Condition based maintenance of trains doors

As part of the project DUST financed by Vinnova, we have investigated whether event data generated on trains can be used for finding evidence of wear on train doors. We have compared the event data and maintenance reports relating to doors of Regina trains. Although some interesting relations were found, the overall result is that the information in event data about wear of doors is very limited

Condition-based maintenance at both scheduled and unscheduled opportunities

Motivated by original equipment manufacturer (OEM) service and maintenance practices we consider a single component subject to replacements at failure instances and two types of preventive maintenance opportunities: scheduled, which occur due to periodic system reviews of the equipment, and unscheduled, which occur due to failures of other components in the system. Modelling the state of the component appropriately and incorporating a realistic cost structure for corrective maintenance as well as condition-based maintenance (CBM), we derive the optimal CBM policy. In particular, we show that the optimal long-run average cost policy for the model at hand is a control-limit policy, where the control limit depends on the time until the next scheduled opportunity. Furthermore, we explicitly calculate the long-run average cost for any given control-limit time dependent policy and compare various policies numerically.Comment: published at proceedings of the 9th IMA International Conference on Modelling in Industrial Maintenance and Reliability (MIMAR), 201

On condition-based maintenance for machine components

The goal of condition-based maintenance (CBM) is to base the decisions whether or not to perform maintenance on information collected from the machine or component of interest. A condition-based maintenance tool should be able to diagnose if the component of interest is in a state of failure but the ultimate goal of a CBM tool is to be able to estimate time until failure, either in terms of remaining useful life (RUL) or estimated time to failure (ETTF). Therefore a CBM tool should have both diagnostic and prognostic features. This master’s thesis was carried out at a company within the packaging industry and the goal was to implement a CBM tool with the possibility to estimate RUL for a set of critical components which could serve as a base for further development within the company. The selection of components to focus on was part of the thesis as well. The process of implementing CBM with prognostic functionality was more difficult than expected and the goal of estimating RUL was not met for any of the components, but the work that has been done forms a basis for further development. Thus, this thesis will serve as a pre-study on developing CBM and contains information of what is required in order to be successful

Condition-based maintenance of wind turbine blades

The blades of offshore wind farms (OWTs) are susceptible to a wide variety of diverse sources of damage. Internal impacts are caused primarily by structure deterioration, so even though outer consequences are the consequence of harsh marine ecosystems. We examine condition-based maintenance (CBM) for a multiblade OWT system that is exposed to environmental shocks in this work. In recent years, there has been a significant rise in the number of wind turbines operating offshore that make use of CBMs. The gearbox, generator, and drive train all have their own vibration-based monitoring systems, which form most of their foundation. For the blades, drive train, tower, and foundation, a cost analysis of the various widely viable CBM systems as well as their individual prices has been done. The purpose of this article is to investigate the potential benefits that may result from using these supplementary systems in the maintenance strategy. Along with providing a theoretical foundation, this article reviews the previous research that has been conducted on CBM of OWT blades. Utilizing the data collected from condition monitoring, an artificial neural network is employed to provide predictions on the remaining life. For the purpose of assessing and forecasting the cost and efficacy of CBM, a simple tool that is based on artificial neural networks (ANN) has been developed. A CBM technique that is well-established and is based on data from condition monitoring is used to reduce cost of maintenance. This can be accomplished by reducing malfunctions, cutting down on service interruption, and reducing the number of unnecessary maintenance works. In MATLAB, an ANN is used to research both the failure replacement cost and the preventative maintenance cost. In addition to this, a technique for optimization is carried out to gain the optimal threshold values. There is a significant opportunity to save costs by improving how choices are made on maintenance to make the operations more cost-effective. In this research, a technique to optimizing CBM program for elements whose deterioration may be characterized according to the level of damage that it has sustained is presented. The strategy may be used for maintenance that is based on inspections as well as maintenance that is based on online condition monitoring systems

Condition-based maintenance implementation: A literature review

Industrial companies are increasingly dependent on the availability and performance of their equipment to remain competitive. This circumstance demands accurate and timely maintenance actions in alignment with the organizational objectives. Condition-Based Maintenance (CBM) is a strategy that considers information about the equipment condition to recommend appropriate maintenance actions. The main purpose of CBM is to prevent functional failures or a significant performance decrease of the monitored equipment. CBM relies on a wide range of resources and techniques required to detect deviations from the normal operating conditions, diagnose incipient failures or predict the future condition of an asset. To obtain meaningful information for maintenance decision making, relevant data must be collected and properly analyzed. Recent advances in Big Data analytics and Internet of Things (IoT) enable real-time decision making based on abundant data acquired from several different sources. However, each appliance must be designed according to the equipment configuration and considering the nature of specific failure modes. CBM implementation is a complex matter, regardless of the equipment characteristics. Therefore, to ensure cost-effectiveness, it must be addressed in a systematic and organized manner, considering the technical and financial issues involved. This paper presents a literature review on approaches to support CBM implementation. Published studies and standards that provide guidelines to implement CBM are analyzed and compared. For each existing approach, the steps recommended to implement CBM are listed and the main gaps are identified. Based on the literature, factors that can affect the effective implementation of CBM are also highlighted and discussed.This work is supported by: European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project nº 39479; Funding Reference: POCI-01-0247-FEDER-39479]