The effect of overloading sequences on landing gear fatigue damage

In service, landing gear can be subject to unexpected hard landing load, which is beyond the design domain. The consequences due to overload can affect the design life of a landing gear to some extent. In this paper, the effect of overload and different loading sequences in random spectra on fatigue damage are investigated, using strain–life based fatigue analysis methods. The discussions are emphasised on the effect of loading sequences on residual stress and mean stress, especially the effect of overload on the fatigue damage of subsequent cycles. In addition, different fatigue analysis techniques in commercial fatigue analysis packages are reviewed and compared. The analysis indicates that the overload effect is stress state dependent and dominated by local residual and mean stress. A ‘Begin’ overload in a load spectrum would cause more damage in the local compressive yield area and an ‘End’ overload within a spectrum will worsen the tensile yielding area. It is suggested that the load sequence effect should be considered in common fatigue analysis if local yielding would exist before or after overloading.     

Improved quality of input data for maintenance optimization using expert judgment

Most maintenance optimization models need an estimate of the so-called ‘naked’ failure rate function as input. In practice it is very difficult to estimate the ‘naked’ failure rate, because overhauls and other preventive maintenance actions tend to ‘corrupt’ the recorded lifelengths. The purpose of this paper is to stress the importance of utilizing the knowledge of maintenance engineers, i.e., expert judgment, in addition to recorded equipment lifelengths, in order to get credible input data. We have shown that without utilizing expert judgment, the estimated mean time to failure may be strongly biased, often by a factor of 2–3, depending on the life distribution that is assumed. We recommend including a simple question about the mean remaining lifelength on the work-order forms. By this approach the knowledge of maintenance engineers may be incorporated in a simple and cost-effective way.     

The British Airways human factors reporting programme

This paper describes three safety programmes that provide BA flight operations with feedback on operational quality. Two of these are flight data recording and air safety reporting. These earlier programmes are described primarily to set in context the development of the most recent of the three, the human factors reporting programme (HFR). This is a confidential programme that records and analyses data using a distinctly different process from most reporting programmes. It requests not only information about ‘What’ happened in an event but ‘Why’ the event occurred and ‘How’ the crew dealt with the problem. Analysis uses ‘factors’ describing crew actions and the influences on crew actions. Actions and influences can describe safety positive as well as negative events and the analysis produces causal event sequence diagrams of each reported event. The paper describes these processes and offers some illustrative examples taken from the database.     

Maintenance, reliability and policies for orbital space station life support systems

The performance of productive work on space missions is critical to sustaining a human presence on orbital space stations (OSS), the Moon, or Mars. Available time for productive work has potentially been impacted on past OSS missions by underestimating the crew time needed to maintain systems, such as the Environmental Control and Life Support System (ECLSS). To determine the cause of this apparent disconnect between the design and operation of an OSS, documented crew time for maintenance was collected from the three Skylab missions and Increments 4–8 on the International Space Station (ISS), and the data was contrasted to terrestrial facility maintenance norms. The results of the ISS analysis showed that for four operational and seven functional categories, the largest deviation of 60.4% over the design time was caused by three of the four operational categories not being quantitatively included in the design documents. In a cross category analysis, 35.3% of the crew time was found to have been used to repair air and waste handling systems. The air system required additional crew time for maintenance due to a greater than expected failure rate and resultant increased time needed for repairs. Therefore, it appears that the disconnect between the design time and actual operations for ECLSS maintenance on ISS was caused by excluding non-repair activities from the estimates and experiencing greater than expected technology maintenance requirements. Based on these ISS and Skylab analyses, future OSS designs (and possibly lunar and Martian missions as well) should consider 3.0–3.3 h/day for crews of 2 to 3 as a baseline of crew time needed for ECLSS maintenance.     

A general discrete-time dynamic programming model for the opportunistic replacement policy and its application to ball-bearing systems

A general discrete-time, stochastic, dynamic-programming model of opportunistic corrective and preventive replacement policy is presented for multi-component systems with a different life distribution for each component. An approach to optimize the corrective and preventive maintenance policy is proposed, which is based on the introduction of the ‘oldest age’ concept and an ‘array dimensionality reduction technique’. The application of this policy to ball-bearing systems has resulted in considerable savings, as this paper demonstrates.     

Technology of the new society

To seriously speculate about the rôle of technology in a society of the future it is necessary to carefully distinguish between quantitative and qualitative aspects of life. Thus ‘progress’ and ‘improvement’, as well as ‘surviving’ and ‘living’, are pairs of notions inspected in this Paper as to their relative importance for society.

