A test rig for the condition-based maintenance application on the traction chain of very high speed trains

The preventive maintenance of traction equipment for Very High Speed Trains (VHST) nowadays is becoming very expensive owing to the high complexity and quality of these components that require high reliability. An efficient maintenance approach like the Condition-Based Maintenance (CBM) should be implemented to reduce the costs. For this purpose, an experimental full-scale test rig for the CBM of VHST traction equipment has been designed to investigate in detail failures in the main mechanical components of system, i.e. motor, bearings and gearbox. The paper describes the main characteristics of this unique test rig, able to reproduce accurately the train operating conditions, including the relative movements of the motor, the gearbox and the wheel axle. Gearbox, bearing seats and motor are equipped by accelerometers, thermocouples, torque meter and other sensors in different positions. The testing results give important information about the most suitable sensor position and type to be installed for each component and show the effectiveness of the techniques used for the signal analysis in order to identify faults of the gearbox and motor bearings.

Optimizing preventative maintenance strategies for linear assets

Linear assets are engineering infrastructure, such as pipelines, railway lines, and electricity cables, which span long distances and can be divided into different segments. Optimal management of such assets is critical for asset owners as they normally involve significant capital investment. Currently, Time Based Preventive Maintenance (TBPM) strategies are commonly used in industry to improve the reliability of such assets, as they are easy to implement compared with reliability or risk-based preventive maintenance strategies. Linear assets are normally of large scale and thus their preventive maintenance is costly. Their owners and maintainers are always seeking to optimize their TBPM outcomes in terms of minimizing total expected costs over a long term involving multiple maintenance cycles. These costs include repair costs, preventive maintenance costs, and production losses. A TBPM strategy defines when Preventive Maintenance (PM) starts, how frequently the PM is conducted and which segments of a linear asset are operated on in each PM action. A number of factors such as required minimal mission time, customer satisfaction, human resources, and acceptable risk levels need to be considered when planning such a strategy. However, in current practice, TBPM decisions are often made based on decision makers’ expertise or industrial historical practice, and lack a systematic analysis of the effects of these factors. To address this issue, here we investigate the characteristics of TBPM of linear assets, and develop an effective multiple criteria decision making approach for determining an optimal TBPM strategy. We develop a recursive optimization equation which makes it possible to evaluate the effect of different maintenance options for linear assets, such as the best partitioning of the asset into segments and the maintenance cost per segment.

Assessing organizational culture in complex sociotechnical systems : Methodological evidence from studies in nuclear power plant maintenance organizations

Failures in industrial organizations dealing with hazardous technologies can have widespread consequences for the safety of the workers and the general population. Psychology can have a major role in contributing to the safe and reliable operation of these technologies. Most current models of safety management in complex sociotechnical systems such as nuclear power plant maintenance are either non-contextual or based on an overly-rational image of an organization. Thus, they fail to grasp either the actual requirements of the work or the socially-constructed nature of the work in question. The general aim of the present study is to develop and test a methodology for contextual assessment of organizational culture in complex sociotechnical systems. This is done by demonstrating the findings that the application of the emerging methodology produces in the domain of maintenance of a nuclear power plant (NPP). The concepts of organizational culture and organizational core task (OCT) are operationalized and tested in the case studies. We argue that when the complexity of the work, technology and social environment is increased, the significance of the most implicit features of organizational culture as a means of coordinating the work and achieving safety and effectiveness of the activities also increases. For this reason a cultural perspective could provide additional insight into the problem of safety management. The present study aims to determine; (1) the elements of the organizational culture in complex sociotechnical systems; (2) the demands the maintenance task sets for the organizational culture; (3) how the current organizational culture at the case organizations supports the perception and fulfilment of the demands of the maintenance work; (4) the similarities and differences between the maintenance cultures at the case organizations, and (5) the necessary assessment of the organizational culture in complex sociotechnical systems. Three in-depth case studies were carried out at the maintenance units of three Nordic NPPs. The case studies employed an iterative and multimethod research strategy. The following methods were used: interviews, CULTURE-survey, seminars, document analysis and group work. Both cultural analysis and task modelling were carried out. The results indicate that organizational culture in complex sociotechnical systems can be characterised according to three qualitatively different elements: structure, internal integration and conceptions. All three of these elements of culture as well as their interrelations have to be considered in organizational assessments or important aspects of the organizational dynamics will be overlooked. On the basis of OCT modelling, the maintenance core task was defined as balancing between three critical demands: anticipating the condition of the plant and conducting preventive maintenance accordingly, reacting to unexpected technical faults and monitoring and reflecting on the effects of maintenance actions and the condition of the plant. The results indicate that safety was highly valued at all three plants, and in that sense they all had strong safety cultures. In other respects the cultural features were quite different, and thus the culturally-accepted means of maintaining high safety also differed. The handicraft nature of maintenance work was emphasised as a source of identity at the NPPs. Overall, the importance of safety was taken for granted, but the cultural norms concerning the appropriate means to guarantee it were little reflected. A sense of control, personal responsibility and organizational changes emerged as challenging issues at all the plants. The study shows that in complex sociotechnical systems it is both necessary and possible to analyse the safety and effectiveness of the organizational culture. Safety in complex sociotechnical systems cannot be understood or managed without understanding the demands of the organizational core task and managing the dynamics between the three elements of the organizational culture.

