Application of Predictive Maintenance Strategy for Rolling Stock Assignment Problem

Maintenance is fundamental to ensure the safety, reliability and availability of engineering systems, and predictive maintenance is the leading one in maintenance technology. This paper aims to develop a novel data-driven predictive maintenance strategy that can make appropriate maintenance decisions for repairable complex engineering systems. The proposed strategy includes degradation feature selection and degradation prognostic modeling modules to achieve accurate failure prognostics. For maintenance decision-making, the perfect time for taking maintenance activities is determined by evaluating the maintenance cost online that has taken into account of the failure prognostic results of performance degradation. The feasibility and effectiveness of the proposed strategy is confirmed using the NASA data set of aero-engines. Results show that the proposed strategy outperforms the two benchmark maintenance strategies: classical periodic maintenance and emerging dynamic predictive maintenance.

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Effectiveness assessment of diesel locomotives operation with the use of mobile maintenance points

Archives of Transport ◽

10.5604/01.3001.0014.2622 ◽

2020 ◽

Vol 54 (2) ◽

pp. 7-19

Author(s):

Maciej Szkoda ◽

Magdalena Satora ◽

Zbigniew Konieczek

Keyword(s):

Transport Process ◽

System Development ◽

Average Distance ◽

Rolling Stock ◽

Maintenance Strategy ◽

Maintenance System ◽

Unit Costs ◽

New Strategy ◽

Maintenance Activities ◽

Effectiveness Assessment

Based on the example of 6Dg type diesel locomotives, the paper presents a new maintenance strategy concerning periodical checks at the P1 maintenance level. Currently, such locomotives are sent off to service points every 102 hours of operation or every 14 days for a P1 level check. Studies demonstrate that the average distance to be covered by a locomotive to arrive at a service point is in excess of 60.0 km, and the quantity of fuel consumed is more than 88 litres. It is costly and time consuming to have locomotives out of service and considerable resources of the railway carrier are engaged which could be made use of in the transport process. The aim of the newly developed strategy of P1 checks is to eliminate the need for locomotives to exit their routes to reach rolling stock maintenance points. The control/diagnostic and maintenance activities specified in the Maintenance System Documentation will be performed by so-called mobile maintenance points. The development of the new strategy required: identification of the current condition of the maintenance system, development of the concept of a new strategy of P1 maintenance checks, conduct of studies and operational analyses for SM42 series locomotives, performance of a durability, reliability analysis, assessment of safety together with an analysis of the risks involved in the proposed changes. In order to review and assess the efficiency of the new strategy, an observed operation of selected locomotives was conducted together with a railway carrier. During the study, the maintenance activities and processes were monitored and the costs of P1 checks were recorded. The analysis of efficiency of the new strategy of performing P1 checks without the need for a locomotive to exit its route to reach a rolling stock maintenance point demonstrated that depending on the distance covered by the locomotive to reach a maintenance point, the unit costs of a P1 level check are lower by up to 67.1% compared with the currently applied method.

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RESOURCE-SAVING MAINTENANCE AND REPAIR OF SPECIAL SELF-PROPELLED ROLLING STOCK

Psychology and Education Journal ◽

10.17762/pae.v58i1.1305 ◽

2021 ◽

Vol 58 (1) ◽

pp. 3550-3555

Author(s):

Z. G. Mukhamedova Et al.

Keyword(s):

The Self ◽

Rolling Stock ◽

Reliability Model ◽

Maintenance Strategy ◽

Structural Diagram ◽

Resource Saving ◽

Maintenance And Repair ◽

Reliability Indices ◽

Compatible System

The article provides the issues of improving the models of reliability and optimization of maintenance and repair of special self-propelled rolling stock (SPRS) with a compatible system of diagnostics. A new maintenance strategy is proposed, taking into account the new requirements of the self-propelled rolling stock. Examples of solving practical problems of reliability indices are considered. The study of the reliability of SPRS, as an object, is advisable to start with its presentation in the form of a system of assembly units. In this sense, the level of consideration is important. Figure 1 gives a structural diagram of SPRS, as a system consisting of elements. for several possible levels of consideration. SPRS, as a system of elements, makes it possible to concretize the task of analyzing and improving the reliability model and developing measures to perfect it.

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Risk-Oriented Product Assembly System Health Modeling and Predictive Maintenance Strategy

Sensors ◽

10.3390/s19092086 ◽

2019 ◽

Vol 19 (9) ◽

pp. 2086 ◽

Cited By ~ 2

Author(s):

Fengdi Liu ◽

Yihai He ◽

Yixiao Zhao ◽

Anqi Zhang ◽

Di Zhou

Keyword(s):

Health Risk ◽

High Reliability ◽

Health Assessment ◽

Assessment Method ◽

Predictive Maintenance ◽

Assembly System ◽

Health State ◽

Maintenance Strategy ◽

Health States ◽

Operational Data

Assembly quality is the barometer of assembly system health, and a healthy assembly system is an important physical guarantee for producing reliable products. Therefore, for ensuring the high reliability of products, the operational data of the assembly system should be analyzed to manage health states. Therefore, based on the operational data of the assembly system collected by intelligent sensors, from the perspective of quality control based on risk thinking, a risk-oriented health assessment method and predictive maintenance strategy for managing assembly system health are proposed. First, considering the loss of product reliability, the concept of assembly system health risk is proposed, and the risk formation mechanism is expounded. Second, the process variation data of key reliability characteristics (KRCs) collected by different sensors are used to measure and assess the health risk of the running assembly system to evaluate the health state. Third, the assembly system health risk is used as the maintenance threshold, the predictive maintenance decision model is established, and the optimal maintenance strategy is determined through stepwise optimization. Finally, the case study verifies the effectiveness and superiority of the proposed method. Results show that the proposed method saves 37.40% in costs compared with the traditional method.

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