Evaluation of Equipment Vulnerability and Potential Shock Hazards

The vulnerability of electric equipment to carbon fibers released from aircraft accidents is investigated and the parameters affecting vulnerability are discussed. The shock hazard for a hypothetical set of accidents is computed

Enhancement in Reliability for Multi-core system consisting of One Instruction Cores

Rapid CMOS device size reduction resulted in billions of transistors on a chip have led to integration of many cores leading to many challenges such as increased power dissipation, thermal dissipation, occurrence of transient faults and permanent faults. The mitigation of transient faults and permanent faults at the core level has become an important design parameter in a multi-core scenario. Core level techniques is a redundancy-based fault mitigation technique that improves the lifetime reliability of multi-core systems. In an asymmetric multi-core system, the smaller cores provide fault tolerance to larger cores is a core level fault mitigation technique that has gained momentum and focus from many researchers. The paper presents an economical, asymmetric multi-core system with one instruction cores (MCSOIC). The term Hardware Cost Estimation signifies power and area estimation for MCS-OIC. In MCSOIC, OIC is a warm standby redundant core. OICs provide functional support to conventional cores for shorter periods of time. To evaluate the idea, different configurations of MCSOIC is synthesized using FPGA and ASIC. The maximum power overhead and maximum area overhead are 0.46% and 11.4% respectively. The behavior of OICs in MCS-OIC is modelled using a One-Shot System (OSS) model for reliability analysis. The model parameters namely, readiness, wakeup probability and start-up-strategy for OSS are mapped to the multi-core systems with OICs. Expressions for system reliability is derived. System reliability is estimated for special cases.Comment: 46 page

Environmental Cleaning Evaluation Project for Rural Tribal Health Center

This Tribal health clinic is located on reservation land and serves a community of approximately 1,550 members located over 64,000 acres located in Nevada and California. The clinic offers general health practice and podiatry at its central clinic with satellite offices located off-reservation offering dentistry, optometry, and behavioral health. The clinic is operated by Tribal staff and partners with the Indian Health Service (IHS) for environmental survey on an annual basis for compliance with Accreditation Association for Ambulatory Health Care (AAAHC) standards. While this clinic only offers outpatient services, the disinfection and cleaning protocols are an important step in limiting cross transmission of illnesses (Protano et al., 2019). Centers for Disease Control and Prevention (CDC) reported about 15% increase in methicillin-resistant Staphylococcus aureus (MRSA) infections in acute care facilities between 2019 and 2020 alone (CDC, 2020), which illustrates the need to evaluate cleaning procedures where possible. Cleaning within the clinic is contracted to a third party for nightly services and is to be carried out during the day by clinic staff. Seen as a deficiency, the clinic was lacking documented cleaning and disinfection procedures and policies. Without documented procedures the clinic needed a way to verify disinfection activities were being performed and that the activities were being completed consistently. It was hypothesized that staff was not correctly disinfecting areas or not disinfecting areas at all, allowing bacterial load to build to harmful levels. Surface testing to determine activities and effectiveness of current cleaning activities was determined to be the best evaluation measure available. Methods for testing included a baseline Adenosine triphosphate (ATP) analysis to measure initial bacterial loads on surfaces. ATP has been shown as a positive correlation to bacterial load based upon plate growth studies (UKUKU et al., 2001). Development of formal disinfection procedures, a training for both medical and housekeeping staff, and periodic ATP testing to verify activities followed the initial baseline measures. The process was originally planned to be a four-month project, but was completed in 7 months, starting in April 2022 and completing in October 2022. Utilization of the ATP testing did identify in the baseline results a problem of insufficient disinfection with a passing rate of only an average of 32% of surfaces. Testing was completed at two separate times and were unannounced to staff. The purpose of the separate times was to compare the disinfection results of the night-time “terminal” cleaning staff and the medical staff between patient cleaning. Training on proper disinfection techniques, universal product adoption, mechanical interventions, and checklists for staff in patient rooms showed progressive improvement throughout the test period with the final week’s ATP swab results returned a passing rate of 89% of surfaces

Lifetime reliability of multi-core systems: modeling and applications.

Huang, Lin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (leaves 218-232).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Preface --- p.1Chapter 1.2 --- Background --- p.5Chapter 1.3 --- Contributions --- p.6Chapter 1.3.1 --- Lifetime Reliability Modeling --- p.6Chapter 1.3.2 --- Simulation Framework --- p.7Chapter 1.3.3 --- Applications --- p.9Chapter 1.4 --- Thesis Outline --- p.10Chapter I --- Modeling --- p.12Chapter 2 --- Lifetime Reliability Modeling --- p.13Chapter 2.1 --- Notation --- p.13Chapter 2.2 --- Assumption --- p.16Chapter 2.3 --- Introduction --- p.16Chapter 2.4 --- Related Work --- p.19Chapter 2.5 --- System Model --- p.21Chapter 2.5.1 --- Reliability of A Surviving Component --- p.22Chapter 2.5.2 --- Reliability of a Hybrid k-out-of-n:G System --- p.26Chapter 2.6 --- Special Cases --- p.31Chapter 2.6.1 --- Case I: Gracefully Degrading System --- p.31Chapter 2.6.2 --- Case II: Standby Redundant System --- p.33Chapter 2.6.3 --- Case III: l-out-of-3:G System with --- p.34Chapter 2.7 --- Numerical Results --- p.37Chapter 2.7.1 --- Experimental Setup --- p.37Chapter 2.7.2 --- Experimental Results and Discussion --- p.40Chapter 2.8 --- Conclusion --- p.43Chapter 2.9 --- Appendix --- p.44Chapter II --- Simulation Framework --- p.47Chapter 3 --- AgeSim: A Simulation Framework --- p.48Chapter 3.1 --- Introduction --- p.48Chapter 3.2 --- Preliminaries and Motivation --- p.51Chapter 3.2.1 --- Prior Work on Lifetime Reliability Analysis of Processor- Based Systems --- p.51Chapter 3.2.2 --- Motivation of This Work --- p.53Chapter 3.3 --- The Proposed Framework --- p.54Chapter 3.4 --- Aging Rate Calculation --- p.57Chapter 3.4.1 --- Lifetime Reliability Calculation --- p.58Chapter 3.4.2 --- Aging Rate Extraction --- p.60Chapter 3.4.3 --- Discussion on Representative Workload --- p.63Chapter 3.4.4 --- Numerical Validation --- p.65Chapter 3.4.5 --- Miscellaneous --- p.66Chapter 3.5 --- Lifetime Reliability Model for MPSoCs with Redundancy --- p.68Chapter 3.6 --- Case Studies --- p.70Chapter 3.6.1 --- Dynamic Voltage and Frequency Scaling --- p.71Chapter 3.6.2 --- Burst Task Arrival --- p.75Chapter 3.6.3 --- Task Allocation on Multi-Core Processors --- p.77Chapter 3.6.4 --- Timeout Policy on Multi-Core Processors with Gracefully Degrading Redundancy --- p.78Chapter 3.7 --- Conclusion --- p.79Chapter 4 --- Evaluating Redundancy Schemes --- p.83Chapter 4.1 --- Introduction --- p.83Chapter 4.2 --- Preliminaries and Motivation --- p.85Chapter 4.2.1 --- Failure Mechanisms --- p.85Chapter 4.2.2 --- Related Work and Motivation --- p.86Chapter 4.3 --- Proposed Analytical Model for the Lifetime Reliability of Proces- sor Cores --- p.88Chapter 4.3.1 --- "Impact of Temperature, Voltage, and Frequency" --- p.88Chapter 4.3.2 --- Impact of Workloads --- p.92Chapter 4.4 --- Lifetime Reliability Analysis for Multi-core Processors with Vari- ous Redundancy Schemes --- p.95Chapter 4.4.1 --- Gracefully Degrading System (GDS) --- p.95Chapter 4.4.2 --- Processor Rotation System (PRS) --- p.97Chapter 4.4.3 --- Standby Redundant System (SRS) --- p.98Chapter 4.4.4 --- Extension to Heterogeneous System --- p.99Chapter 4.5 --- Experimental Methodology --- p.101Chapter 4.5.1 --- Workload Description --- p.102Chapter 4.5.2 --- Temperature Distribution Extraction --- p.102Chapter 4.5.3 --- Reliability Factors --- p.103Chapter 4.6 --- Results and Discussions --- p.103Chapter 4.6.1 --- Wear-out Rate Computation --- p.103Chapter 4.6.2 --- Comparison on Lifetime Reliability --- p.105Chapter 4.6.3 --- Comparison on Performance --- p.110Chapter 4.6.4 --- Comparison on Expected Computation Amount --- p.112Chapter 4.7 --- Conclusion --- p.118Chapter III --- Applications --- p.119Chapter 5 --- Task Allocation and Scheduling for MPSoCs --- p.120Chapter 5.1 --- Introduction --- p.120Chapter 5.2 --- Prior Work and Motivation --- p.122Chapter 5.2.1 --- IC Lifetime Reliability --- p.122Chapter 5.2.2 --- Task Allocation and Scheduling for MPSoC Designs --- p.124Chapter 5.3 --- Proposed Task Allocation and Scheduling Strategy --- p.126Chapter 5.3.1 --- Problem Definition --- p.126Chapter 5.3.2 --- Solution Representation --- p.128Chapter 5.3.3 --- Cost Function --- p.129Chapter 5.3.4 --- Simulated Annealing Process --- p.130Chapter 5.4 --- Lifetime