Defects In Components Research Articles

Maintenance Tasks for 20 Kv Medium-Voltage Switchgear Based on Technical Judgment Experience and Manufacturer

A 20 kV medium-voltage switchgear is an important equipment in the electricity system that distributes electricity generated by the 20 kV electric power generator to electricity users. Calor Emag, Merlin Gerin, Alsthom, Fuji, AEG, Schneider, Meidensha, Goldstar, Modalek, Areva, and Siemens are among the companies that make products. This study describes a method for maintaining 20 kV medium-voltage switchgear, an essential equipment in the electricity system. In less technical terms, the failure rate is high at the beginning of an asset's life, which follows the curve of the bathtub. However, the failure rate quickly declines as we identify and discard defective components and eliminate early sources of potential failures, such as installation errors. This study introduces a novel approach by examining the relationship between maintenance tasks, which are based on technical judgment and experiences, and the maintenance tasks provided by the manufacturer for 20 kV medium voltage switchgear. The goal is to develop and deliver the most effective maintenance method for 20 kV medium voltage switchgear, to prevent malfunctions or even catastrophic explosions, while maintaining the lowest possible maintenance costs. Total use of labor over 2 years (24 months) for daily, monthly, 3 monthly, and 2 yearly is 3554-man hours (MH). It provides a comprehensive approach to preventing malfunctions and explosions, resulting in optimal maintenance costs and performance.

Power‐DETR: end‐to‐end power line defect components detection based on contrastive denoising and hybrid label assignment

AbstractMaintenance of power transmission lines is essential for the safe and reliable operation of the power grid. The use of deep learning‐based networks to improve the performance of power line defect detection faces significant challenges, such as small target sizes, shape similarities, and occlusion issues. In response to these challenges, a transformer‐based end‐to‐end power line detection network called Power‐DETR is introduced. Initially, building upon Deformable DETR, a large pre‐trained model (Swin‐large) is utilized to increase the number of multi‐scale features, and activation checkpoint technology is applied to ensure effective training within limited memory capacity. Subsequently, a contrastive denoising training strategy is integrated to combat ambiguity and instability of the Hungarian matching algorithm during training, aiming to expedite model convergence. Additionally, a hybrid label assignment strategy combining OHEM and cost‐based ATSS is proposed to provide the model with high‐quality queries, ensuring adequate training for the decoder and enhancing encoder supervision. Experimental results substantiate the efficacy of the proposed Power‐DETR model as a novel end‐to‐end detection paradigm, surpassing both one‐stage and two‐stage detection models. Furthermore, the model demonstrates a significant 15.7% enhancement in mAP0.5 compared to the baseline.

Treatment strategies for revision surgery of artificial urinary sphincter: A review.

Artificial urinary sphincters (AUS) are an effective treatment for male stress urinary incontinence (SUI). However, infection, erosion, mechanical failure, atrophy, and balloon deterioration cause device malfunction in approximately half of patients by 10 years after implantation. Many patients desire to regain urinary continence and require revision surgery (RS), including device removal and simultaneous or delayed implantation. Patients for whom RS is considered should be examined physically and by interview for signs of infection. Urethral erosion should be assessed using cystoscopy. If there is infection or erosion, all devices should be removed first, and a new device should be implanted several months later. During the RS, after strong adhesion around the urethra, transcorporal cuff implantation is a safe choice. Device removal and simultaneous implantation can be performed in the absence of infection or erosion. If a long time has passed since device implantation, the entire device should be replaced due to device aging and deterioration; however, if the time is short, only the defective component need be replaced. Intraoperative assessment of urethral health is necessary for device removal and implantation. If the urethra is healthy, a new cuff can be placed in the same position as the old cuff was removed from; however, if the urethra is unhealthy, the cuff can be implanted in a more proximal/distal position, or a transcorporal cuff implant may be chosen. This article reviews the literature on diagnostic and treatment strategies for recurrent SUI in male patients with AUS and proposes a flowchart for AUS revision.

