| 1. |
- Kumar, Uday, et al.
(författare)
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Current trends in maintenance engineering
- 2011
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Ingår i: International Journal of Performability Engineering. - : RAMS Consultants. - 0973-1318. ; 7:6, s. 503-504
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 2. |
- Al-Douri, Yamur K.
(författare)
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Two-Level Multi-Objective Genetic Algorithm for Risk-Based Life Cycle Cost Analysis
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Artificial intelligence (AI) is one of the fields in science and engineering and encompasses a wide variety of subfields, ranging from general areas (learning and perception) to specific topics, such as mathematical theorems. AI and, specifically, multi-objective genetic algorithms (MOGAs) for risk-based life cycle cost (LCC) analysis should be performed to estimate the optimal replacement time of tunnel fan systems, with a view towards reducing the ownership cost and the risk cost and increasing company profitability from an economic point of view. MOGA can create systems that are capable of solving problems that AI and LCC analyses cannot accomplish alone.The purpose of this thesis is to develop a two-level MOGA method for optimizing the replacement time of reparable system. MOGA should be useful for machinery in general and specifically for reparable system. This objective will be achieved by developing a system that includes a smart combination of techniques by integrating MOGA to yield the optimized replacement time. Another measure to achieve this purpose is implementing MOGA in clustering and imputing missing data to obtain cost data, which could help to provide proper data to forecast cost data for optimization and to identify the optimal replacement time.In the first stage, a two-level MOGA is proposed to optimize clustering to reduce and impute missing cost data. Level one uses a MOGA based on fuzzy c-means to cluster cost data objects based on three main indices. The first is cluster centre outliers; the second is the compactness and separation ( ) of the data points and cluster centres; the third is the intensity of data points belonging to the derived clusters. Level two uses MOGA to impute the missing cost data by using a valid data period from that are reduced data in size. In the second stage, a two-level MOGA is proposed to optimize time series forecasting. Level one implements MOGA based on either an autoregressive integrated moving average (ARIMA) model or a dynamic regression (DR) model. Level two utilizes a MOGA based on different forecasting error rates to identify proper forecasting. These models are applied to simulated data for evaluation since there is no control of the influenced parameters in all of the real cost data. In the final stage, a two-level MOGA is employed to optimize risk-based LCC analysis to find the optimal replacement time for reparable system. Level one uses a MOGA based on a risk model to provide a variation of risk percentages, while level two uses a MOGA based on an LCC model to estimate the optimal reparable system replacement time.The results of the first stage show the best cluster centre optimization for data clustering with low and high intensity. Three cluster centres were selected because these centres have a geometry that is suitable for the highest data reduction of 27%. The best optimized interval is used for imputing missing data. The results of the second stage show the drawbacks of time series forecasting using a MOGA based on the DR model. The MOGA based on the ARIMA model yields better forecasting results. The results of the final stage show the drawbacks of the MOGA based on a risk-based LCC model regarding its estimation. However, the risk-based LCC model offers the possibility of optimizing the replacement schedule.However, MOGA is highly promising for allowing optimization compared with other methods that were investigated in the present thesis.
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| 3. |
- Alsyouf, Imad
(författare)
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Cost Effective Maintenance for Competitve Advantages
- 2004
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis describes the role of cost effective maintenance in achieving competitive advantages. It explores by means of a survey which maintenance practices are used, and how maintenance policies are selected in Swedish industries. Also, it suggests a model for selecting the most cost effective maintenance policy, and how to improve the effectiveness of condition based maintenance decision-making. Finally it discusses how to assess the impact of maintenance practices on business strategic objectives. The main results achieved in the thesis are 1) A better understanding of maintenance organisation, management, systems and maintenance status in Swedish industry. For example, it was found that about 70% of Swedish companies still consider maintenance as a cost centre. Preventive and predictive maintenance approaches are also emphasised. 2) Most Swedish firms, i.e. about 81%, use the accumulated knowledge and experience within the company as a method for maintenance selection. Besides, about 31% use a method based on modelling the time to failure and optimisation. About 10% use failure mode effect and criticality analysis (FMECA) and decision trees and only 2% use multiple criterion decision-making (MCDM). However, the most used maintenance selection method is not the one most satisfactory to its users. Furthermore, about 30% use a combination of at least two methods. 3) A practical model for selecting and improving the most cost effective maintenance policy was developed. It is characterised by incorporating all the strengths of the four methods used in industry. 4) A mechanistic model for predicting the value of vibration level was verified both at the lab and in a case study. 5) A model for identifying, assessing, monitoring and improving the economic impact of maintenance was developed and tested in a case study. Thus it was proved that maintenance is no longer a cost centre, but could be a profit-generating function. To achieve competitive advantages, companies should do the right thing, e.g. use the most cost effective maintenance policy, and they should do it right, e.g. ensure that they have the right competence. Furthermore, they should apply the never-ending improvement cycle, i.e. Plan-Do-Check-Act, which requires identifying problem areas by assessing the savings and profits generated by maintenance and monitoring the economic impact of the applied maintenance policy. Thus, they would know where investments should be allocated to eliminate the basic reasons for losses and increase savings. The major conclusion is that proper maintenance would improve the quality, efficiency and effectiveness of production systems, and hence enhances company competitiveness, i.e. productivity and value advantages, and long-term profitability.
