Trends in multiprocessor and distributed operating systems designs

This paper presents an overview of the developments in operating systems technology for distributed computing systems and multiprocessor machines. We focus on those design principles that are now widely accepted as useful design paradigms. Approaches common to distributed and multiprocessor operating systems are identified. Issues discussed include process scheduling and synchronization, load balancing, virtual and shared-memory management and parallel file systems. The task-thread model and the object model of computing are also discussed.     

机载系统故障预测与健康管理验证与评估方法

随着故障预测与健康管理系统研究的不断深入,如何对机载故障预测与健康管理系统进行验证和评估是一个较为棘手的问题。首先,在分析故障预测与健康管理系统设计过程的基础上,指出了存在的突出问题;其次,结合实例给出了3种验证方法,通过比较分析,构建了一种机载燃油管理系统半实物仿真平台;再次,对故障预测与健康管理的性能评价指标进行了分类,并给出了相应的应用范围;最后讨论了故障预测与健康管理有关的标准体系,指出构建故障预测与健康管理标准体系是未来的发展方向。     

Industrial data management strategy towards an SME-oriented PHM

The fourth industrial revolution is derived from advances in digitization and prognostic and health management (PHM) disciplines to make plants smarter and more efficient. However, an adapted approach for data-driven PHM process implementation in small and medium-sized enterprises (SMEs) has not been yet discussed. This research gap is due to the specificities of SMEs and the lack of documentation. In this paper, we examine existing standards for implementing PHM in the industrial field and discuss the limitations within SMEs. Based on that, a novel strategy to implement a data-driven PHM approach in SMEs is proposed. Accordingly, the data management process and the impact of data quality are reviewed to address some critical data problems in SMEs (e.g., data volume and data accuracy). A first set of simulations was carried out to study the impact of the data volume and percentage of missing data on classification problems in PHM. A general model of the evolution of the results accuracy in function of data volume and missing data is then generated, and an economic data volume notion is proposed for data infrastructure resizing. The proposed strategy and the developed models are then applied to the Scoder enterprise, which is a French SME. The feedback on the first results of this application is reported and discussed.     

故障预测与健康状态管理技术综述

故障预测和健康状态管理(PHM)技术是新一代武器系统的先进测试、维修和管理技术,也是一种全面的故障检测、隔离和预测及状态管理技术,正在成为新一代武器系统设计和使用中的一个重要组成部分;论文首先综述了PHM技术的内涵、工作原理以及该技术的功能与作用,然后对PHM技术涉及到的关键技术进行了详细的介绍,最后展望了该技术的发展趋势以及对我国国防工业的借鉴意义。     

Robustness of AI-based prognostic and systems health management

Prognostic and systems Health Management (PHM) is an integral part of a system. It is used for solving reliability problems that often manifest due to complexities in design, manufacturing, operating environment and system maintenance. For safety-critical applications, using a model-based development process for complex systems might not always be ideal but it is equally important to establish the robustness of the solution. The information revolution has allowed data-driven methods to diffuse within this field to construct the requisite process (or system models) to cope with the so-called big data phenomenon. This is supported by large datasets that help machine-learning models achieve impressive accuracy. AI technologies are now being integrated into many PHM related applications including aerospace, automotive, medical robots and even autonomous weapon systems. However, with such rapid growth in complexity and connectivity, a systems’ behaviour is influenced in unforeseen ways by cyberattacks, human errors, working with incorrect or incomplete models and even adversarial phenomena. Many of these models depend on the training data and how well the data represents the test data. These issues require fine-tuning and even retraining the models when there is even a small change in operating conditions or equipment. Yet, there is still ambiguity associated with their implementation, even if the learning algorithms classify accordingly. Uncertainties can lie in any part of the AI-based PHM model, including in the requirements, assumptions, or even in the data used for training and validation. These factors lead to sub-optimal solutions with an open interpretation as to why the requirements have not been met. This warrants the need for achieving a level of robustness in the implemented PHM, which is a challenging task in a machine learning solution.This article aims to present a framework for testing the robustness of AI-based PHM. It reviews some key milestones achieved in the AI research community to deal with three particular issues relevant for AI-based PHM in safety-critical applications: robustness to model errors, robustness to unknown phenomena and empirical evaluation of robustness during deployment. To deal with model errors, many techniques from probabilistic inference and robust optimisation are often used to provide some robustness guarantee metric. In the case of unknown phenomena, techniques include anomaly detection methods, using causal models, the construction of ensembles and reinforcement learning. It elicits from the authors’ work on fault diagnostics and robust optimisation via machine learning techniques to offer guidelines to the PHM research community. Finally, challenges and future directions are also examined; on how to better cope with any uncertainties as they appear during the operating life of an asset.     

