Programmable Electronic Mining Systems: Best Practice Recommendations (In Nine Parts) - Part 6: 5.1 System Safety Guidance

This report (System Safety Guidance 5.1) is the sixth in a nine-part series of recommendations and guidance addressing the functional safety of processor-controlled mining equipment. It is part of a risk-based system safety process encompassing hardware, software, humans, and the operating environment for the equipment s life cycle. Figure 1 shows a safety framework containing these recommendations. The reports in this series address the various lifecycle stages of inception, design, approval and certification, commissioning, operation, maintenance, and decommissioning. These recommendations were developed as a joint project between the National Institute for Occupational Safety and Health and the Mine Safety and Health Administration. They are intended for use by mining companies, original equipment manufacturers, and aftermarket suppliers to these mining companies. Users of these reports are expected to consider the set in total during the design cycle. 1.0 Safety Introduction (Part 1). This is an introductory report for the general mining industry. It provides basic system/software safety concepts, discusses the need for mining to address the functional safety of programmable electronics (PE), and includes the benefits of implementing system/software safety program. 2.1 System Safety (Part 2) and 2.2 Software Safety (Part 3). These reports draw heavily from International Electrotechnical Commission (IEC) standard IEC 61508 [IEC 1998a,b,c,d,e,f,g]and other standards. The scope is surface and underground safety-related mining systems employing embedded, networked, and nonnetworked programmable electronics. System safety seeks to design safety into all phases of the entire system. Software is a subsystem; thus, software safety is a part of the system s safety. 3.0 Safety File (Part 4). This report contains the documentation that demonstrates the level of safety built into the system and identifies limitations for the system s use and operation. In essence, it is a proof of safety that the system and its operation meet the appropriate level of safety for the intended application. It starts from the beginning of the design, is maintained during the full life cycle of the system, and provides administrative support for the safety program of the full system. 4.0 Safety Assessment (Part 5). The independent assessment of the safety file is addressed. It establishes consistent methods to determine the completeness and suitability of safety evidence and justifications. This assessment could be conducted by an independent third party. Safety Framework Guidance. It is intended to supplement the safety framework reports with guidance providing users with additional information. The purpose is to assist users in applying the concepts presented. In other words, the safety framework is what needs to be done and the guidance is how it can be done. The guidance information reinforces the concepts, describes various methodologies that can be used, and gives examples and references. It also gives information on the benefits and drawbacks of various methodologies. The guidance reports are not intended to promote a single methodology or to be an exhaustive treatment of the subject material. They provide information and references so that the user can more intelligently choose and implement the appropriate methodologies given the user s application and capabilities. The guidance reports comprise parts 6 through 9 of the series and are listed below: [< 5.1 System Safety Guidance (Part 6). This guidance supplements 2.1 System Safety. < 5.2 Software Safety Guidance (Part 7). This guidance supplements 2.2 Software Safety. < 6.0 Safety File Guidance (Part 8). This guidance supplements 3.0 Safety File. < 7.0 Independent Functional Safety Assessment Guidance (Part 9). This guidance supplements 4.0 Independent Functional Safety Assessment.] [

Software Reliability Growth Models from the Perspective of Learning Effects and Change-Point.

Increased attention towards reliability of software systems has led to the thorough analysis of the process of reliability growth for prediction and assessment of software reliability in the testing or debugging phase. With many frameworks available in terms of the underlying probability distributions like Poisson process, Non-Homogeneous Poisson Process (NHPP), Weibull, etc, many researchers have developed models using the Non-Homogeneous Poisson Process (NHPP) analytical framework. The behavior of interest, usually, is S-shaped or exponential shaped. S-shaped behavior could relate more closely to the human learning. The need to develop different models stems from the fact that nature of the underlying environment, learning effect acquisition during testing, resource allocations, application and the failure data itself vary. There is no universal model that fits everywhere to be called an Oracle. Learning effects that stem from the experiences of the testing or debugging staff have been considered for the growth of reliability. Learning varies over time and this asserts need for conduct of more research for study of learning effects.Digital copy of ThesisUniversity of Kashmi

MICROELECTRONICS PACKAGING TECHNOLOGY ROADMAPS, ASSEMBLY RELIABILITY, AND PROGNOSTICS

This paper reviews the industry roadmaps on commercial-off-the shelf (COTS) microelectronics packaging technologies covering the current trends toward further reducing size and increasing functionality. Due tothe breadth of work being performed in this field, this paper presents only a number of key packaging technologies. The topics for each category were down-selected by reviewing reports of industry roadmaps including the International Technology Roadmap for Semiconductor (ITRS) and by surveying publications of the International Electronics Manufacturing Initiative (iNEMI) and the roadmap of association connecting electronics industry (IPC). The paper also summarizes the findings of numerous articles and websites that allotted to the emerging and trends in microelectronics packaging technologies. A brief discussion was presented on packaging hierarchy from die to package and to system levels. Key elements of reliability for packaging assemblies were presented followed by reliabilty definition from a probablistic failure perspective. An example was present for showing conventional reliability approach using Monte Carlo simulation results for a number of plastic ball grid array (PBGA). The simulation results were compared to experimental thermal cycle test data. Prognostic health monitoring (PHM) methods, a growing field for microelectronics packaging technologies, were briefly discussed. The artificial neural network (ANN), a data-driven PHM, was discussed in details. Finally, it presented inter- and extra-polations using ANN simulation for thermal cycle test data of PBGA and ceramic BGA (CBGA) assemblies

Topology control and data handling in wireless sensor networks

Our work in this thesis have provided two distinctive contributions to WSNs in the areas of data handling and topology control. In the area of data handling, we have demonstrated a solution to improve the power efficiency whilst preserving the important data features by data compression and the use of an adaptive sampling strategy, which are applicable to the specific application for oceanography monitoring required by the SECOAS project. Our work on oceanographic data analysis is important for the understanding of the data we are dealing with, such that suitable strategies can be deployed and system performance can be analysed. The Basic Adaptive Sampling Scheduler (BASS) algorithm uses the statistics of the data to adjust the sampling behaviour in a sensor node according to the environment in order to conserve energy and minimise detection delay. The motivation of topology control (TC) is to maintain the connectivity of the network, to reduce node degree to ease congestion in a collision-based medium access scheme; and to reduce power consumption in the sensor nodes. We have developed an algorithm Subgraph Topology Control (STC) that is distributed and does not require additional equipment to be implemented on the SECOAS nodes. STC uses a metric called subgraph number, which measures the 2-hops connectivity in the neighbourhood of a node. It is found that STC consistently forms topologies that have lower node degrees and higher probabilities of connectivity, as compared to k-Neighbours, an alternative algorithm that does not rely on special hardware on sensor node. Moreover, STC also gives better results in terms of the minimum degree in the network, which implies that the network structure is more robust to a single point of failure. As STC is an iterative algorithm, it is very scalable and adaptive and is well suited for the SECOAS applications

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen