Fault tree based fault diagnostics methodology for an aircraft fuel system

improved by reducing the time taken to restore systems to the working state when faults occur. The fault identification process can be a significant proportion of the time taken in the repair process. Having diagnosed the problem the restoration of the system back to its fully functioning condition can then take place. This paper describes the development of a fault diagnostic methodology for an aircraft fuel system. The approach takes into account the dynamics of the system. Using sensors installed to provide information about the current status of certain critical parameters. The information produced for these parameters are then categorised into different trend types using a simple pattern recognition technique. Non-coherent fault trees are then used to identify all possible causes of the observed sensor reading trends. By combining the information provided from all sensors the causal faults can be detected. The approach presented has been developed and tested for small demonstration systems – this paper describes how it has been scaled up for a larger, more representative system and the issues that have been overcome in doing this. The system used exists as an experimental facility where the procedure developed can now be fully tested

Editorial special issue: PHM for railway systems and mass transportation

The railway and mass transportation system is composed of industrial goods with substantial capital investments and long life cycles. This applies to rolling stock like trains, locomotives, wagons, and even more to the infrastructure like signaling, catenary, tracks, bridges, and tunnels. The lifespan of rolling stock is 30 to 40 years while the infrastructure is used 30 to 60 years even more than 100 years in case of tunnels and bridges. As in other industrial goods, the cost drivers are determined in the early design phases but realized mainly during a long time of operation. Maintenance is one of the main cost drivers but essential to a reliable, capable, and – above all – safe operation

Scheduling predictive maintenance in flow-shop.

International audienceAvailability of production equipments is one major issue for manufacturers. Predictive maintenance is an answer to prevent equipment from risk of breakdowns while minimizing the maintenance costs. Nevertheless, conflicts could occur between maintenance and production if a maintenance operation is programmed when equipment is used for production. The case studied here is a flow-shop typology where machines could be maintained once during the planning horizon. Machines are able to switch between two production modes. A nominal one and a degraded one where machine run slowly but increase its remaining useful life. We propose a mixed integer programming model for this problem with the makespan and maintenance delays objective. It allows to find the best schedule of production operation. It also produces, for each machine, the control mode and if necessary the preventive maintenance plan

Task scheduling to extend platform useful life using prognostics.

International audienceIn this paper, we aim at maximizing the useful life of a heterogeneous distributed platform which has to deliver a given production. The machines (one nominal mode and several degraded ones). Depending on the profile, a machine reaches a given throughput. At each time the sum of the machine throughputs that are currentky running determines the global throughput. Moreover, each machine is supposed to be monitored and a prognostic module gives its remaining useful life depending on both its past and future usage (profile). the objective is to configure the platform so as to reach the demand as long as possible. We propose to discretize the time into periods and to choose a configuration for each period. We propose an Integer Linear Programming (ILP) model to find such configurations for a fixed time horizon. Due to the number of variables and constraints in the ILP, the largest horizon can be computed for small instances of the problem. For larger ones , we propose polynomial time heuristics to maximize the useful life. Exhaustive simulations show that the heuristics solutions are close to the optimal (5% in average) in the case where the optimal horizon can to computed. for other platforms with a very large number of machines, simulations assess the efficienty of our heuristics. The distance to the theoretical maximal value is about 8% in average

A sensor selection method for fault diagnostics

In the modern world, systems are becoming increasingly complex, consisting of large numbers of components and their failures. In order to monitor system performance and to detect faults and diagnose failures, sensors can be used. However, using sensors can increase the cost and weight of the system. Therefore, sensors need to be selected based on the information that they provide. In this paper, a sensor selection process is introduced based on a novel sensor performance metric. In this process, sensors are selected based on their ability to detect faults and diagnose failures of components in the system, as well as the severity of failure effects on system performance. A Bayesian Belief Network (BBN) is used to model the outputs of the sensors. Sensor reading evidence is introduced in the BBN to enable the component failures to be identified. A simple system example is used to illustrate the proposed approach

Exploring the role of E-maintenance for value creation in service provision

Technological innovations has always played an important role in economic growth and industrial productivity, but they have also potential to influence service industry. In particular, they can offer support to the process of servitization in manufacturing companies. This article presents a study regarding the prospective value that different technological innovations can offer to maintenance service provision. A review of different baseline technologies and a categorization of several types of E-maintenance tools and applications has been carried out in order to understand the new functionalities that can potentially bring to the provision of smart maintenance services. Moreover, a value analysis method for representing the contribution of tool categories to several value dimensions is presented here. This method can be used for identifying the best technological solution, matching both customer value and provider value, i.e. conforming a win-win situation for the parties involved in the service provision. Some preliminary results based on a survey are eventually given as a first test of its applicability