The business article this month propose a discussion about which are the important reliability and safety engineering methods to be applied during railways asset life cycle in order to achieve high performance.
The business article this month propose a discussion about why FMEA is important for the railways assets throughout it life cycle. The paper aims to explain the FMEA importance and achievement, the different types of the FMEA and the advantages to be applied for the railways asset life cycle phases.
this paper aims to clarify the aspect of RCM implemented during the design phase and how to integrate with the asset management during operation phase.
The RCM still plays an important hole in RAMS & LCC railway program during design and operation phase. The maintenance management process and implemented during the operation phase need to be part of the asset management program considering all aspects defined in ISO 55000 as well as the three key success factors such as equipment data, people and process.
The RCM information during the design are inputting information for other important analysis during the design phase such as RAM analysis, LCC analysis and LORA. In addition, the RCM information is input to the assurance plan during the operation phase as part of an asset management program, which is facilitated with some integrated data system. Such system enables to implement the asset management as a process include performance monitoring, maintenance information (maintenance plan and work orders), FRACAS, time and task management, LCC management. The coming paper will demonstrate all aspects of the asset management for the railway industry.
This paper explain the basis of the lifetime data analysis, it application throughout the railway asset life cycle, the main mathematic concepts and the study case.
The lifetime data analysis is the basis of reliability prediction as well as other index such as failure rate and unreliability. In order to predict such index for a specific period of time and plot the reliability and failure rate functions, it's necessary to apply different best fit methods in order to know firstly, which probability density function (pdf) fits better with the historical data. Secondly, the PDF parameter definition, which enable the reliability and failure rate function plot and index prediction.
The reliability concept means “probability of one equipment, product or service be successful until a specific time under defined operating conditions. In order to define the equipment reliability is necessary to collect historical failure data.
Therefore, the first step in the lifetime data analysis (LDA) study is to know how failures occur a long time and that's a critical issue for the reliability proper prediction in order to support decisions such as the best time of inspection and preventive maintenance, to check if the equipment is achieved reliability requirement and to supply reliability information to new projects.
The RAM analysis is a systematic system performance and bad actors effect prediction based on reliability and maintainability data, as well as preventive maintenance, spare parts and LCC data throughout the asset life cycle.
Abstract: This business paper aims to demonstrate the application of RAM analysis for railway industry as part of RAMS program implementation, which enable to predict the railways asset reliability, availability and maintainability in different levels, such as system, subsystem, equipment and component by taking into account the relation to each part failure to the impact to the highest system level. The basis for the RAM analysis, prediction is the LDA results based on historical data as discussed in business paper presented in March 2018 in the ECC website. Therefore, to predict the railways system performance, after the LDA results, it´s necessary to model the systems based on the RBD (Reliability Diagram Block) or FTA (Fault Tree Analysis). The final step is the Monte Carlo simulation concerning the system life cycle and operational profile. Finally, the RAM analysis results will provide more than systems, subsystem, equipment and component performance, but also the bad actors definition, in other words, the equipment/component which causes more impact on system operational availability. In order to exemplify the RAM analysis methodology, the case studies applied to critical equipment such as Bogie, Break and signalling will be presented at the end of this chapter.
Key Words: Lifetime Data Analysis (LDA), reliability, availability, maintainability, expected number of failures and life cycle cost.
Abstract: This paper aims to demonstrate the application of ILS analysis for railway industry as part of RAMS program implementation, which enable to predict the effect of the logistic on the railways asset performance. The basis for the ILS are the main RAM methods such as LDA, RAM, FMEA and RCM results as discussed in previous chapters. Therefore, the spare parts level, the level of repair analysis, life cycle cost and supportability analysis will be the main topics of this paper. Finally, the case study concerning ILS concepts applied to critical equipment such as pantograph will be presented at the end of this paper.
Key Words: LSA, LRA, spare parts, Life cycle cost, supportability.
Abstract: This paper aims to demonstrate the application of FHA for railway industry application as part of RAMS program implementation based on the standard EN 50129 concept. Such risk analysis enables to predict the effect of the safety critical element, which may trigger a major accident such as collision or derailment. In addition, it´s possible to define the level of safety integrity necessary for such safety critical element. In order to demonstrate the FHA concept application a cases study concerns ETCS will be demonstrated
Key Words: FHA, SIL, Risk Matrix, THL, ETCS
Abstract: This paper aims to demonstrate the application Safety Integrity Level (SIL) for railway industry application as part of RAMS program implementation based on the standard EN 50129 concept. Such risk analysis enables to define a semi-quantitative safety requirement for each safety function as well as to allocate the SIL target for hardware and software associated functions. Moreover, such SIL target needs to be demonstrated as part of SIL verification, which is a quantitative assessment based on fault tree analysis and field historical data. In order to demonstrate the SIL concept application a cases study concerns ETCS on board will be demonstrated
Key Words: FHA, SIL, Risk Matrix, THL, ETCS