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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 usefulness of the computerized maintenance management systems (CMMS) through improved usability. Design/methodology/approach -The paper opted for an exploratory study using interviews, one expert focus group discussion, and observations. Findings -The paper provides insights on specific usability characteristics that can be adapted to eMaintenance solutions for industrial usage, e.g. aviation and process industry. The findings show that the current implementations of eMaintenance solutions in CMMS, in many cases, suffer from an insufficient level of usability. This has led to usability issues resulting in errors and mistakes. The result is a call for a more user-based focus, in which, the system needs to be easily understood, easily navigated, containing the necessary information to conduct maintenance tasks, tracking of the work conducted and who was involved, and the system needs to be compatible with other systems so that necessary information can be accessed via the CMMS. Research limitations/implications -Because of the chosen research approach, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further. Practical implications -The paper includes implications for the development of a CMMS, which could have positive effects for maintenance tasks. Originality/value -This paper fulfills an identified need to study how CMMS actually fulfill the task they are designed to do.

Decision-making in maintenance has to be augmented to instantly understand and efficiently act, i.e. the new know. The new know in maintenance needs to focus on two aspects of knowing: 1) what can be known and 2) what must be known, in order to enable the maintenance decision-makers to take appropriate actions.Hence, the purpose of this paper is to propose a concept for knowledge discovery in maintenance with focus on Big Data and analytics. The concept is called Maintenance Analytics (MA). MA focuses in the new knowledge discovery in maintenance. MA addresses the process of discovery, understanding, and communication of maintenance data from four time-related perspectives, i.e. 1) "Maintenance Descriptive Analytics (monitoring)"; 2) "Maintenance Diagnostic Analytics"; 3) "Maintenance Predictive Analytics"; and 4) "Maintenance Prescriptive analytics".Keywords: big data, maintenance analytics, eMaintenance, Knowledge discovery, maintenance decision support. INTRODUCTIONThe dynamic global and local business scenarios put new demands on the decision-making processes in an organisation.The new decision-making processes need to provide enhanced capability for knowledge discovery online and in real-time. To increase the overall business efficiency, organisations need to implement a knowledge discovery platform in their core processes such as business, operation, and maintenance. Knowledge discovery is depended on availability if accurate and consistence data and information.Today, enterprises are overwhelmed by managing data and its logistics. It can be testified that there is a growing gap between data generation and data understanding (Witten et. al, 2011). It can be considered that decisions are also becoming more complex with greater uncertainty, increasing time pressure, more rapidly changing conditions, and higher stakes (Busemeyer & Pleskac, 2009). The increased information needs and the development of Information and Communication Technology (ICT) have added velocity to everything that is done within an organization through transforming the business process into eBusiness (Lee, 2003). The knowledge discovery, which is an essentially a major aspect for maintenance decision support; is usually done by discovering special pattern of data, i.e. by clustering together data that share certain common properties (Wang, 1997).Extensive application of ICT and other emerging technologies facilitate easy and effective collection of data and information (Parida, 2006;Candell et al., 2009). In maintenance, enhanced use of ICT facilitates the development of artefacts (e.g. frameworks, tools, methodologies and technologies); which aim to support maintenance decision-making. These artefacts also enable improvement of different maintenance approaches, such as; preventive maintenance and corrective maintenance. Furthermore, ICT provide additional capabilities, which can be used within diagnostic and prognostic processes. The prognostic and diagnostic processes in an enterprise can be facilitated through provision of pr...

Abstract-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 highdimensional 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.

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 nonlinear systems. The approach assigns each sample a local outlier score indicating how much one sample deviates from others in its locality. Specifically, the local outlier score is defined as a relative measure of local density between a sample and a set of its neighboring samples. To achieve smoothness in the measure, we adopt the Gaussian kernel function. Further, to enhance its discriminating power, we use adaptive kernel width: in high-density regions, we apply wide kernel widths to smooth out the discrepancy between normal samples; in low-density regions, we use narrow kernel widths to intensify the abnormality of potentially anomalous samples. The approach is extended to an online mode with the purpose of detecting anomalies in stationary data streams. To validate the proposed approach, we compare it with several alternatives using synthetic datasets; the approach is found superior in terms of smoothness, effectiveness and robustness. A further experiment on a real-world dataset demonstrated the applicability of the proposed approach in fault detection tasks.