Resource management framework for collaborative computing systems over multiple virtual machines

A resource management framework for collaborative computing systems over multiple virtual machines (CCSMVM) is presented to increase the performance of computing systems by improving the resource utilization, which has constructed a scalable computing environment for resource on-demand utilization. We design a resource management framework based on the advantages of some components in grid computing platform, virtualized platform and cloud computing platform to reduce computing systems overheads and maintain workloads balancing with the supporting of virtual appliance, Xen API, applications virtualization and so on. The content of collaborate computing, the basis of virtualized resource management and some key technologies including resource planning, resource allocation, resource adjustment and resource release and collaborative computing scheduling are designed in detail. A prototype is designed, and some experiments have verified the correctness and feasibility of our prototype. System evaluations show that the time in resource allocation and resource release is proportional to the quantity of virtual machines, but not the time in the virtual machines migrations. CCSMVM has higher CPU utilization and better performance than other systems, such as Eucalyptus 2.0, Globus4.0, et al. It is concluded that CCSMVM can accelerate the execution of systems by improving average CPU utilization from the results of comparative analysis with other systems, so it is better than others. Our study on resource management framework has some significance to the optimization of the performance in virtual computing systems.     

新一代装备的预测与健康状态管理技术

预测和健康状态管理（PHM）技术是一种先进测试、维修技术,更是一种全面的故障检测、隔离和预测及状态管理技术。PHM技术正在成为新一代武器装备设计和使用中的一个重要组成部分。论文首先综述了PHM技术的内涵、功能、结构和工作原理,并对PHM技术涉及到的关键技术进行了详细的介绍,然后介绍了PHM技术的应用现状,最后展望了该技术的发展趋势以及对我国国防工业的借鉴意义。     

基于云边协同的新一代机载预测及健康管理架构设计

为适应未来战争节奏快、战场环境复杂以及下一代制空作战装备远航久航等新特点,提出了下一代制空作战装备机载故障预测与健康管理（PHM）的新需求,即关键故障精准预测、安全状态全面感知、辅助任务决策快速生成。而目前传统机载PHM由于规划功能有限、数据传输能力和计算资源不足等问题无法满足新需求。云边协同是一种可以支撑开放式统一系统架构的典型方法,符合下一代制空作战装备机载架构的发展趋势,并且可以有效减少机载健康数据传输并可合理地动态分配计算资源。在传统机载PHM架构的基础上,提出了基于云边协同的新一代机载PHM功能架构及物理架构,并阐述关键功能运行逻辑,为新一代机载PHM架构设计提供了一个可参考的技术思路。     

Maintenance scheduling and production control of multiple-machine manufacturing systems

This paper deals with the production and preventive maintenance control problem for a multiple-machine manufacturing system. The objective of such a problem is to find the production and preventive maintenance rates for the machines so as to minimize the total cost of inventory/backlog, repair and preventive maintenance. A two-level hierarchical control model is presented, and the structure of the control policy for both identical and non-identical manufacturing systems is described using parameters, referred to here as input factors. By combining analytical formalism with simulation-based statistical tools such as experimental design and response surface methodology, an approximation of the optimal control policies and values of input factors are determined. The results obtained extend those available in existing literature to cover non-identical machine manufacturing systems. A numerical example and a sensitivity analysis are presented in order to illustrate the robustness of the proposed approach. The extension of the proposed production and preventive maintenance policies to cover large systems (multiple machines, multiple products) is discussed.     

Characterizations of some classes of spiking neural P systems

We look at the recently introduced neural-like systems, called SN P systems. These systems incorporate the ideas of spiking neurons into membrane computing. We study various classes and characterize their computing power and complexity. In particular, we analyze asynchronous and sequential SN P systems and present some conditions under which they become (non-)universal. The non-universal variants are characterized by monotonic counter machines and partially blind counter machines and, hence, have many decidable properties. We also investigate the language-generating capability of SN P systems.     