Reliability Computation for MPSoC Embedded Systems --- p.133Chapter 5.5 --- Efficient MPSoC Lifetime Approximation --- p.138Chapter 5.5.1 --- Speedup Technique I - Multiple Periods --- p.139Chapter 5.5.2 --- Speedup Technique II - Steady Temperature --- p.139Chapter 5.5.3 --- Speedup Technique III - Temperature Pre- calculation --- p.140Chapter 5.5.4 --- Speedup Technique IV - Time Slot Quantity Control --- p.144Chapter 5.6 --- Experimental Results --- p.144Chapter 5.6.1 --- Experimental Setup --- p.144Chapter 5.6.2 --- Results and Discussion --- p.146Chapter 5.7 --- Conclusion and Future Work --- p.152Chapter 6 --- Energy-Efficient Task Allocation and Scheduling --- p.154Chapter 6.1 --- Introduction --- p.154Chapter 6.2 --- Preliminaries and Problem Formulation --- p.157Chapter 6.2.1 --- Related Work --- p.157Chapter 6.2.2 --- Problem Formulation --- p.159Chapter 6.3 --- Analytical Models --- p.160Chapter 6.3.1 --- Performance and Energy Models for DVS-Enabled Pro- cessors --- p.160Chapter 6.3.2 --- Lifetime Reliability Model --- p.163Chapter 6.4 --- Proposed Algorithm for Single-Mode Embedded Systems --- p.165Chapter 6.4.1 --- Task Allocation and Scheduling --- p.165Chapter 6.4.2 --- Voltage Assignment for DVS-Enabled Processors --- p.168Chapter 6.5 --- Proposed Algorithm for Multi-Mode Embedded Systems --- p.169Chapter 6.5.1 --- Feasible Solution Set --- p.169Chapter 6.5.2 --- Searching Procedure for a Single Mode --- p.171Chapter 6.5.3 --- Feasible Solution Set Identification --- p.171Chapter 6.5.4 --- Multi-Mode Combination --- p.177Chapter 6.6 --- Experimental Results --- p.178Chapter 6.6.1 --- Experimental Setup --- p.178Chapter 6.6.2 --- Case Study --- p.180Chapter 6.6.3 --- Sensitivity Analysis --- p.181Chapter 6.6.4 --- Extensive Results --- p.183Chapter 6.7 --- Conclusion --- p.185Chapter 7 --- Customer-Aware Task Allocation and Scheduling --- p.186Chapter 7.1 --- Introduction --- p.186Chapter 7.2 --- Prior Work and Problem Formulation --- p.188Chapter 7.2.1 --- Related Work and Motivation --- p.188Chapter 7.2.2 --- Problem Formulation --- p.191Chapter 7.3 --- Proposed Design-Stage Task Allocation and Scheduling --- p.192Chapter 7.3.1 --- Solution Representation and Moves --- p.193Chapter 7.3.2 --- Cost Function --- p.196Chapter 7.3.3 --- Impact of DVFS --- p.198Chapter 7.4 --- Proposed Algorithm for Online Adjustment --- p.200Chapter 7.4.1 --- Reliability Requirement for Online Adjustment --- p.201Chapter 7.4.2 --- Analytical Model --- p.203Chapter 7.4.3 --- Overall Flow --- p.204Chapter 7.5 --- Experimental Results --- p.205Chapter 7.5.1 --- Experimental Setup --- p.205Chapter 7.5.2 --- Results and Discussion --- p.207Chapter 7.6 --- Conclusion --- p.211Chapter 7.7 --- Appendix --- p.211Chapter 8 --- Conclusion and Future Work --- p.214Chapter 8.1 --- Conclusion --- p.214Chapter 8.2 --- Future Work --- p.215Bibliography --- p.23

MODERN LUBRICATING OIL SYSTEMS

TutorialsLubricating oil systems are vital to operation, long-term reliability, and availability of turbomachinery trains (or strings) which are strategic and expensive components of industrial plants. Sometimes, the entities that specify machinery strings for their projects do not completely specify the required features of oil systems. For example, API 614 datasheets that contain only minimum information, such as the type of lube oil pumps and their drivers, the type of coolers, and the required inspection and testing, creates an inadequate basis for oil system design, and time is wasted in obtaining missing or incomplete details. In some situations, the purchasers change or add scope much later into the design, which can negatively impact cost and delivery schedules. Design deficiencies in lubricating oil system results in underperformance and affects reliability, and operability not only of the oil system but also the served equipment string or train. The scope and configuration of the lubricating oil system for a stated application should be jointly evaluated by the vendor having the unit responsibility, the oil system designer, and the specifying machinery engineer. All components of the oil system should be discussed, and the design basis finalized by these parties as much as practically possible before awarding the purchase order