Optimal filter design using mountain gazelle optimizer driven by novel sparsity index and its application to fault diagnosis

The informative frequency band (IFB) plays a vital role in detecting defects in complex machinery through visible informative features. In the present work, a denoising filter has been designed to enhance the small non-stationarities present in the signal. Initially, the system impulse is computed to estimate the filter coefficients which are further optimized by the mountain gazelle optimization (MGO) based on the maximum value fitness function. The novel sparsity index based on kurtosis and negentropy (NE) is put forward as the fitness function. Then, optimized coefficients are convolved with the system impulse to design the denoising filter. The efficacy of the designed filter is verified through vibration and acoustic signals from the defective components of the belt conveyor system. The designed filter is better able to extract the impulsiveness from the signal, give improved values of kurtosis and signal-to-noise ratio (SNR), and reduce interferences from other machinery components and the environment simultaneously.

Vacuum Loss Detection of PTC in CSP Plants via Temperature-Sensors

The efficient operation of a solar field is an essential factor for the commercial operation of a concentrating solar power (CSP) plant. In addition to predictive control for the highest possible constant outlet temperature at high mass flow, efficient operation also includes early detection of defective components and heat losses. This work presents a method for non-invasive heat loss detection as a strong indication for vacuum losses, based on measured operational data of Andasol III, an operating 50 MW parabolic trough collector (PTC) plant located in southern Spain. To detect vacuum losses via this method, mass flow rate and temperature reduction are determined separately for each individual loop via the analysis of a short-term temperature rise of the heat transfer fluid (HTF) during preheating. While the temperature reduction was measured directly, the mass flow was determined via the thermal time-of-flight (ToF) method using the same installed temperature sensors. By measuring thermal step responses during the preheating of the solar field at nighttime operation, the influence of fluctuating direct normal irradiance (DNI), misalignment of the absorber tubes and an offset in collector focus was circumvented. In the scope of the presented work, single loops were detected, which show a higher heat loss at lower mass flow rate and therefore have an increased probability of a higher vacuum loss. Better localization and early detection of these vacuum losses would allow the corresponding absorber tubes to be renewed at the economically and environmentally best time, improving the efficiency of the solar field and thus the entire CSP plant.

Психологическая структура дефекта как основание психологической помощи детям с отклонениями в развитии

<p>The concept of “psychological structure of a defect” is analyzed in the context of its use for designing psychological assistance for a child with developmental disabilities. The potential of this concept is revealed for identifying individual characteristics of abnormal mental development. The psychological structure of the defect is defined as a set of nuclear mental anomalies that serve as the basis for the emergence of the entire spectrum of particular manifestations of the child’s abnormal development, as well as the agent’s reaction to them. Two types of personality response are identified – objective and subjective. The first reveals changes in mental functions, the second reveals the subject’s experiences about the defect, including the attitude towards the defect, emotional-sensory and behavioral components. The agent's reaction to the defect reflects the cyclical nature of the formation of the structure of the defect. It can both support the existence of the nuclear defect, generate defects of a higher order (dysfunctional cycle) and facilitate overcoming them (evolutionary cycle).</p>

A Pragmatic Approach for Rapid, Non-Destructive Assessment of Defect Types in Laser Powder Bed Fusion Based on Melt Pool Monitoring Data.

Process monitoring systems, e.g., systems based on photodiodes, could be used in laser-based powder bed fusion (PBF-LB/M) to measure various process parameters and process signatures to eventually allow for a local, detailed analysis of the produced parts. Here, simple statements only concerning the occurrence of defects in parts are sufficient in many cases, especially with respect to industrial application. Therefore, a pragmatic approach to rapidly infer the occurrence of defects and their types based on in situ data obtained by commercially available process monitoring systems is introduced. In this approach, a color distribution in form of a histogram is determined for each produced part using layer-wise screenshots of the visualized data provided by the monitoring software. Assessment of the histograms of AlSi10Mg samples, which were processed with different parameter combinations, revealed characteristics depending on the prevailing defect types. These characteristics enable the prediction of the occurring defect types without the necessity to apply conventional downstream testing methods, and thus, a straightforward separation of parts with good quality from defective components. Since the approach presented uses the data visualization of the monitoring software, it can be used even when direct access to the raw data is not provided by the machine manufacturer.

