Estimating a Parametric Component Lifetime Distribution from a Collection of Superimposed Renewal Processes

Maintenance data can be used to make inferences about the lifetime distribution of system components. Typically, a fleet contains multiple systems. Within each system, there is a set of nominally identical replaceable components of particular interest (e.g., 2 automobile headlights, 8 dual in-line memory module (DIMM) modules in a computing server, 16 cylinders in a locomotive engine). For each component replacement event, there is system-level information that a component was replaced, but no information on which particular component was replaced. Thus, the observed data are a collection of superpositions of renewal processes (SRP), one for each system in the fleet. This article proposes a procedure for estimating the component lifetime distribution using the aggregated event data from a fleet of systems. We show how to compute the likelihood function for the collection of SRPs and provide suggestions for efficient computations. We compare performance of this incomplete-data maximum likelihood (ML) estimator with the complete-data ML estimator and study the performance of confidence interval methods for estimating quantiles of the lifetime distribution of the component. Supplementary materials for this article are available online.     

产品质量风险信息管理系统的研究与设计

本文立足我国当前产品质量风险信息监测与预警体系建设的实际情况,基于J2EE架构,研究和设计一个具有多角度信息采集、风险信息全过程处理和风险监测库建设等关键内容的产品质量风险信息管理系统,并重点介绍了系统的目标、系统架构、核心业务流程设计和系统功能设计等内容。     

The use of reliability‐oriented field feedback information for product design improvement: a case study

In the innovative industry, four major trends are found to influence product quality and reliability: the increase in product complexity, the strong pressure on time to market, the increasing global economy, and the decreasing tolerance for quality problems. Thus, it becomes more difficult to anticipate all potential failures during the product development process. In this context, an efficient field feedback process should be in place to react to the unanticipated deviations in product performance. Based on a case study made in an innovative company, this paper shows that the problem is not so much in the information collection as in the inherent quality of the information and in the manner the information is processed. Therefore, a new method, presented in this paper, was developed to classify and prioritize field data and to upgrade it into information that can be used for design improvement according to the dominant classes of failures using the four‐phase roller coaster model. Although this newly generated information is richer than raw field data it is not yet detailed enough to allow direct design optimization. Therefore, a second upgrading stage, based on design of experiments, was developed. It uses a method that combines physics‐of‐failure (bottom‐up) and field information (top‐down). As traditional DoE mainly deals with largely time‐independent quality data obtained during the manufacturing process the approach had to be modified to deal with time‐dependent reliability data. Case study results show that it is a promising approach for characterizing and resolving failure mechanisms also in innovative companies. Copyright © 2008 John Wiley & Sons, Ltd.     

Why do quality and reliability feedback loops not always work in practice: a case study

The increasing competition in the market of consumer electronics forces industry to simultaneously improve the functionability, reliability and costs of their products. Due to the strong dynamics of this field an important measure in improving product quality is the feedback of information on actual field behaviour of a product which is extremely relevant for product development. This information, usually gathered by the service department, can help development to learn from the failures of earlier products and to improve the next product generations. This feedback process, however, does not always bring the expected benefits. This paper presents a model, based on an actual case study, of the structure of the feedback process in terms of information flows. The information flow model describes the chain or network of activities in the feedback process as a function of (different) drivers such as cost, time or quality. Using this information flow model it is possible to explain how quality and reliability related information propagates through a business process and where and why communication problems lead to disruptions in the feedback of this information. With this model, it is easier to understand the function of different parties in the service or after sales process, so that management of the information in this process becomes more transparent and can be improved.     

装配工艺信息建模技术在信息集成中的应用

为了解决制造行业中异构系统间的装配工艺信息交互问题,建立了基于IDEF1x的装配工艺信息模型,本信息模型描述了装配工艺规划过程中的产品、工艺、资源信息,阐明了基于1DEF1x的装配工艺信息的表现形式及其特点．以DELMIA（数字企业精益制造交互式应用）系统为实例,基于本信息模型,通过DELMIA二次开发技术访问、操纵并且组织制造数据库中装配工艺信息．给出了2种实现工艺信息交互的途径：XML（可扩展的标识语言）技术和oracle数据库应用．并利用实例说明了本文建立的装配工艺信息模型在DELMIA系统中的有效性．     