Résumé

To seriously speculate about the rôle of technology in a society of the future it is necessary to carefully distinguish between quantitative and qualitative aspects of life. Thus ‘progress’ and ‘improvement’, as well as ‘surviving’ and ‘living’, are pairs of notions inspected in this Paper as to their relative importance for society.     

Applying RCM in large scale systems: a case study with railway networks

In 2000, the European Union founded a project named ‘RAIL: Reliability centered maintenance approach for the infrastructure and logistics of railway operation’ aimed to study the application of Reliability centered maintenance (RCM) techniques to the railway infrastructure. In this paper, we present the results obtained into the RAIL project, including a RCM methodology adapted to large infrastructure networks and a RCM toolkit to perform the RCM analysis, including cost aspects and maintenance planning guidance. This paper addresses the problem of applying RCM to large scale railway infrastructure networks to achieve an efficient and effective maintenance concept. Railways use nowadays very traditional preventive maintenance (PM) techniques, relying mostly on ‘blind’ periodic inspection and the ‘know-how’ of maintenance staff. RCM was seen as a promising technique from the beginning of the RAIL project because of several factors. First, technical insights obtained were better than the existing, so that several maintenance processes could be revised and adjusted. Second, the interdisciplinary approach used to make the analysis was very enriching and very encouraging for maintenance staff consulted. Third, using the RCM structured approach allowed to achieve well-documented analysis and clear decision diagrams. Our methodology includes some new features to overcome the problems of RCM observed in other projects. As a whole, our methodology and Computerized Maintenance Management Systems have produced two short-term benefits: reduction of time and paperwork because databases and tools are accessible through Internet, and creation of a permanent, accurate, and better collection of information. It will also have some long-term benefits: better PM will increase equipment life and will help to reduce corrective maintenance costs; Production will increase as unscheduled downtime decreases; purchase costs of parts and materials will be reduced; more effective and up-to-date record of inventory/stores reports; and better knowledge of the systems to help the company to chose those systems with the best LCC. The results have been corroborated with the application of our methodology to signal equipment in several railway network sections, as shown in this paper. Because of the successful conclusion of the project, the Spanish railway company (RENFE) and the German railway company (DB A.G.), not only decided to adopt RCM to enhance PM, but they have started a large project to implement Total Preventive Maintenance relying on the implantation of the RCM methodology.     

A Maintainability Prediction Method Considering Environmental Impacts and Cost

Maintainability prediction is one kind of primary maintainability action.Design deficiency would be found through predicting maintainability parameters under certain conditions.Now a main-tainability prediction method that mainly considers maintenance time or maintenance man-hour is a kind of prediction method with a single index.With increasing product complexity and peoples environ-mental consciousness, more attention is paid to environment impacts and maintenance cost or resource consumption in the maintenance process.It is necessary for a maintainability prediction method that can predict maintenance cost and maintenance environmental impacts .A new maintainability predic-tion method is presented in this paper based on analyzing existing maintainability prediction methods.The method is MABTCE(maintenance activity based timing/costing/environment impact assessment) and can pridict maintenance time,maintenance costing and maintenance environmental impacts and then improve maintainability design with prediction results.     

The EDF failure reporting system process, presentation and prospects

This paper describes the procedure Electricité de France uses to exploit the information on pressurized water reactor operation it receives back from the field (operation feedback).The first requirement in analyzing such data is a knowledge of past records. The first step, therefore, is to record the data, particularly events occurring on the plant and failures occurring on equipment, in large reliability data banks.However, the ‘raw’ information stored is rarely usable directly. The first step in the second stage—analysis—is to review and qualify the data before using it for any purpose. This difficult, but essential, review provides valuable information on the improvement of equipment reliability.The greater knowledge of plant and equipment behaviour, and the damage mechanisms involved, allows:

• safety to be kept at a high level: operation feedback is also essential for probabilistic safety studies,

• improvement of availability and preventive maintenance practices,

• correction of the initial design (design changes) and help for designing future plants.

Finally, operation feedback is a source of progress. Although it requires heavy initial investment, it is also a source of profit. It is a source of learning. The analysis results make it possible to define more suitable procedures and better preventive maintenance practices and thus improve the operation and safety of existing and future plants.     