Interfacial modification strategies to secure phase-stability for inorganic perovskite solar cells

The rapid success achieved from perovskite solar cell has drawn great expectations for commercialization of next-generation photovoltaics. Among the various perovskite materials, the inorganic perovskite derivatives have been of particular interest, ascribed to its superior thermal and chemical stability, which is a crucial criterion for reliable long-term operation. Nonetheless, the development of the efficient inorganic perovskite solar cells has been lagged from its organic–inorganic hybrid counterparts owing to the notorious phase-stability challenges associated with the formation of non-photoactive phases. The early progress of the inorganic perovskite solar cells has been centered on the stable perovskite phase-preparation and leads to the effective bulk management through intermediate engineering and compositional engineering strategies. Yet, challenges remain in securing the as-formed perovskite phase throughout the long-term operation. Accordingly, recent studies find interfacial modification strategies successful by constricting the phase-transformation channels in various perspectives such as defect propagation, strain, component segregation, charge accumulation, and external stresses. In this review, we start with the brief description on the inorganic perovskite solar cells and the associated advantages including chemical and optoelectronic properties. We then provide a review on the challenges of inorganic perovskite solar cells associated with the phase instabilities. We elaborate on the origins of the phase instabilities in terms of thermodynamics and the recently proposed channels including intrinsic factors and extrinsic factors that facilitate the detrimental phase transformation. Finally, we survey the recent successful approaches to stabilize the inorganic perovskite solar cells through interface managements and provide outlook on further progress.

Biomechanical Effects of Stem Extension of Tibial Components for Medial Tibial Bone Defects in Total Knee Arthroplasty: A Finite Element Study.

The aim of this study was to investigate the biomechanical effects of stem extension with a medial tibial bone defect in primary total knee arthroplasty (TKA) on load distribution and stress in the proximal tibia using finite element (FE) analysis.FE simulations were performed on the tibia bone to evaluate the stress and strain on the tibia bone and bone cement. This was done to investigate the stress shielding effect, stability of the tibia plate, and the biomechanical effects in TKA models with various medial defects and different stem length models.The results demonstrated that in the bone defect model, the longer the stem, the lower the average von Mises stress on the cortical and trabecular bones. In particular, as the bone defect increased, the average von Mises stress on cortical and trabecular bones increased. The average increase in stress according to the size of the bone defect was smaller in the long stem than in the short stem. The maximal principal strain on the trabecular bone occurred mainly at the contact point on the distal end of the stem of the tibial implant. When a short stem was applied, the maximal principal strain on the trabecular bone was approximately 8% and 20% smaller than when a long stem was applied or when no stem was applied, respectively.The findings suggest that a short stem extension of the tibial component could help achieve excellent biomechanical results when performing TKA with a medial tibial bone defect.

High-Density Polyethylene Pipe Butt-Fusion Joint Detection via Total Focusing Method and Spatiotemporal Singular Value Decomposition

High-density polyethylene (HDPE) pipes are widely used for urban natural gas transportation. Pipes are usually welded using the technique of thermal butt fusion, which is prone to manufacturing defects that are detrimental to safe operation. This paper proposes a spatiotemporal singular value decomposition preprocessing improved total focusing method (STSVD-ITFM) imaging algorithm combined with ultrasonic phased array technology for non-destructive testing. That is, the ultrasonic real-value signal data are first processed using STSVD filtering, enhancing the spatiotemporal singular values corresponding to the defective signal components. The TFM algorithm is then improved by establishing a composite modification factor based on the directivity function and the corrected energy attenuation factor by adding angle variable. Finally, the filtered signal data are utilized for imaging. Experiments are conducted by examining specimen blocks of HDPE materials with through-hole defects. The results show the following: the STSVD-ITFM algorithm proposed in this paper can better suppress static clutter in the near-field region, and the average signal-to-noise ratios are all higher than the TFM algorithm. Moreover, the STSVD-ITFM algorithm has the smallest average error among all defect depth quantification results.

Enhancing Therapeutic Efficacy in Cancer Treatment: Integrating Nanomedicine with Autophagy Inhibition Strategies.

The complicated stepwise lysosomal degradation process known as autophagy is in charge of destroying and eliminating damaged organelles and defective cytoplasmic components. This mechanism promotes metabolic adaptability and nutrition recycling. Autophagy functions as a quality control mechanism in cells that support homeostasis and redox balance under normal circumstances. However, the role of autophagy in cancer is controversial because, mostly depending on the stage of the tumor, it may either suppress or support the disease. While autophagy delays the onset of tumors and slows the dissemination of cancer in the early stages of tumorigenesis, numerous studies demonstrate that autophagy promotes the development and spread of tumors as well as the evolution and development of resistance to several anticancer drugs in advanced cancer stages. In this Review, we primarily emphasize the therapeutic role of autophagy inhibition in improving the treatment of multiple cancers and give a broad overview of how its inhibition modulates cancer responses. There have been various attempts to inhibit autophagy, including the use of autophagy inhibitor drugs, gene silencing therapy (RNA interference), and nanoparticles. In this Review, all these topics are thoroughly covered and illustrated by recent studies and field investigations.