应用于PHM系统的相近粗糙集决策模型研究

PHM(Prognostic and Health Management)是现代军事和工业生产领域设备实现自主维护与保障的关键技术。文章通过对相近粗糙集理论进行分析,构建一个可以应用于PHM系统的决策模型。以某歼击机的故障飞行信息作为测试数据,在加入噪声干扰和不加噪声干扰的条件下,对经典粗糙集理论,可变精度粗糙集理论以及相近粗糙集理论的决策结果进行比较。结果表明在保持分类性能不变的条件下,相近粗糙集理论对含有噪声系统的适应性要好于经典粗糙集理论和可变精度的粗糙集理论,适合运用于PHM系统的决策算法。     

OntoProg: An ontology-based model for implementing Prognostics Health Management in mechanical machines

Trends in Prognostics Health Management (PHM) have been introduced into mechanical items of manufacturing systems to predict Remaining Useful Life (RUL). PHM as an estimate of the RUL allows Condition-based Maintenance (CBM) before a functional failure occurs, avoiding corrective maintenance that generates unnecessary costs on production lines. An important factor for the implementation of PHM is the correct data collection for monitoring a machine’s health, in order to evaluate its reliability. Data collection, besides providing information about the state of degradation of the machine, also assists in the analysis of failures for intelligent interventions. Thus, the present work proposes the construction of an ontological model for future applications such as expert system in the support in the correct decision-making, besides assisting in the implementation of the PHM in several manufacturing scenarios, to be used in the future by web semantics tools focused on intelligent manufacturing, standardizing its concepts, terms, and the form of collection and processing of data. The methodological approach Design Science Research (DSR) is used to guide the development of this study. The model construction is achieved using the ontology development 101 procedure. The main result is the creation of the ontological model called OntoProg, which presents: a generic ontology addressing by international standards, capable of being used in several types of mechanical machines, of different types of manufacturing, the possibility of storing the knowledge contained in events of real activities that allow through consultations in SPARQL for decision-making which enable timely interventions of maintenance in the equipment of a real industry. The limitation of the work is that said model can be implemented only by specialists who have knowledge in ontology.     

Stakeholder-oriented systematic design methodology for prognostic and health management system: Stakeholder expectation definition

Prognostic and health management (PHM) describes a set of capabilities that enable to detect anomalies, diagnose faults and predict remaining useful lifetime (RUL), leading to the effective and efficient maintenance and operation of assets such as aircraft. Prior research has considered the methodological factors of PHM system design, but typically, only one or a few aspects are addressed. For example, several studies address system engineering (SE) principles for application towards PHM design methodology, and a concept of requirements from a theoretical standpoint, while other papers present requirement specification and flow-down approaches for PHM systems. However, the state of the art lacks a systematic methodology that formulates all aspects of designing and comprehensively engineering a PHM system. Meanwhile, the process and specific implementation of capturing stakeholders’ expectations and requirements are usually lacking details. To overcome these drawbacks, this paper proposes a stakeholder-oriented design methodology for developing a PHM system from a systems engineering perspective, contributing to a consistent and reusable representation of the design. Further, it emphasizes the process and deployment of stakeholder expectations definition in detail, involving the steps of identifying stakeholders, capture their expectations/requirements, and stakeholder and requirement analysis. Two case studies illustrate the applicability of the proposed methodology. The proposed stakeholder-oriented design methodology enables the integration of the bespoke main tasks to design a PHM system, in which sufficient stakeholder involvement and consideration of their interests can lead to more precise and better design information. Moreover, the methodology comprehensively covers the aspects of traceability, consistency, and reusability to capture and define stakeholders and their expectations for a successful design.     