Modifications to the ‘directional simulation in the load space’ approach to structural reliability analysis

‘Directional simulation in the load space (DS-LS)’ is a simulation-based technique used to perform reliability analysis of structures subjected to time-invariant or time-variant random loads. To perform DS-LS a location must first be chosen for an ‘origin of simulation’. The origin may be positioned in either the safe or failure region of the load space, and its precise location (with respect to these regions) influences the DS-LS formulation needed to evaluate reliability correctly. The current formulation requires the origin to be positioned in the safe region. However, even for simple structures, the ‘exact’ location of the safe and failure region is not always known explicitly ‘a priori’. Modifications to allow for the possibility of positioning the origin not only in the ‘safe’ region but in the ‘failure’ region are proposed in this paper. Some numerical examples involving one or more stationary continuous Gaussian loads and the simulation of directions by ‘Monte Carlo’ and ‘the hyperspace division method’ are presented to demonstrate the validity of the proposed formulations. Some comments on convergence are made.     

Generalized renewal process for analysis of repairable systems with limited failure experience

Repairable systems can be brought to one of possible states following a repair. These states are: ‘as good as new’, ‘as bad as old’, ‘better than old but worse than new’, ‘better than new’, and ‘worse than old’. The probabilistic models traditionally used to estimate the expected number of failures account for the first two states, but they do not properly apply to the last three, which are more realistic in practice. In this paper, a robust solution to a probabilistic model that is applicable to all of the five after repair states, called generalized renewal process (GRP), is presented. This research demonstrates that the GRP offers a general approach to modeling repairable systems and discusses application of the classical maximum likelihood and Bayesian approaches to estimation of the GRP parameters. This paper also presents a review of the traditional approaches to the analysis of repairable systems as well as some applications of the GRP and shows that they are subsets of the GRP approach. It is shown that the proposed GRP solution accurately describes the failure data, even when a small amount of failure data is available.Recent emphasis in the use of performance-based analysis in operation and regulation of complex engineering systems (such as those in space and process industries) require use of sound models for predicting failures based on the past performance of the systems. The GRP solution in this paper is a promising and efficient approach for such performance-based applications.     

空间站卫生间人机仿真评估与实践

目的 对空间站卫生间的人机关系进行分析,评价其人机工效,为空间站卫生间人机工程评价、设计优化提供理论方法和技术支持。方法 以DELMIA软件为分析工具,在对空间站卫生间环境建模后,导入航天飞行乘员人体尺寸模型,构建人机仿真虚拟场景。将空间站卫生间如厕过程分解,调整人体模型的如厕姿态及舒适角度范围,对空间站卫生间可达可视域进行判别,开展空间站卫生间如厕姿态舒适性仿真分析与评价。结果 仿真结果显示虚拟航天成员如厕各阶段姿态可达性、可视性、舒适性均满足人机工程学需求。结论 该空间站卫生间能为航天乘员提供一个舒适的如厕环境,其总体结构与布局符合人机工程学设计原则。     

On preventive maintenance policy of a critical reliability level for system subject to degradation

Conventional preventive maintenance (PM) policies generally hold same time interval for PM actions and are often applied with known failure modes. The same time interval will give unavoidably decreasing reliabilities at the PM actions for degradation system with imperfect PM effect and the known failure modes may be inaccurate in practice. Therefore, field managers would prefer policy with an acceptable reliability level to keep system often at a good state.A PM policy with the critical reliability level is presented to address the preference of field managers. Through assuming that system after a PM action starts a new failure process, a parameter so-called degradation ratio is introduced to represent the imperfect effect. The policy holds a law that there is same number of failures in the time intervals of various PM cycles, and same degradation ratio for the system reliability or benefit parameters such as the optimal time intervals and the hazard rates between the neighboring PM cycles. This law is valid to any of the failure modes that could be appropriately referred as a ‘general isodegrading model’, and the degradation ratio as a ‘general isodegrading ratio’. In addition, life cycle availability and cost functions are derived for system with the policy. An analysis of the field data of a loading and unloading machine indicates that the reliability, availability and cost in life cycle might be well modeled by the present theory and approach.     

Bayesian estimation of unavailability

This paper gives two Bayesian methods for estimating test-and-maintenance unavailability. Both unplanned and periodic maintenance are considered. One estimation method uses ‘detailed data,’ the individual outage times. The other method uses ‘summary data,’ totals of outage time and exposure time in various time periods such as calendar months. Either method can use either a noninformative or an informative prior distribution. Both methods are illustrated with an example data set, and the results are compared.     