产品设计过程中的可靠性增长研究

针对产品设计过程中的可靠性增长问题,提出把整个产品系统分为大样本子系统和小样本子系统,对于不同的子系统采用不同的可靠性增长方案和可靠性增长数学分析模型,充分挖掘所有的直接或问接数据信息,并利用到产品设计过程;建立产品的可靠性增长分析数据库系统,保存产品在设计过程中的数据,辅助同类产品设计．     

Some factors to consider in developing a quality-related feedback system

This paper reports on work which has been carried out in an attempt to identify some of the key factors in the development of a quality-related feedback system associated specifically with major installations. The main reasons why the majority of manufacturers have not developed systems to obtain data from users is due to the difficulties encountered in obtaining meaningful information. The current main sources of feedback are from technical sales representatives, customer complaints and internal feedback obtained from monitoring recurrent orders for spares. The information being generated by users of computerized maintenance systems has, to date, been little used by manufacturers to initiate quality-improving changes. However, most manufacturers said they would use such data if it was offered. It is also pointed out that designers can play a key role in improving the quality of plant and equipment by requesting feedback information in those areas where there might be design uncertainty and by giving consideration to the possibility of condition monitoring when a product is being designed.     

Modelling infant failure rate of electromechanical products with multilayered quality variations from manufacturing process

The optimisation of product infant failure rate is the most important and difficult task for continuous improvement in manufacturing; how to model the infant failure rate promptly and accurately of the complex electromechanical product in manufacturing is always a dilemma for manufacturers. Traditional methods of reliability analysis for the produced product usually rely on limited test data or field failures, the valuable information of quality variations from the manufacturing process has not been fully utilised. In this paper, a multilayered model structured by ‘part-level, component-level, system-level’ is presented to model the reliability in the form of infant failure rate by quantifying holistic quality variations from manufacturing process for electromechanical products. The mechanism through which the multilayered quality variations affect the infant failure rate is modelled analytically with a positive correlation structure. Furthermore, an integrated failure rate index is derived to model the reliability of electromechanical product in manufacturing by synthetically incorporating overall quality variations with Weibull distribution. A case study on a control board suffering from infant failures in batch production is performed. Results show that the proposed approach could be effective in assessing the infant failure rate and in diagnosing the effectiveness of quality control in manufacturing.     

A STEP-compliant product data model for injection moulding products

In the current rapidly changing manufacturing environment, most manufacturing companies have realised that the ability to quickly develop a customised product at competitive prices is very crucial for them to survive in the keen competitive global market. Product modelling has been recognised as one of the key factors in determining the success of various product development strategies and for industrial competitiveness now and in the future. Standard for Exchange of Product model data (STEP) is an international standard designed to provide a complete, unambiguous, computer-readable definition of the physical and functional characteristic of a product through its lifecycle. This paper proposes a STEP-compliant product data model to fully support the complete product information representation and management for injection moulding product development. The data model is developed based on the structure of an injection moulding company. The entire data model consists of six data models which are defined to express information from various departments of the company. The data model is described in EXPRESS language, which can be integrated with other STEP-based product data models and databases. Consequently, the proposed system can be further extended for helping other injection moulding manufacturers to improve their product development process. An information sharing prototype system is implemented based on the proposed product data model to demonstrate the feasibility of the approach.     

Improving the Quality of Information Flows in the Backend of a Product Development Process: a Case Study

Considerable research has gone into designing effective product development processes. This, coupled with the increasing need for products that are able to deliver reliable, complex functionality with a high degree of innovation, presents a major challenge to modern day industries in the business of developing products. In order to incorporate relevant field experience in the design and manufacturing of new products, increasingly detailed information needs to be retrieved from the market in a very short amount of time. In one particular consumer electronics industry, business process models describing the information flow in the backend of the product development process indicated massive data loss and also serious data quality degradation. This paper attempts to show how such losses can be mitigated and also proposes a business model that can adequately capture information of a higher quality and in a more structured manner. The end result will be a product development process that provides better feedback on current product performance and is more responsive to future market needs. Copyright © 2005 John Wiley & Sons, Ltd.     

A Study on Engineering History Base

During engineering processes, many kinds and amounts of information are used and produced. Such information is useful in successive cases, and thus its reuse is desired. In this paper, information on attributes of the products is referred to as ‘product information’, and information on the reasons for and/or history of a designer’s thought process is referred to as ‘process information’. According to this classification, process information can be used for obtaining an explanation of product information. The purpose of this work is to build a database, called an ‘engineering history base’, from which engineers can retrieve explanations to enable the reuse of product information. In this paper, explanation from the ‘process’ viewpoint is thought to be important in promoting the reuse of product information. There are two types of explanation for the process: teleological and causal. However, until now, little attention has been paid to the causal explanation for the process. Thus, in this paper, an information model which focuses on both types of explanation is proposed. The model is adopted for the engineering history base and a prototype system is developed. The appropriateness of this model is discussed by analyzing the actual data in the development of a color video printer.     

Enhancing product development through a dynamic information interchange approach

In today's increasingly competitive environment, firms are experiencing growing pressure to reduce the product development lead time to meet market expectations. In this paper, a Responsive Product Development System (RPDS), which is used to model the product development process and the components of the process with object technology, introduces a dynamic product information schema characterized by its ability to provide design practitioners with a product data exchange standard, thus transforming data to information and then knowledge. To validate the feasibility of the proposed schema, a case study has been conducted in a plastic product factory based on the suggested approach. Following feedback from these companies, a further review of the design of the proposed system has been conducted to ensure efficient information flow across the heterogeneous computing environment. A measure of loss to society associated with RPDS in product development time is also included as a system evaluation.     

A meta-model for mechanical products based upon the mechanical design process

This paper describes preliminary work toward the develoment of a framework and a system for modeling the meta-physical information of mechanical products. Meta-physical information is that information which describes the nature or reason for existence of objects in the physical product model. Such information includes product and feature functionality, design intent, relations, constraints and viewpoint-dependent definitions. This effort has resulted in an initial model structure and a prototype system. The product model consists of a meta-physical product model with attached physical product models containing, among other information, geometry, dimensions, tolerances, and features. The content and structure of the product model correspond directly to the information used and produced during the mechanical design process. The prototype system integrates a solid modeler, a feature modeler, a dimension and tolerance modeler, and a meta-physical modeler. This paper provides an overview of the meta-model structure, usage and potential.     

混合装配线防错系统的研究与开发

提出了面向复杂机械产品混合装配线的多Agent防错系统的模型,进行了相应的适应混合流水装配线的软硬件系统结构的研究。采用RS485总线控制系统的硬件配置,建立了工作站节点、数据采集、数据集成接口、智能决策等多Agent软件模块,形成了可适应装配产品变换的具有一定柔性、可扩展配置的防错系统。     

产品数字化开发中的过程管理

分析了项目管理、流程管理和产品数据对象的生命周期管理的底层一致性和相互关系.将产品数字化开发过程的管理在粒度上分为项目管理、流程管理和产品数据对象的生命周期管理三种,分别对应任务、活动和操作三种不同粒度的过程单元,不同粒度的过程单元使用不同策略.基于此建立了过程管理的模型,给出了过程管理在PDM系统中的初步实现,实现了过程和数据动态管理的集成.     

The challenge of quantitative reliability

After examining the need of the equipment/system user to receive, collect, analyse and supply quantitative reliability information, this paper discusses the methods, existing and potential, of satisfying this need. It does so against the background of the now widely acknowledged inaccuracy of traditional methods of reliability prediction, the recognition that the conventional failure-rate bathtub curve is an oversimplification and the difficulty of gathering adequate test and field failure data on increasingly high-reliability product in an age of fast technology evolution and short new product lead-time markets. There are three important tasks for the equipment user: (a) establishment of an efficient system of failure data collection and analysis; (b) utilization of these data in all business and organizational activities; (c) participation in the creation of an accurate method of reliability prediction by providing feedback of field reliability data to product manufacturers. © 1998 John Wiley & Sons, Ltd.     

Field-Failure and Warranty Prediction Based on Auxiliary Use-Rate Information

Assessment of risk due to product failure is important both for purposes of finance (e.g., warranty costs) and safety (e.g., potential loss of human life). In many applications a prediction of the number of future failures is an important input to such an assessment.

Usually the field-data response used to make predictions of future failures is the number of weeks (or another unit of real time) in service. Use-rate information usually is not available (automobile warranty data are an exception, where both weeks in service and number of miles driven are available for units returned for warranty repair). With new technology, however, sensors and smart chips are being installed in many modern products ranging from computers and printers to automobiles and aircraft engines. Thus the coming generations of field data for many products will provide information on how the product was used and the environment in which it was used. This article was motivated by the need to predict warranty returns for a product with multiple failure modes. For this product, cycles-to-failure/use-rate information was available for those units that were connected to the network. We show how to use a cycles-to-failure model to compute predictions and prediction intervals for the number of warranty returns. We also present prediction methods for units not connected to the network. To provide insight into the reasons that use-rate models provide better predictions, we also present a comparison of asymptotic variances comparing the cycles-to-failure and time-to-failure models. This article has supplementary material online.     

离散制造行业MES的产品可追溯性建模

阐述了MES中生产执行、数据采集和生产跟踪与其他活动之间的关系,对生产跟踪和追溯所关联的实体关系进行定界,结合离散制造过程的特点,建立了生产跟踪和追溯数据模型.数据模型着眼生产跟踪、质量和物料批次组成关系三个方面,在工序层面上关联物料批次组成关系,满足以物料或任务为入口的向前向后追溯.最后给出了可追溯在MES中的实现.     

Comparison of EPA's QMS to SEI's CMMI.

EPA and other government organizations make decisions based on environmental measurements. How good are the data? How well are the data generators performing? What measurements apply to them? How can the data life cycle processes be improved so data generators can continually provide the best data? EPA's Quality Management System requirements go beyond evaluation of environmental data quality itself to examine systems associated with production, collection, processing (validation/verification), transfer, reduction, storage, and retrieval of data throughout a life cycle. This QMS specifies minimum quality requirements for particular environmental programs. But how can you measure and compare programs that go well beyond the minimum, towards optimal quality? This paper compares EPA's requirements for Quality Management Systems (R2) and Project Plans (R5) to the Software Engineering Institute Capability Maturity Model (CMMISM). The CMMISM model provides for growth (staged or continuous) and a comprehensive assessment that is not yet provided in EPA's R2 or R5. Properly implemented, the CMMISM model serves as a quality framework for integrating and aligning organizational processes and implementing a program of continual process improvements. It identifies process areas ("things to do"), and provides measures of performance ("how well things are done") against specific goals and practices. CMMISM uses a Systems Engineering Management approach, built on process models, that helps identify "how good" the system is. Goodness is defined as stages in a complete model for optimal operation. CMMISM provides two methods for evaluating the goodness of the project. The Staged model in CMMISM provides a Maturity Level that is a well-defined evolutionary plateau describing the manner in which a specified set of processes are performed. As the organization advances in maturity, these levels become more defined and processes are tailored for specific project needs. The other method is called the Continuous Model in CMMISM, and it allows you to achieve Capability Levels. These are used to describe how well each project is doing in relationship to the different process areas. There are six Capability Levels from 0-5 that apply to individual process areas. Organizations using the Capability Level approach can select individual process areas that are important to specific projects and work to improve the processes. Improving capability in individual process areas raises the organization's overall quality of products delivered. The Continuous Model, unlike the Staged Model, lets you pick higher maturity level process areas before completing all of the ones below. Environmental measurement programs need to focus on the quality of the systems where data are collected, processed, transferred, and so forth. DynCorp built on the quality foundation from our experience with R2 to successfully implement CMMISM practices in the development of Forms II Lite and other applications. DynCorp is now migrating to the CMMISM model that has evolved from the existing CMM model. The CMMISM model focuses on the full cycle of Requirements Management from identification, development, collection, refinement, analysis, and validation throughout a project life cycle. It also has a more refined focus on the identification, development, collection, analysis, and evaluation of meaningful measurements, so the results can be used to improve a process or product.     

Diagnosability Study of Multistage Manufacturing Processes Based on Linear Mixed-Effects Models

Automatic in-process data collection techniques have been widely used in complicated manufacturing processes in recent years. The huge amounts of product measurement data have created great opportunities for process monitoring and diagnosis. Given such product quality measurements, this article examines the diagnosability of the process faults in a multistage manufacturing process using a linear mixed-effects model. Fault diagnosability is defined in a general way that does not depend on specific diagnosis algorithms. The concept of a minimal diagnosable class is proposed to expose the “aliasing” structure among process faults in a partially diagnosable system. The algorithms and procedures needed to obtain the minimal diagnosable class and to evaluate the system-level diagnosability are presented. The methodology, which can be used for any general linear input–output system, is illustrated using a panel assembly process and an engine head machining process.