A data fusion approach of multiple maintenance data sources for real-world reliability modelling

A central tenet in the theory of reliability modelling is the quantification of the probability of asset failure. In general, reliability depends on asset age and the maintenance policy applied. Usually, failure and maintenance times are the primary inputs to reliability models. However, for many organisations, different aspects of these data are often recorded in different databases (e.g. work order notifications, event logs, condition monitoring data, and process control data). These recorded data cannot be interpreted individually, since they typically do not have all the information necessary to ascertain failure and preventive maintenance times. This paper presents a methodology for the extraction of failure and preventive maintenance times using commonly-available, real-world data sources. A text-mining approach is employed to extract keywords indicative of the source of the maintenance event. Using these keywords, a Naïve Bayes classifier is then applied to attribute each machine stoppage to one of two classes: failure or preventive. The accuracy of the algorithm is assessed and the classified failure time data are then presented. The applicability of the methodology is demonstrated on a maintenance data set from an Australian electricity company.

Optimal maintenance policies for machines subject to deterioration and intermittent breakdowns

Optimal preventive maintenance policies, for a machine subject to deterioration with age and intermittent breakdowns and repairs, are derived using optimal control theory. The optimal policies are shown to be of bang-bang nature. The extension to the case when there are a large number of identical machines and several repairmen in the system is considered next. This model takes into account the waiting line formed at the repair facility and establishes a link between this problem and the classical ``repairmen problem.''

Predictive Maintenance for Improved Sustainability - An Ion Beam Etch Endpoint Detection System Use Case

In modern semiconductor manufacturing facilities maintenance strategies are increasingly shifting from traditional preventive maintenance (PM) based approaches to more efficient and sustainable predictive maintenance (PdM) approaches. This paper describes the development of such an online PdM module for the endpoint detection system of an ion beam etch tool in semiconductor manufacturing. The developed system uses optical emission spectroscopy (OES) data from the endpoint detection system to estimate the RUL of lenses, a key detector component that degrades over time. Simulation studies for historical data for the use case demonstrate the effectiveness of the proposed PdM solution and the potential for improved sustainability that it affords.

A New Model For Reliability Centered Maintenance In Petroleum Refineries

Refiners today operate their equipment for prolonged periods without shutdown. This is primarily due to the increased pressures of the market resulting in extended shutdown-to-shutdown intervals. This places extreme demands on the reliability of the plant equipment. The traditional methods of reliability assurance, like Preventive Maintenance, Predictive Maintenance and Condition Based Maintenance become inadequate in the face of such demands. The alternate approaches to reliability improvement, being adopted the world over are implementation of RCFA programs and Reliability Centered Maintenance. However refiners and process plants find it difficult to adopt this standardized methodology of RCM mainly due to the complexity and the large amount of analysis that needs to be done, resulting in a long drawn out implementation, requiring the services of a number of skilled people. These results in either an implementation restricted to only few equipment or alternately, one that is non-standard. The paper presents the current models in use, the core requirements of a standard RCM model, the alternatives to classical RCM, limitations in the existing model, classical RCM and available alternatives to RCM and will then go on to present an ‗Accelerated‘ approach to RCM implementation, that, while ensuring close conformance to the standard, does not place a large burden on the implementers

Optimal maintenance policies under different operational schedules

In the reliability literature, maintenance time is usually ignored during the optimization of maintenance policies. In some scenarios, costs due to system failures may vary with time, and the ignorance of maintenance time will lead to unrealistic results. This paper develops maintenance policies for such situations where the system under study operates iteratively at two successive states: up or down. The costs due to system failure at the up state consist of both business losses & maintenance costs, whereas those at the down state only include maintenance costs. We consider three models: Model A, B, and C: Model A makes only corrective maintenance (CM). Model B performs imperfect preventive maintenance (PM) sequentially, and CM. Model C executes PM periodically, and CM; this PM can restore the system as good as the state just after the latest CM. The CM in this paper is imperfect repair. Finally, the impact of these maintenance policies is illustrated through numerical examples.

Maintenance of a reducer by vibration and wear particles analysis

Purpose - The purpose of the paper is to provide information on wear debris on oil and vibration analysis as predictive maintenance techniques in reducer. Design/methodology/approach - The estate of a reducer is verified by analyzing the vibration and oil conditions of a test rig under well-designed conditions utilizing some predictive variables. Findings - According to the vibration and oil analysis it is found out what it was happening into the reducer without disassembling it. Practical implications - This paper demonstrates the use of oil debris analysis and vibration analysis as a technique that enhances preventive maintenance practices. The paper helps practitioners to utilize these techniques more effectively. Originality/value - This paper gives information about two predictive maintenance techniques with a test rig. © Emerald Group Publishing Limited.

Organizational maintenance manual : turret for combat engineer vehicle, M728 (2350-00-795-1797).

"September 1980."