The Influence of Nonlinear High-Intensity Dynamic Processes on the Standing Wave Precession of a Non-Ideal Hemispherical Resonator.

The properties of small size, low noise, high performance and no wear-out have made the hemispherical resonator gyroscope a good choice for high-value space missions. To enhance the precision of the hemispherical resonator gyroscope for use in tasks with large angular velocities and angular accelerations, this paper investigates the standing wave precession of a non-ideal hemispherical resonator under nonlinear high-intensity dynamic conditions. Based on the thin shell theory of elasticity, a dynamic model of a hemispherical resonator is established by using Lagrange's second kind equation. Then, the dynamic model is equivalently transformed into a simple harmonic vibration model of a point mass in two-dimensional space, which is analyzed using a method of averaging that separates the slow variables from the fast variables. The results reveal that taking the nonlinear terms about the square of the angular velocity and the angular acceleration in the dynamic equation into account can weaken the influence of the 4th harmonic component of a mass defect on standing wave drift, and the extent of this weakening effect varies with the dimensions of the mass defects, which is very important for steering the development of the high-precision hemispherical resonator gyroscope.

Real-Time Defect Detection in Electronic Components during Assembly through Deep Learning

This paper introduces a pioneering method for real-time image processing in electronic component assembly, revolutionizing quality control in manufacturing. By promptly capturing images from pick-and-place machines during the interval between component pick-up and mounting, defects are identified and promptly addressed in line. This proactive approach ensures that defective components are rejected before mounting, effectively preventing issues from ever occurring, thus significantly enhancing efficiency and reliability. Leveraging rapid network protocols such as gRPC and orchestration via Kubernetes, in conjunction with C++ programming and TensorFlow, this approach achieves an impressive average turnaround time of less than 5 milli-seconds. Rigorously tested on 20 operational production machines, it not only ensures adherence to IPC-A-610 and IPC-STD-J-001 standards but also optimizes production efficiency and reliability.

A hybrid fault diagnosis method for rolling bearings based on GGRU-1DCNN with AdaBN algorithm under multiple load conditions

The fault diagnosis of rolling bearings is a critical aspect of rotating machinery, as it significantly contributes to the overall operational safety of the mechanical equipment. In the practical engineering environment, the complex and variable working conditions, along with the presence of overlapping noise, contribute to intricate frequency information in the acquired signals and their highly time-dependent characteristics, which makes it difficult to extract the available fault features hidden in the signal. Based on this, a hybrid fault diagnosis method named GGRU-1DCNN-AdaBN is introduced, which combines improved gap-gated recurrent unit network (GGRU), one-dimensional convolutional neural network (1DCNN), and adaptive batch normalization (AdaBN). The proposed approach involves several parts to enhance fault diagnosis accuracy in vibration signals under constant load conditions and variable load conditions. Firstly, the end-layer structure of the traditional GRU is replaced with a one-dimensional global average pooling layer to aggregate the influence components of defects and reduce model training parameters. Secondly, the fusion of different types of frequency and sequence features is achieved by combining 1DCNN, addressing the limitation of a single network’s feature extraction capability and the loss of temporal features in a cascaded hybrid model. Subsequently, the fused features are input into a softmax multi-classifier to obtain fault type identification results. Lastly, the GGRU-1DCNN method is further improved by incorporating the AdaBN algorithm, enhancing the model’s domain adaptive capability under variable load conditions and noisy environments. The method is validated using datasets obtained from Case Western Reserve University, aero-engine bearings, Xi’an Jiaotong University, and the Changxing Sumyoung Technology. The findings suggest that the proposed method demonstrates superior accuracy and robustness in fault diagnosis, as well as excellent generalization capability and universal applicability.

