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Abstract The proposed research aims to tackle the issue of predictive ship machinery inspection by enhancing reliability and safety, avoiding accidents, and protecting the environment. This paper presents the development of Machinery Risk/Reliability Analysis (MRA). The innovation of this model is the consideration of components' failure and degradation utilizing raw recorded data. MRA takes into account the system's dynamic state change, concerning failure rate variation over time. The presented methodology involves the generation of Markov Chain arrangement integrated with the advantages of Bayesian Belief Networks (BBNs). Following research involves components and systems interdependencies and feed the continuous dynamic probabilistic condition monitoring algorithm. Introduction Market competition has grown gradually due to continuous increase in production demand, resulting in the implementation of mechanized and automated systems which enhance targeted delivery time, quality and quantity of supply. The automation of operational processes and equipment mechanization force the development of maintenance functions and control in order to manage system failure uncertainty. The business effectiveness and efficiency are influenced by factors such as time, economic aspects, technology and innovation, quality, reliability and information management (MADU, 2000). With the intention of competing successfully, companies strive to enhance their inspection and maintenance systems, which need to be considered during the organization's strategic planning. In this respect, several definitions are provided for maintenance by various authors summarizing the notion that maintenance is a set of technical, administrative and managerial actions targeting to retain or restore the state of a sys-tem to function as required (MOBLEY et al., 2008). Nowadays, maintenance is encountered as an operational method, which is employed as a profit generating process and a cost reduction budget center through an enhanced Operation and Maintenance (O&M) strategy.
Collection and Analysis of Data for Ship Condition Monitoring Aiming at Enhanced Reliability & Safety
Raptodimos, Yiannis (University of Strathclyde) | Lazakis, Iraklis (University of Strathclyde) | Theotokatos, Gerasimos (University of Strathclyde) | Salinas, Raul (University of Strathclyde) | Moreno, Alfonso (University of Strathclyde)
Abstract This paper presents the onboard measurement campaign for the case study of a container ship and provides a customary methodology for monitoring important machinery systems. The main principle aim of this paper is to collect important machinery data and parameters from critical systems, located in the engine room of the ship, by determining systems to be monitored, scenarios for monitoring, sensors and suitable portable equipment and physical parameters to be inspected. Introduction Maintenance is an important contributor to reach the intended life-time of technical capital assets and is defined as a combination of all the technical and associated administrative activities required to keep equipment, installations and other physical assets in the desired operating condition or to restore them to this condition (BS, 1993). Maintenance also includes the engineering decisions and associated actions that are required for the optimisation of specified equipment capability, meaning the ability to perform a specified function within a range of performance levels that may relate to capacity, rate, quality, safety and responsiveness. Furthermore, maintenance costs are a significant portion of the operational cost and breakdowns and downtime have an impact on plant capacity, product quality and cost of production as well as on health, safety and the environment. Thus, nowadays, the shift of maintenance as a strategic perspective within a company organization can be attributed to the utilisation of more advanced technologies, increased emphasis on safety, new environmental legislations, optimised operations with increased fuel efficiency and reduction of emissions (Parida et al., 2015).
- Transportation > Marine (1.00)
- Transportation > Freight & Logistics Services > Shipping (0.49)
- Information Technology > Architecture (0.70)
- Information Technology > Sensing and Signal Processing (0.69)
- Information Technology > Communications > Networks (0.47)