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| 4. |
- Asplund, Matthias
(författare)
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Wayside Condition Monitoring System for Railway Wheel Profiles : Applications and Performance Assessment
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The railway is an important mode of transport, due to its environmental friendliness, high safety level, and low energy consumption combined with a high transport capacity, among other factors. The Swedish railway network is old, there has been almost no expansion of the network during the past few decades, and more traffic is expected. Therefore, there is currently a demand for more track capacity and, in the short term, the existing network is expected to deliver the increased capacity. The railway operators in the network have a large impact on train delays, and wheel failures are one large contributor of delays. Delays destroy capacity and, therefore, capacityconsuming failures, such as abnormal wheels, need to be minimised. This can be achieved by using appropriate condition monitoring for the wheels on the track to find potential capacity consumers before failures happen. Therefore, the condition of the wheel-rail interface is important, since the state of the wheel influences that of the rail and vice versa. The monitoring of rail profiles is already being performed, but the monitoring of wheel profiles is still in the development phase. This thesis treats the applications and performance assessment of a wheel profile measurement system (WPMS), and presents case studies focusing on its system and measurement performance. The proposed applications concern how the information from the WPMS can be integrated with information from other data sources and with physical models to obtain a true current picture of the wheel behaviour. The thesis investigates the measurement performance of the WPMS by using a paired T-test and a number of quality measures, e.g. the reproducibility and repeatability, the precision-to-tolerance ratio and the signal-to-noise ratio. In conclusion, this thesis shows that the WPMS works well with an expected level of reliability in a harsh climate with respect to its measurement and system performance. By combining other data with the data from the WPMS, potentially abnormal wheels can be found in an early stage if the proposed new maintenance limit for the wheel parameter of the flange height is implemented. Furthermore, through adding a physical model to the process, the real contact condition of the actual wheel-rail interface can be evaluated and measurement deviations can be found. However, the wheel parameters, as well as the entire profile, need a high measurement quality with little variation, which seems to be an issue with respect to the measurement performance when advanced calculations are to be done. Therefore, a new approach for evaluating measurement performance has been developed using established statistical tools and quality measures with predefined acceptance limits; with the help of this approach, one can differentiate between the variation in the measurements originating in the different measurement units and the variation originating in the wheels. This new approach can be applied to judge the measurement performance of wheel profile condition-monitoring systems, and can also be implemented for other condition-monitoring systems to evaluate their measurement performance. Finally, this approach promotes the development of a condition-based maintenance policy by providing more reliable information for maintenance decision makers.
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| 5. |
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| 6. |
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| 7. |
- Famurewa, Stephen Mayowa, et al.
(författare)
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Framework for performance based maintenance contracting
- 2012
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The achievement of maintenance objectives is the pursuit of any maintenance department, as this will support the achievement of the overall business objectives. Using in-house or outsourced maintenance service provider is a decision which poses challenge to a lot of organisations. Should the decision be for outsourcing, the next concern is the selection of the most appropriate strategy suitable for the business environment, structure and philosophy. This article gives a detailed description of innovative maintenance contracting strategy namely performance based maintenance contracting. We have presented a structured framework as well as a monitoring tool for the mentioned outsourcing strategy to facilitate easy implementation.
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| 8. |
- Famurewa, Stephen Mayowa, et al.
(författare)
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Railway Maintenance Performance : Perspective for Improvement
- 2012
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Railway transport system is complex and requires effective maintenance to achieve the business goal of safe, economic and sustainable transportation of passengers and goods. The maintenance service either provided by internal or external agents is anticipated to reach specified objectives. The major objective of maintenance is to assure dependable infrastructure with the available resources, to meet operational target and other business objectives of infrastructure manager. This however requires continuous improvement through effective performance measurement and management. This article has identified some salient criteria or perspective of maintenance process that are essential to quantify the impact of past maintenance decisions and actions. The challenges of developing and implementing maintenance performance system in the railway industries are discussed. A synthesised system of maintenance performance measurement is also suggested, this emphasises the important performance aspects. A case study of a line section on the heavy haul line with mixed traffic, belonging to the Swedish railway network is presented as well to demonstrate the analytical perspective of performance indicators. This will enhance the identification of improvement opportunities in railway infrastructure maintenance. Such improvements will support the overall business goal of meeting service quality and capacity target of infrastructure manager.
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| 9. |
- Fuqing, Yuan, et al.
(författare)
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Anomaly detection using support vector machines on overhead contact wire
- 2013
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This paper describes an anomaly detection method on the Overhead Contact Wire (OCW) in electrified railway system. The fundamental basic of contact wire is described. Their mechanical property and thermal property are discussed. The principle of the current collection through the overhead wire is described in brief. Some classical mechanic dynamic models between the pantograph and overhead contact wire are presented. Concentrating on the anomaly detection using vertical acceleration signal, this paper proposes a support vector regression based method to detect the anomaly detection on the surface of the overhead contact wire. The Support Vector Regression (SVR) is used to model the dependency between vertical acceleration and the other factor such as uplift, train speed, height of the wire. Correlation is used to find the significant factors which influence the vertical acceleration. The SVR model is used to de-trend the vertical acceleration signal. The statistical model is proposed to find the anomaly points on the contact wire.
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| 10. |
- Fuqing, Yuan, et al.
(författare)
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Complex system reliability evaluation using support vector machine
- 2010
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Support Vector Machine (SVM) is a data mining technique that has been successfully used in classification problems, starting from a known training data set (TDS). In systems modeled as networks, SVM has been used to classify the state of a network as failed or operating and jointly combined in a Monte Carlo sampling approach to approximate the network reliability. The analytical expression of the binary function (failed/operating) produced by SVM is difficult to be understood, since it generally involves the evaluation of non-linear operators, which consider a subset of the TDS, called Support Vectors (SV) and sampled system states. In this paper a different approach is proposed to assess system reliability. Information about path and cut sets is obtained directly from SV, without considering the analytical expression of the binary function produced by SVM. From here the system reliability is approximated directly. Several examples illustrate the approach.
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