prognosis, and options for treatment.

Online Prognosis of Bimodal Crack Evolution for Fatigue Life Prediction of Composite Laminates Using Particle Filters

Composite materials are extensively used in aircraft structures, wherein they are subjected to cyclic loads and subsequently impact-induced damages. Progressive fatigue degradation can lead to catastrophic failure. This highlights the need for an efficient prognostic framework to predict crack propagation in the field of structural health monitoring (SHM) of composite structures to improve functional safety and reliability. However, achieving good accuracy in crack growth prediction is challenging due to uncertainties in the material properties, loading conditions, and environmental factors. This paper presents a particle-filter-based online prognostic framework for damage prognosis of composite laminates due to crack-induced delamination and fiber breakage. An optimized Paris law model is used to describe the damage propagation in glass-fiber-reinforced polymer (GFRP) laminates subject to low-velocity impacts. Our proposed methodology deduces the jump energy/inflection point online wherein the damage growth switches from rapid degradation to slow degradation. The prediction results obtained are compared with the conventional Paris law model to validate the need for an optimized bimodal crack growth propagation model. The root mean square error (RMSE) and remaining useful life (RUL) prediction errors are used as the prognostic metrics.

Digital twin approach for intelligent operation planning and health management of mechanical systems

The digital twin paradigm aims to fuse information obtained from sensor data, physics models, and operational data for a mechanical component in use to make well-informed decisions regarding health management and operations of the component. In this work, we discuss a methodology for digital-twin-based operation planning in mechanical systems to enable: a) cost-effective maintenance scheduling, and b) resilient operations of the system. As properties of mechanical systems, as well as their operational parameters, loads and environment are stochastic in nature, our methodology includes probabilistic damage diagnosis, probabilistic damage prognosis, and system optimization under uncertainty. As an illustrative example, we consider the problem of fatigue crack growth in a metal component. We discuss a probabilistic, ultrasonic-guided-wave-based crack diagnosis framework that can handle both aleatory and epistemic uncertainties in the diagnosis process. We build a high-fidelity, finite element model to simulate the piezoelectric effect and ultrasonic guided wave propagation. We use test data obtained by conducting diagnostic experiments on the physical twin to calibrate the error in the diagnosis model. We perform Bayesian diagnosis of crack growth using the corrected diagnosis model, considering data corrupted by measurement noise, and fuse the information from multiple sensors. We build a finite-element-based high-fidelity model for crack growth under uniaxial cyclic loading, and calibrate a phenomenological (low-fidelity) fatigue crack growth model using the high-fidelity model output as well as data from fatigue loading experiments performed on the physical twin. We use the resulting multi-fidelity model in a probabilistic crack growth prognosis framework; thus achieving both accuracy and computational efficiency. Lastly, we utilize the damage diagnosis framework along with the damage prognosis model to optimize system operations under diagnostic and prognostic uncertainty. We perform simulation as well as laboratory experiments that show how the digital twin of the component of interest can be used for intelligent health management and operation planning for mechanical systems.

A data-driven method for predicting structural degradation using a piezoceramic array

There is a growing use of carbon fiber reinforced polymers (CFRPs) in modern airframes with still a limited understanding of the in-service behavioral characteristics of these structures.Structural Health Monitoring (SHM) technologies that use surface-bonded piezoceramic (PZT) transducers to generate and measure guided waves within these structures have demonstrated promising damage detection and localization results and potential for data gathering in data-driven damage prognosis. This paper investigates the development of a data-driven SHM based damage prognosis system for estimating remaining useful life (RUL) of CFRP coupons following damage initiation. A robust and realistic laboratory data gathering methodology is introduced as a building block for evaluating the feasibility of data-driven damage prognosis for in-service aerospace structures. Data are gathered using a PZT-based SHM system. Using the gathered raw guided wave signals, a number of time and frequency domain features are first extracted which are derived from existing damage imaging and detection algorithms. Then, using various combinations of the feature sets as inputs to generic data mining algorithms, the paper presents estimates of the predicted RUL against actual damage diameter progression.