工程装备虚拟维修技术研究现状与展望

目的 针对工程装备设计的维修性难以验证,维修训练面临装备昂贵、不易重复拆卸、危险性高等问题,运用虚拟维修技术来评估装备的维修性和开展维修训练。方法 进行文献分析,理清虚拟维修的研究范畴和热点,从维修性设计、维修训练两个应用层面和虚拟人技术、虚拟样机及环境技术、维修过程与交互技术、维修性评估技术四个关键技术层面入手,对相关研究成果进行了整理和综述。结论 分析了目前研究存在的不足,提出了还可以进一步发展基于传感器技术的沉浸式交互方式,完善核心软件技术,研究多人协同维修与团体评估方法。     

Considerations in the use of optimal preventive maintenance policies

Designing products which require maintenance always involves compromises between reliability and maintainability. Both scheduled and preventive maintenance (PM) should be considered in the design phases of a product so that the design can include features to ease the maintenance task. In addition, many design decisions based on Failure Modes and Effects Criticality Analysis (FMECA), Pareto criticality rankings, etc., could and should be strongly influenced by the potential for using preventive maintenance. A component that has a major negative impact on system reliability (because of its life distribution) could become much less consequential if appropriate PM policies are implemented. This paper describes the use of an easy-to-implement analysis procedure to assist a designer or systems analyst in making the reliability/maintainability tradeoff.     

The effect of two complexity factors on the performance of emergency tasks—An experimental verification

It is well known that the use of procedures is very important in securing the safety of process systems, since good procedures effectively guide human operators by providing ‘what should be done’ and ‘how to do it’, especially under stressful conditions. At the same time, it has been emphasized that the use of complicated procedures could drastically impair operators’ performance. This means that a systematic approach that can properly evaluate the complexity of procedures is indispensable for minimizing the side effects of complicated procedures. For this reason, Park et al. have developed a task complexity measure called TACOM that can be used to quantify the complexity of tasks stipulated in emergency operating procedures (EOPs) of nuclear power plants (NPPs). The TACOM measure consists of five sub-measures that can cover five important factors making the performance of emergency tasks complicated. However, a verification activity for two kinds of complexity factors—the level of abstraction hierarchy (AH) and engineering decision (ED)—seems to be insufficient. In this study, therefore, an experiment is conducted by using a low-fidelity simulator in order to clarify the appropriateness of these complexity factors. As a result, it seems that subjects’ performance data are affected by the level of AH as well as ED. Therefore it is anticipate that both the level of AH and ED will play an important role in evaluating the complexity of EOPs.     

Optimal restarting distribution after repair for a Markov deteriorating system

We consider a repairable system such that different completeness degrees are possible for the repair (or corrective maintenance) that go from a ‘minimal’ up to a ‘complete’ repair. Our question is: to what extent must the system be repaired in case of failure for the long-run availability to be optimal? The system evolves in time according to a Markov process as long as it is running, whereas the duration of repairs follows general distributions. After repair, the system starts again in the up-state i with probability d(i). We observe from numerical examples that the optimal restarting distribution d^opt (such that the long-run availability is optimal) is generally random and does not correspond to a new start in a fixed up-state. Sufficient conditions under which the optimal restarting distribution is non-random are given. Also, the optimal restarting distribution is provided for two classical structures in reliability.     

Generalized renewal process for repairable systems based on finite Weibull mixture

Repairable systems can be brought to one of possible states following a repair. These states are: ‘as good as new’, ‘as bad as old’ and ‘better than old but worse than new’. The probabilistic models traditionally used to estimate the expected number of failures account for the first two states, but they do not properly apply to the last one, which is more realistic in practice. In this paper, a probabilistic model that is applicable to all of the three after-repair states, called generalized renewal process (GRP), is applied. Simplistically, GRP addresses the repair assumption by introducing the concept of virtual age into the stochastic point processes to enable them to represent the full spectrum of repair assumptions. The shape of measured or design life distributions of systems can vary considerably, and therefore frequently cannot be approximated by simple distribution functions. The scope of the paper is to prove that a finite Weibull mixture, with positive component weights only, can be used as underlying distribution of the time to first failure (TTFF) of the GRP model, on condition that the unknown parameters can be estimated. To support the main idea, three examples are presented. In order to estimate the unknown parameters of the GRP model with m-fold Weibull mixture, the EM algorithm is applied. The GRP model with m mixture components distributions is compared to the standard GRP model based on two-parameter Weibull distribution by calculating the expected number of failures. It can be concluded that the suggested GRP model with Weibull mixture with an arbitrary but finite number of components is suitable for predicting failures based on the past performance of the system.