Organizational maintenance manual : turret for tank, combat, full-tracked, 105-mm gun, M60A1 Rise, (2350-00-116-9765) and M60A1 (Rise passive), (2350-01-059-1503).

"July 1980"--Vol. 1.

Maintenance Strategies for used equipment under lease

Abstract: Purpose – The aim of this research is to determine the optimal upgrade and preventive maintenance actions that minimize the total expected cost (maintenance costs+penalty costs). Design/methodology/approach – The problem is a four-parameter optimization with two parameters being k-dimensional. The optimal solution is obtained by using a four-stage approach where at each stage a one-parameter optimization is solved. Findings – Upgrading action is an extra option before the lease of used equipment, in addition to preventive maintenance action. Upgrading action makes equipment younger and preventive maintenance action lowers the ROCOF. Practical implications – There is a growing trend towards leasing equipment rather than owning it. The lease contract contains penalties if the equipment fails often and repairs are done within reasonable time period. This implies that the lessor needs to look at optimal preventive maintenance strategies in the case of new equipment lease, and upgrade actions plus preventive maintenance in the case of used equipment lease. The paper deals with this topic and is of great significant to business involved with leasing equipment. Originality/value – Nowadays many organizations are interested in leasing equipment and outsourcing maintenance. The model in this paper addresses the preventive maintenance problem for leased equipment. It provides an approach to dealing with this problem.

Optimization of pavement maintenance and rehabilitation programming using shuffled complex evolution algorithm

The major barrier to practical optimization of pavement preservation programming has always been that for formulations where the identity of individual projects is preserved, the solution space grows exponentially with the problem size to an extent where it can become unmanageable by the traditional analytical optimization techniques within reasonable limit. This has been attributed to the problem of combinatorial explosion that is, exponential growth of the number of combinations. The relatively large number of constraints often presents in a real-life pavement preservation programming problems and the trade-off considerations required between preventive maintenance, rehabilitation and reconstruction, present yet another factor that contributes to the solution complexity. In this research study, a new integrated multi-year optimization procedure was developed to solve network level pavement preservation programming problems, through cost-effectiveness based evolutionary programming analysis, using the Shuffled Complex Evolution (SCE) algorithm.^ A case study problem was analyzed to illustrate the robustness and consistency of the SCE technique in solving network level pavement preservation problems. The output from this program is a list of maintenance and rehabilitation treatment (M&R) strategies for each identified segment of the network in each programming year, and the impact on the overall performance of the network, in terms of the performance levels of the recommended optimal M&R strategy. ^ The results show that the SCE is very efficient and consistent in the simultaneous consideration of the trade-off between various pavement preservation strategies, while preserving the identity of the individual network segments. The flexibility of the technique is also demonstrated, in the sense that, by suitably coding the problem parameters, it can be used to solve several forms of pavement management programming problems. It is recommended that for large networks, some sort of decomposition technique should be applied to aggregate sections, which exhibit similar performance characteristics into links, such that whatever M&R alternative is recommended for a link can be applied to all the sections connected to it. In this way the problem size, and hence the solution time, can be greatly reduced to a more manageable solution space. ^ The study concludes that the robust search characteristics of SCE are well suited for solving the combinatorial problems in long-term network level pavement M&R programming and provides a rich area for future research. ^

A practical approach for reliability prediction of pipeline systems

Pipelines play an important role in the modern society. Failures of pipelines can have great impacts on economy, environment and community. Preventive maintenance (PM) is often conducted to improve the reliability of pipelines. Modern asset management practice requires accurate predictability of the reliability of pipelines with multiple PM actions, especially when these PM actions involve imperfect repairs. To address this issue, a split system approach (SSA) based model is developed in this paper through an industrial case study. This new model enables maintenance personnel to predict the reliability of pipelines with different PM strategies and hence effectively assists them in making optimal PM decisions.

Sustainable Road Management in Texas : network-level flexible pavement structural condition analysis using data mining techniques

The research team recognized the value of network-level Falling Weight Deflectometer (FWD) testing to evaluate the structural condition trends of flexible pavements. However, practical limitations due to the cost of testing, traffic control and safety concerns and the ability to test a large network may discourage some agencies from conducting the network-level FWD testing. For this reason, the surrogate measure of the Structural Condition Index (SCI) is suggested for use. The main purpose of the research presented in this paper is to investigate data mining strategies and to develop a prediction method of the structural condition trends for network-level applications which does not require FWD testing. The research team first evaluated the existing and historical pavement condition, distress, ride, traffic and other data attributes in the Texas Department of Transportation (TxDOT) Pavement Maintenance Information System (PMIS), applied data mining strategies to the data, discovered useful patterns and knowledge for SCI value prediction, and finally provided a reasonable measure of pavement structural condition which is correlated to the SCI. To evaluate the performance of the developed prediction approach, a case study was conducted using the SCI data calculated from the FWD data collected on flexible pavements over a 5-year period (2005 – 09) from 354 PMIS sections representing 37 pavement sections on the Texas highway system. The preliminary study results showed that the proposed approach can be used as a supportive pavement structural index in the event when FWD deflection data is not available and help pavement managers identify the timing and appropriate treatment level of preventive maintenance activities.