基于视情维修的电子装备PHM体系结构分析

为推行自主式保障理念,提高电子装备的综合保障能力,提出了一种基于视情维修的电子装备预测与健康管理(Prognostics and Health Management,PHM)构建方案。在分析PHM内涵和功能的基础上,从数据信息维、模型维和生命周期维3个维度研究了PHM系统结构,采用开放式PHM架构方式,建立了基于视情维修的电子装备PHM体系结构,并对其系统实现技术进行了探讨。通过对某型装备模拟训练与仿真测试系统PHM策略的设计,验证了所提方案的有效性。     

Theoretical advances in artificial immune systems

Artificial immune systems (AIS) constitute a relatively new area of bio-inspired computing. Biological models of the natural immune system, in particular the theories of clonal selection, immune networks and negative selection, have provided the inspiration for AIS algorithms. Moreover, such algorithms have been successfully employed in a wide variety of different application areas. However, despite these practical successes, until recently there has been a dearth of theory to justify their use. In this paper, the existing theoretical work on AIS is reviewed. After the presentation of a simple example of each of the three main types of AIS algorithm (that is, clonal selection, immune network and negative selection algorithms respectively), details of the theoretical analysis for each of these types are given. Some of the future challenges in this area are also highlighted.     

Deployment of real-time systems in the cloud environment

Interest in real-time systems has grown considerably over recent years, primarily due to significant increase in the use of smart technologies and latency-sensitive applications such as cloud gaming, audio/video streaming, and smart homes. Significant work has been done on resource mapping in the cloud environment, and a number of promising results have been established accordingly where the focus is mainly on resource provisioning. However, the applicability of cloud computing services for real-time systems generated from smart systems is still in its infancy and remains unexplored, relatively. To address this gap, we propose a model for the smart systems that periodically offload computational workload to the cloud environment where virtual machines are allocated according to rate-monotonic scheduling policy to ensure requests are processed within the associated deadlines. Deadlines of tasks have been relaxed to improve server utilization as well as maintain a level of confidence in the timing constrains. Experimental results are discussed to highlight the applicability of static priority assignment for the workload in the context of virtual machines allocation.

     

New perspectives for the future interoperable enterprise systems

The rapid changes in today's socio-economic and technological environment in which the enterprises operate necessitate the identification of new requirements that address both theoretical and practical aspects of the Enterprise Information Systems (EIS). Such an evolving environment contributes to both the process and the system complexity which cannot be handled by the traditional architectures. The constant pressure of requirements for more data, more collaboration and more flexibility motivates us to discuss about the concept of Next Generation EIS (NG EIS) which is federated, omnipresent, model-driven, open, reconfigurable and aware. All these properties imply that the future enterprise system is inherently interoperable. This position paper presents the discussion that spans several research challenges of future interoperable enterprise systems, specialized from the existing general research priorities and directions of IFAC Technical Committee 5.3,¹ namely: context-aware systems, semantic interoperability, cyber-physical systems, cloud-based systems and interoperability assessment.     

航空装备预测与健康管理系统的验证方法概述

随着国内PHM(预测与健康管理)技术发展迅速,航空装备PHM验证与确认方法成为制约PHM技术成果工程转化的难点.针对上述问题,提出了PHM系统全寿命周期内的验证流程,明确了在需求分析、设计研制、使用与维护等阶段PHM验证与确认的具体内容与要求,结合PHM对象的特点,阐述了目前典型的航电PHM、机电/动力PHM、结构SHM的验证和确认方法.     

A metamodel for cyber-physical systems

With the advent of the Internet of Things and Industry 4.0 concepts, cyber-physical systems in civil engineering experience an increasing impact on structural health monitoring (SHM) and control applications. Designing, optimizing, and documenting cyber-physical system on a formal basis require platform-independent and technology-independent metamodels. This study, with emphasis on communication in cyber-physical systems, presents a metamodel for describing cyber-physical systems. First, metamodeling concepts commonly used in computing in civil engineering are reviewed and possibilities and limitations of describing communication-related information are discussed. Next, communication-related properties and behavior of distributed cyber-physical systems applied for SHM and control are explained, and system components relevant to communication are specified. Then, the metamodel to formally describe cyber-physical systems is proposed and mapped into the Industry Foundation Classes (IFC), an open international standard for building information modeling (BIM). Finally, the IFC-based approach is verified using software of the official IFC certification program, and it is validated by BIM-based example modeling of a prototype cyber-physical system, which is physically implemented in the laboratory. As a result, cyber-physical systems applied for SHM and control are described and the information is stored, documented, and exchanged on the formal basis of IFC, facilitating design, optimization, and documentation of cyber-physical systems.