An empirical study of just-in-time-defect prediction using various machine learning techniques

The goal of defect specificity is to pinpoint defective program components (such as faulty files, problematic procedures, or troublesome lines of code) based on defect symptoms, such as event logs or program spectrum. However, the problem is revealed and adverse effects are introduced when one experiences the defect indications. Consequently, one difficult task is to determine whether one can find buggy program before the signs of the defect manifests itself. Just-in-time defect prediction (JIT-DP) is a particular category of Software Defect Prediction (SDP) that includes this type of early detection of flawed changes to a software programs. This paper presents a novel five-phase JIT-DP framework that classifies the newly committed change as either a buggy change or a clean change relying on the project's historical data regarding the executed changes. The distinctive aspect of the model is that it uses the Chunk Balancing Algorithm (CBA) as a novel way of handling the Class Imbalance Problem (CIP) rather than utilizing conventional data re-sampling techniques. The experimental study uses 10 open-source projects with an average of 23,989 modifications to train the JIT-DP model. The research study examines the JIT-DP model's prediction abilities from both Within Project (WP) and Cross Project (CP) scenarios. The findings show that for within project setup, Logistic Regression (LR) outperforms among all other classifiers with the highest accuracy, TPR, and FPR as 0.85, 0.847, and 0.207, respectively. The model provides comparable prediction findings with an average accuracy of 0.90 for all seven testing samples when it was trained using commit logs from related six Java projects under CP context. However, taking into account the larger training pool of commit records in the former scenario, the model in the CP context (3.740 s) lags behind in terms of training and classification time periods when compared to the results of WI (0.984 s).

Effect of constraint on creep fracture toughness of a Cr-Mo-V steel

The creep fracture toughness of materials is required for the assessment of defective components at high-temperatures. Existing evidence indicates that creep crack-tip constraint state has a great influence on the fracture behavior of materials. In this work, the effect of constraint on creep fracture toughness was explored by conducting a series of creep crack growth tests on specimens with various thicknesses and loading modes. Results showed that the curves of creep fracture toughness with creep crack initiation time on the logarithmic scale for different groups of specimens were almost parallel with one another. Through the characterization of crack-tip constraint level for different groups of specimens in terms of a load-independent creep fracture constraint parameter, R*, the creep fracture toughness was found to increase with the decrease in the crack-tip constraint level. Moreover, evident effect of specimen geometry on creep fracture toughness was observed. Of all types of specimens, the CCT2 and CT10-SG exhibited the highest and lowest values of Kcmat at the same ti, respectively. In addition, the Kcmat was found to increase following a specimen group order of CT10-SG, CT10, CT5, SENT5, CT2, SENT2 and CCT2 at a given ti. Finally, a constraint-dependent creep fracture toughness equation was established for the Cr-Mo-V steel, which is expected for practical applications to improve accuracy of life assessment methods. Follow-up work on life assessment methods incorporating the constraint parameter R* is still necessary.

(114) A Comparative Meta-Analysis of Revision Strategies for Malfunctioning Inflatable Penile Prosthesis: Partial vs Complete Component Replacement With or Without Retained Reservoir

Abstract Introduction In cases of non-infected malfunctioning Inflatable penile prosthesis (IPP) device, surgeon often opt to exchange all the device rather than the defective component for fear of increased infection rate and future mechanical dysfunction. Objective To assess whether partial component exchange of IPP device has comparable outcomes to complete explant and replacement of IPP device with or without retained reservoir. Methods A meta-analysis was completed in line with PRISMA 2020 and AMSTAR guidelines. A comprehensive search was done on MEDLINE (OVID), PubMed, and Cochrane Library electronic databases from their inceptions until May 2023, identifying studies that reported on outcomes and complications of revision surgery for non-infected malfunctioning IPP device. Three groups were compared: the first group underwent a single or two-component exchange, the second group had a complete explant replacement of the entire device, and the third group had replacement of all components with a drained and retained primary reservoir. Results A total of 9 articles were included in the analysis. 12,152 patients had complete replacement of their device, 128 had partial exchange of their device and 151 patients had “drained and retained” reservoir following revision. The mean age was similar among the 3 groups between 62–68 years. The median follows up time ranged between 12 and 79 months. Partial component ex change had a higher rate of mechanical failure 10.3% versus 1.3% and 2.3 % (p=0.03) for complete and “drain and retain” groups respectively. (Figure 1) Similarly, partial component exchange had higher peri-operative complications at 21.2% compared to complete replacement group 9.5% and “drain and retain” group 14.5% (p=0.022). However, the infection rate was similar among the three groups, 7.7%, 2.7% and 3.3% respectively (p= 0.301). (Figure 2) Conclusions Partial component exchange during IPP revision has higher rates of perioperative complication and future mechanical failure but has similar rate of infection compared to complete component replacement with or without “drain and retain” of original reservoir. Disclosure No.

