Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN)

Nowadays, the complex manufacturing processes have to be dynamically modelled and controlled to optimise the diagnosis and the maintenance policies. This article presents a methodology that will help developing Dynamic Object Oriented Bayesian Networks (DOOBNs) to formalise such complex dynamic models. The goal is to have a general reliability evaluation of a manufacturing process, from its implementation to its operating phase. The added value of this formalisation methodology consists in using the a priori knowledge of both the system's functioning and malfunctioning. Networks are built on principles of adaptability and integrate uncertainties on the relationships between causes and effects. Thus, the purpose is to evaluate, in terms of reliability, the impact of several decisions on the maintenance of the system. This methodology has been tested, in an industrial context, to model the reliability of a water (immersion) heater system.     

Research on multiple-state industrial robot system with epistemic uncertainty reliability allocation method

Reliability allocation of industrial robot (IR) system is one of the important means to improve its whole life cycle, reduce maintenance cost, and characterize weak subsystems. The IR system is not only very complex but also has strong customization; meanwhile, its sample data are small, resulting in unclear degeneration and failure. Based on the above two epistemic uncertainties, a new methodology called multiple-state IR system reliability allocation method with epistemic uncertainty (MIRS-RAM-EU) is proposed. First, the Dempster-Shafer (D-S) evidence theory is used to quantify the epistemic uncertainty. Then, the Kolmogorov differential equations of MIR's subsystems are calculated. The reliability index of MIRS is allocated based on Birnbaum importance degree theory, and the reliability allocation coefficient of each IR subsystem is clearly expressed by this method. Finally, compared with traditional importance allocation method, the MIRS-RAM-EU is more efficient and accurate. This method is usefully directive for reliability evaluation of IR.     

Modelling maintenance effects on manufacturing equipment performance: results from simulation analysis

Equipment maintenance and system reliability are important factors affecting the organisation’s ability to provide quality and timely services to customer. While maintenance remains an important function to manufacturing, it is only recently that attempts have been made to quantify its impact on equipment performance. In this research, an approach of linking maintenance with equipment performance is developed using simulation modelling. The modelling approach involves defining probabilistic models and assumptions affecting system performance, such as: the probabilistic model for the initial failure rate/intensity of the equipment; the probabilistic model for the system deterioration and corresponding effect; the probabilistic model for the random times of corrective maintenance (CM) and preventive maintenance (PM) that takes into the account the types of maintenance plans/policies and the potential dependency between CM and PM times; and the probabilistic model for the random effects of CM and PM on the reliability of the equipment. Using a continuous manufacturing equipment, the model is used to analyse the impact of deterioration, failures and maintenance (policies, timing and efficiency) on equipment performance. It is shown that modelling the effect maintenance provides a basis of evaluating maintenance efforts with the potential application in performance evaluation and decision support while investigating opportunities for manufacturing equipment performance improvement.     

Integrated reconfiguration and age-based preventive maintenance decision making

The use of manufacturing system reconfiguration in conjunction with maintenance operations has not been previously reported in the literature. This research attempts to incorporate reconfiguration into Preventive Maintenance (PM) actions for improved system performance in terms of reduced total cost. This paper presents an Integrated Reconfiguration and Age-Based Maintenance (IRABM) policy and applies it to a parallel-serial manufacturing system. The expected total cost of implementing the IRABM policy is estimated and minimized through a simulation-based heuristic optimization procedure. Using this method, it is possible to systematically identify the conditions under which the integration of reconfiguration into maintenance is cost effective. In addition, numerical examples demonstrate that the manufacturing system could have a higher probability of fulfilling production requirements at a lower cost under the IRABM policy compared to the conventional age-based PM policy. The influences of the input parameters associated with reconfiguration, production, and reliability on the performance of IRABM policy also are studied.     

Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system

The importance of the maintenance function has increased because of its role in keeping and improving system availability and safety, as well as product quality. To support this role, the maintenance concept has undergone several major developments that have led to proactive considerations mainly based on a prognosis process, which normally allows selection of the best maintenance action to be carried out. This paper proposes the deployment and experimentation of a prognosis process within an e-maintenance architecture. The deployment follows a methodology based on the combination of both a probabilistic approach for modelling the degradation mechanism and of an event one for dynamical degradation monitoring. The feasibility and benefits of this new prognosis process is investigated with an experiment using a manufacturing TELMA (TELe-MAintenance) platform supporting the unwinding of metal bobbins.     

Predictive maintenance of complex system with multi-level reliability structure

Onboard sensors, which constantly monitor the states of a system and its components, have made the predictive maintenance (PdM) of a complex system possible. To date, system reliability has been extensively studied with the assumption that systems are either single-component systems or they have a deterministic reliability structure. However, in many realistic problems, there are complex multi-component systems with uncertainties in the system reliability structure. This paper presents a PdM scheme for complex systems by employing discrete time Markov chain models for modelling multiple degradation processes of components and a Bayesian network (BN) model for predicting system reliability. The proposed method can be considered as a special type of dynamic Bayesian network because the same BN is repeatedly used over time for evaluating system reliability and the inter-time–slice connection of the same node is monitored by a sensor. This PdM scheme is able to make probabilistic inference at any system level, so PdM can be scheduled accordingly.     

维修管理模式对制造企业质量控制影响的实证研究

可靠的设备预防维修水平和卓越的产品质量保证能力是保障产品过程质量的关键,现有多数研究集中在质量管理模式与企业绩效的关系分析。在对过程质量控制、设备维修管理评述的基础上,探讨了设备维修管理模式与维修绩效及过程质量控制绩效间的关系。以国内制造企业的连续生产方式为研究对象,运用结构方程模型剖析了RCM、TPM、设备采购管理与和设备维修绩效、过程质量控制绩效的路径关系,发现基础维修管理活动对维修绩效有间接影响,RCM、TPM对设备维修绩效有显著影响,路径系数分别为0.346、0.717,设备维修绩效对过程质量控制绩效的影响显著,路径系数为0922。证明制造企业为提升产品质量,需要完善其设备预防维修系统。     

Integrated predictive maintenance strategy for manufacturing systems by combining quality control and mission reliability analysis

Predictive maintenance (PdM) is an effective means to eliminate potential failures, ensure stable equipment operation and improve the mission reliability of manufacturing systems and the quality of products, which is the premise of intelligent manufacturing. Therefore, an integrated PdM strategy considering product quality level and mission reliability state is proposed regarding the intelligent manufacturing philosophy of ‘prediction and manufacturing’. First, the key process variables are identified and integrated into the evaluation of the equipment degradation state. Second, the quality deviation index is defined to describe the quality of the product quantitatively according to the co-effect of manufacturing system component reliability and product quality in the quality–reliability chain. Third, to achieve changeable production task demands, mission reliability is defined to characterise the equipment production states comprehensively. The optimal integrated PdM strategy, which combines quality control and mission reliability analysis, is obtained by minimising the total cost. Finally, a case study on decision-making with the integrated PdM strategy for a cylinder head manufacturing system is presented to validate the effectiveness of the proposed method. The final results shows that proposed method achieves approximately 26.02 and 20.54% cost improvement over periodic preventive maintenance and conventional condition-based maintenance respectively.     

A hybrid methodology for enhancing reliability of large systems in conceptual design and its application to the design of a multiphase flow station

This paper presents a hybrid methodology for conceptual design of large systems with the goal of enhancing system reliability. It integrates the features of several design methodologies and maintenance planning concepts with the traditional reliability analysis. The methodology considers the temporal quality characteristic “reliability” as the main objective and determines the optimal system design. Key ideas from several design methodologies, namely axiomatic design, robust design, and the theory of inventive problem solving, have been integrated with the functional prioritization framework provided by reliability-centered maintenance. A case study of the conceptual design of a multiphase pumping station for crude oil production is presented. The methodology provides a new design tool for determining system configurations with enhanced reliability taking into account maintenance resources and variability.     

Reliability assessment for a stochastic manufacturing system with reworking actions

This article evaluates the system reliability of a manufacturing system with reworking actions, where the system reliability is an essential indicator to determine whether the manufacturing system is capable or not. Based on the path concept, we transformed the manufacturing system into a stochastic-flow network in which the capacity of each machine is stochastic (i.e., multistate) due to failure, partial failure, and maintenance. In such a manufacturing network, the input flow (raw materials/WIP; work-in-process) processed by each machine might be defective and thus the output flow (WIP/products) would be less than the input amount. To analyze the different sources processed by the manufacturing network, we decomposed the network into one general processing path and several reworking paths by a graphical technique. Subsequently, an algorithm for the manufacturing network was proposed to generate the lower boundary vector which allows sufficient products to satisfy the demand. In terms of such a vector, the system reliability can be derived easily.     

Sustainable Manufacturing Oriented Prognosis for Facility Reuse Combining ANN and Reliability

Reuse is considered as one of the most reasonable strategies in realizing sustainability, because it enables longer useful life of facilities. This article presents an effective methodology of artificial neural network–based prognosis combined with reliability methods to evaluate and guarantee the reusability of a facility. The methodology provides the assessment of the degradation trend and prediction of the remaining life of facilities based on online condition monitoring data and historical data utilizing back propagation artificial neural networks. In addition, the corresponding reliability of a facility is calculated by fitting suitable life distribution against the in‐house time‐to‐failure data. Furthermore, maintenance decision is made by predicting the time when reliability or remaining life of a facility reaches the threshold, as determined by the facility's reusability. Application results show that the proposed methodology provides sufficient condition information for reuse decision making from both historical and online perspectives; a facility can be reused for many times during its lifetime until its reuse is no longer economic, which can assist in the achievement of the goal of manufacturing with fewer resources and assets. Copyright © 2011 John Wiley & Sons, Ltd.     

A behavior-driven reliability modeling method for complex smart systems

With recent advancements in Internet technologies and wireless communications, wireless sensor network (WSN)-based smart systems are gaining an enormous increase in use in various applications (eg, healthcare, smart home, smart manufacturing, smart power grids, and smart transportation). Due to the mission-critical or safety-critical nature of smart system applications, it is imperative that a smart system be reliable during its mission time. However, reliability analysis and design of smart systems are still open challenging research problems due to complicated dependencies existing in the WSN domain or the physical domain monitored by the WSN, and due to dependencies crossing the two domains. This paper proposes a new behavior-driven reliability modeling methodology for accurate and efficient reliability analysis of complex WSN-based smart systems, contributing toward their robust designs and operations. The suggested method can address effects of dependent behaviors affecting different domains of the smart system in a combinatorial manner. It also enables the use of efficient single-domain reliability methods to retain their efficiency. A case study of a smart home system is performed to demonstrate the application of the proposed method as well as its advantages in handling nonexponential time-to-failure distributions and in analyzing smart systems with complicated intradomain and cross-domain dependencies.     

Reliability improvement through alternative designs—A case study

In today's competitive world, reliability of equipment is extremely important to maintain quality and delivery deadlines. This is achieved by using proper maintenance and design changes for unreliable subsystems and components of a complex system. It is significant to develop a strategy for maintenance, replacement and design changes related to those subsystems and components. An analysis of down time along with causes is essential to identify the unreliable components and subsystems.This paper presents an analysis of failure data of solenoid coils of automatic internal grinding machine used in a bearing manufacturing plant. It analyses various replacement and change of design options such as introduction of pneumatic system in place of electromagnetic solenoids for improvement of reliability of the plunger movement mechanism.     

Optimal Preventive Maintenance Policies For Systems With Missions of Random Duration

This paper examines the computation of optimal preventive maintenance policies for systems which are viewed over a horizon whose length is modeled as being random. Preventive maintenance is modeled as a new mode of failure which has a different distribution of time required to make repair.

For the simple case of a system made up of one component, general conditions are developed which assure optimality of the preventive maintenance policy. Preventive maintenance for a two component-one cold standby system is also analyzed to illustrate the application of our methodology on more complex systems. Computational examples are included to emphasize the generality and tractability of this approach to reliability modeling.     

Joint production and quality control of unreliable batch manufacturing systems with rectifying inspection

Production control policy and economic sampling plan design problems have been studied separately in previous research. This paper considers a joint production control policy and economic single sampling plan design for an unreliable batch manufacturing system. The production is controlled by a modified hedging point policy which consists of building and maintaining a safety stock of finished product to avoid shortages during corrective maintenance. The main objective of this paper is to determine simultaneously the economic production quantity, the optimal safety stock level and the economic sampling plan design which minimise the expected overall cost. A stochastic mathematical model is developed and solved using a simulation optimisation approach based on the response surface methodology. Simulation is used to imitate the complex dynamic and stochastic behaviour of processes as in the real-life industrial systems. The obtained results show clearly strong interactions between production quantity, inventory state and sampling plan design which confirm the necessity of jointly considering production and quality control parameters in an integrated model. Moreover, it is shown a significant impact of production system reliability on the economic sampling plan design and therefore on the quality of finished product delivered to consumers. Numerical example and sensitivity analyses are presented for illustrative purposes.     

Decision-making on multi-mould maintenance in production scheduling

The reliability of a critical tool like a mould on a machine affects the productivity seriously in many manufacturing firms. In fact, its breakdown frequency is even higher than machines. The decision-making on when mould maintenance should be started become a challenging issue. In the previous study, the mould maintenance plans were integrated with the traditional production schedules in a plastics production system. It was proven that considering machine and mould maintenance in production scheduling could improve the overall reliability and productivity of the production system. However, the previous model assumed that each job contained single operation. It is not workable in other manufacturing systems such as die stamping which may contain multiple operations with multiple moulds in each job. Thus, this study models a new problem for multi-mould production-maintenance scheduling. A genetic algorithm approach is applied to minimise the makespan of all jobs in 10 hypothetical problem sets. A joint scheduling (JS) approach is proposed to decide the start times of maintenance activities during scheduling. The numerical result shows that the JS approach has a good performance in the new problem and it is sensitive to the characteristic of the setup time defined.     

Optimisation of maintenance schedules and extents for composite power systems using multi-objective evolutionary algorithm

Reducing the overall cost and improving the reliability are the two primary but often conflicting objectives in power system. Preventive-maintenance schedules thus need to be optimised to trade-off among multiple objectives. An integrated methodology with three functional blocks is proposed in this study. The first block models the stochastic deterioration process of individual component with a continuous-time Markov model, of which transition rates are influenced by different maintenance extents and aging of components. The second block evaluates the reliability of a composite power system, taking into account the configuration and failure dependence of the system. Particularly, this block identifies the minimum cut sets with consideration of protection trip and operational switching. The third block employs the Pareto-based multi-objective evolutionary algorithm to find the optimal solutions in a large search space and provide a holistic view of relationships among conflicting multiple objectives. A novel representation of maintenance activities is introduced in this study specifying both the maintenance timings and extents, and is proven to outperform the authors' previous representation, specifying the maintenance frequencies only. Optimisation of the reliability, maintenance failure costs is carried out on the Roy Billinton Test System (RBTS) demonstrating the potential of this approach in handling complex systems.     

A copula-based quantified airworthiness modelling for civil aircraft engines

The aircraft engine serves as the core system of an aircraft and operates under extreme conditions, requiring high reliability and absolute safety. The design, manufacturing, and after-sales services of aircraft engines are complex processes. To ensure safety and performance, maintenance checks are performed periodically and hierarchically throughout the engine’s life-cycle. Among these checks, shop visit (SV) heavy maintenance checks play a crucial role but are also costly, especially when they occur unexpectedly and unplanned. Analysis of the maintenance logbook, recording aviation operations, reveals a significant occurrence of unplanned SVs, which may be attributed to the existing maintenance policy based on a single time-definition. To address this issue, this paper seeks to establish a novel approach to quantifying airworthiness through copula modelling, which combines two time-definitions: the flying hour (FH) and the flying cycle (FC). This approach is unique in the aviation industry. By employing the Gumbel copula with the generalized extreme value (GEV) distribution as the marginal distribution, and utilizing non-parametric association measurement parameter estimation, the quantified airworthiness of civil aircraft engine fleets across multiple product lines can be effectively modeled. This research provides valuable insights into optimizing maintenance policies and enhancing the reliability and safety of aircraft engines.     

IDEF*: A comprehensive modelling methodology for the development of manufacturing enterprise systems

To completely describe a manufacturing system, several models are usually created, each from a different viewpoint. Traditionally, these models were created independently using different methodologies and in different environments. This approach poses many problems, for example, time-consumption, incompatibility between models, difficulties in model maintenance, difficulties in ensuring a seamless transition in the system development life cycle, etc. To overcome these problems, a comprehensive modelling methodology and its supporting software tool have been developed at Gintic. The methodology, termed IDEF*, is CIMOSA-compliant, IDEF0-based, and integrated. The purpose of this paper is to introduce to the readers these features of the methodology, and the structure and components of the software tool.