Ramin Karim

Selection of maintenance strategy for aircraft systems using multi-criteria decision making methodologies

This paper, proposes the Multi-Criteria Decision Making (MCDM) methodology for selection of a maintenance strategy to assure the consistency and effectiveness of maintenance decisions. The methodology is based on an AHP-enhanced TOPSIS, VIKOR and benefit-cost ratio, in which the importance of the effectiveness appraisal criteria of a maintenance strategy is determined by the use of AHP. Furthermore, in the proposed methodology the different maintenance policies are ranked using the benefit-cost ratio, TOPSIS and VIKOR. The method provides a basis for consideration of different priority factors governing decisions, which may include the rate of return, total profit, or lowest investment. When the preference is the rate of return, the benefit-cost ratio is used, and for the total profit TOPSIS is applied. In cases where the decision maker has specific preferences, such as the lowest investment, VIKOR is adopted. The proposed method has been tested through a case study within the aviation context for an aircraft system. It has been found that using the methodology presented in the paper, the relative advantage and disadvantage of each maintenance strategy can be identified in consideration of different aspects, which contributes to the consistent and rationalized justification of the maintenance task selection. The study shows that application of the combined AHP, TOPSIS, and VIKOR methodologies is an applicable and effective way to implement a rigorous approach for identifying the most effective maintenance alternative.

Development of ICT‐based maintenance support services

Purpose – The purpose of this paper is to describe a taxonomy for an eMaintenance management framework (eMMF) based on a service‐oriented approach, in order to facilitate the development of information and communication technology (ICT)‐based maintenance support services aimed at actors within the maintenance process related to complex technical industrial systems.Design/methodology/approach – To fulfil this purpose, a case study of a modern multi‐role combat aircraft is selected as an appropriate research strategy and supported by a literature study. Empirical data are collected through interviews, workshops, document studies, and observations. A framework is developed and evaluated using a prototype within the studied case. The study is performed in close cooperation with the aircrafts type certificate holder and the customer representative and operator in one country.Findings – The proposed eMMF aids in the identification and development of ICT‐based maintenance support services tailored for specific ...

An angle-based subspace anomaly detection approach to high-dimensional data: With an application to industrial fault detection

The accuracy of traditional anomaly detection techniques implemented on full-dimensional spaces degrades significantly as dimensionality increases, thereby hampering many real-world applications. This work proposes an approach to selecting meaningful feature subspace and conducting anomaly detection in the corresponding subspace projection. The aim is to maintain the detection accuracy in high-dimensional circumstances. The suggested approach assesses the angle between all pairs of two lines for one specific anomaly candidate: the first line is connected by the relevant data point and the center of its adjacent points; the other line is one of the axis-parallel lines. Those dimensions which have a relatively small angle with the first line are then chosen to constitute the axis-parallel subspace for the candidate. Next, a normalized Mahalanobis distance is introduced to measure the local outlier-ness of an object in the subspace projection. To comprehensively compare the proposed algorithm with several existing anomaly detection techniques, we constructed artificial datasets with various high-dimensional settings and found the algorithm displayed superior accuracy. A further experiment on an industrial dataset demonstrated the applicability of the proposed algorithm in fault detection tasks and highlighted another of its merits, namely, to provide preliminary interpretation of abnormality through feature ordering in relevant subspaces.

Exploiting SOA within emaintenance

SOA is being exploited in many various business domains. One of them is industrial maintenance. In this paper, we outline an eMaintenance Platform (eMP) for maintaining complex technical systems. This platform is part of the eMaintenance Management Framework (eMMF). Our primary goal is to explore how SOA can be exploited within industrial maintenance. Our secondary goal is to provide eMP as an effective tool for organizations to conduct their maintenance and support.

Sliding Window-Based Fault Detection From High-Dimensional Data Streams

High-dimensional data streams are becoming increasingly ubiquitous in industrial systems. Efficient detection of system faults from these data can ensure the reliability and safety of the system. The difficulties brought about by high dimensionality and data streams are mainly the “curse of dimensionality” and concept drifting, and one current challenge is to simultaneously address them. To this purpose, this paper presents an approach to fault detection from nonstationary high-dimensional data streams. An angle-based subspace anomaly detection approach is proposed to detect low-dimensional subspace faults from high-dimensional datasets. Specifically, it selects fault-relevant subspaces by evaluating vectorial angles and computes the local outlier-ness of an object in its subspace projection. Based on the sliding window strategy, the approach is further extended to an online mode that can continuously monitor system states. To validate the proposed algorithm, we compared it with the local outlier factor-based approaches on artificial datasets and found the algorithm displayed superior accuracy. The results of the experiment demonstrated the efficacy of the proposed algorithm. They also indicated that the algorithm has the ability to discriminate low-dimensional subspace faults from normal samples in high-dimensional spaces and can be adaptive to the time-varying behavior of the monitored system. The online subspace learning algorithm for fault detection would be the main contribution of this paper.

Enhancing the usability of maintenance data management systems

Purpose – The purpose of this paper is to explore and study the aspects of usability related to eMaintenance solutions. The study aims to expand the domain of eMaintenance by increasing the usefuln ...

Application of information and communication technology for maintenance support information services: Transferring experiences from an eHealth solution in Sweden

Purpose – The purpose of this paper is to explore the transferability of ICT‐solutions for providing support information services from eHealth to eMaintenance.Design/methodology/approach – The applied research approach takes the form of a single‐case study supported by a literature study. Empirical data were collected through documents, observations and interviews. The conclusions of the study were verified by key informants.Findings – The study indicates some major problems that have to be overcome when implementing support information services using an ICT‐solution (e.g. to manage heterogeneous organizations; manage heterogeneous eService‐environments; and enable context and situation awareness in eServices). Further, the study has identified and graded some solutions, which address these problems and are transferable from eHealth to eMaintenance.Practical implications – The studied case is in the forefront of eHealth solutions and can therefore act as a guidance for those involved at different stages o...

Positioning embedded software maintenance within industrial maintenance

Little is known about of how software embedded in complex industrial technical systems is maintained. In this paper, we identify industrial maintenance process steps and position software maintenance activities within them. We do this in the context of JAS 39 Gripen, a multi-role combat aircraft developed by Saab.

Applications of radio frequency identification (RFID) technology with eMaintenance cloud for railway system

Radio frequency identification (RFID) helps automatic identification of objects using radio waves. This is not a new technology instead decades old and has been used during the World War II, when it was used by allied ground forces to track German bombers. It is a technology for wireless communication between a reader and a transponder/tag. This technology permits the transfer of data to the most diverse objects without the need for physical contact and uses intelligent barcodes to track items and have been successfully applied in military, security, healthcare, real time location tracking, vehicle identification and other areas. This paper is based on applications of RFID technology with eMaintenance cloud for railway system to analyze and visualize data of trains for the cost effective maintenance planning. Further, cloud computing is an emerging research area that can be utilised for acquiring an effective and efficient information logistics. Specifically, the widespread use of RFID will enable wagons to be tracked leading to better resource utilization, lower freight costs, and better maintenance. Therefore, it helps to provide greater control of the train carriages, making it easier to plan resources. However, RFID is a powerful tool that can help to improve industry proficiency, implementing this technology is not easy. Furthermore, operating RFID systems can be a challenging process. Thus, this paper is based on the application of RFID in the context of railway operation.

Adaptive Kernel Density-based Anomaly Detection for Nonlinear Systems

This paper presents an unsupervised, density-based approach to anomaly detection. The purpose is to define a smooth yet effective measure of outlierness that can be used to detect anomalies in nonl ...