Efficient machine learning-assisted failure analysis method for circuit-level defect prediction

Integral to the success of transistor advancements is the accurate use of failure analysis (FA) which benefits in fine-tuning and optimization of the fabrication processes. However, the chip makers face several FA challenges as device sizes, structure, and material complexities scale dramatically. To sustain manufacturability, one can accelerate defect identification at all steps of the chip processing and design. On the other hand, as technologies scale below the nanometer nodes, devices are more sensitive to unavoidable process-induced variability. Therefore, metallic defects and process-induced variability need to be treated concurrently in the context of chip scaling, while failure diagnostic methods to decouple the effects should be developed. Indeed, the locating a defective component from thousands of circuits in a microchip in the presence of variability is a tedious task. This work shows how the SPICE circuit simulations coupled with machine learning based-physical modeling should be effectively used to tackle such a problem for a 6T-SRAM bit cell. An automatic bridge defect recognition system for such a circuit is devised by training a predictive model on simulation data. For feature descriptors of the model, the symmetry of the circuit and a fundamental material property are leveraged: metals (semiconductors) have a positive (negative) temperature coefficient of resistance up to a certain voltage range. Then, this work successfully demonstrates that how a defective circuit is identified along with its defective component's position with approximately 99.5 % accuracy. This proposed solution should greatly help to accelerate the production process of the integrated circuits.

Updates and Challenges in ENS Cell Therapy for the Treatment of Neurointestinal Diseases.

Neurointestinal diseases represent a significant challenge in clinical management with current palliative approaches failing to overcome disease and treatment-related morbidity. The recent progress with cell therapy to restore missing or defective components of the gut neuromusculature offers new hope for potential cures. This review discusses the progress that has been made in the sourcing of putative stem cells and the studies into their biology and therapeutic potential. We also explore some of the practical challenges that must be overcome before cell-based therapies can be applied in the clinical setting. Although a number of obstacles remain, the rapid advances made in the enteric neural stem cell field suggest that such therapies are on the near horizon.

(391) A Comparative Meta-analysis of Revision Strategies for Malfunctioning Inflatable Penile Prosthesis: Partial Vs Complete Component Replacement With or Without Retained Reservoir

Abstract Introduction In cases of non-infected malfunctioning Inflatable penile prosthesis (IPP) device, surgeon often opt to exchange all the device rather than the defective component for fear of increased infection rate and future mechanical dysfunction. Objective To assess whether partial component exchange of IPP device has comparable outcomes to complete explant and replacement of IPP device with or without retained reservoir. Methods A meta-analysis was completed in line with PRISMA 2020 and AMSTAR guidelines. A comprehensive search was done on MEDLINE (OVID), PubMed, and Cochrane Library electronic databases from their inceptions until May 2023, identifying studies that reported on outcomes and complications of revision surgery for non-infected malfunctioning IPP device. Three groups were compared: the first group underwent a single or two-component exchange, the second group had a complete explant replacement of the entire device, and the third group had replacement of all components with a 'drained and retained' primary reservoir. Results A total of 9 articles were included in the analysis. 12,152 patients had complete replacement of their device, 128 had partial exchange of their device and 151 patients had “drained and retained” reservoir following revision. The mean age was similar among the 3 groups between 62-68 years. The median follow up time ranged between 12 and 79 months. Partial component exchange had a higher rates of mechanical failure 10.3% versus 1.3% and 2.3 % (p=0.03) for complete and “drain and retain” groups respectively. (Figure 1) Similarly, partial component exchange had higher perioperative complications at 21.2% compared to complete replacement group 9.5% and “drain and retain” group 14.5% (p=0.022). However, the infection rate was similar among the three groups, 7.7%, 2.7% and 3.3% respectively (p= 0.301). (Figure 2) Conclusions Partial component exchange during IPP revision has higher rates of perioperative complication and future mechanical failure but has similar rate of infection compared to complete component replacement with or without “drain and retain” of original reservoir. Disclosure No.