An artificial neural network approach for predicting the performance of ship machinery equipment

Inadequate ship machinery maintenance can increase equipment failure posing a threat to the environment, affecting ship performance, having a great impact in terms of business losses by reducing ship availability and increasing downtime and moreover increasing the potential of major accidents occurring, endangering lives onboard. Efforts have being made to transform corrective/preventive maintenance techniques into predictive ones. Condition monitoring is considered as a major part of predictive maintenance. It assesses the operational health of equipment, in order to provide early warning of potential failure such that preventive maintenance action may be taken. Condition monitoring is defined as the collection and interpretation of the relevant equipment parameters for the purpose of the identification of the state of equipment changes from normal conditions and trends of the health of the equipment. The equipment condition and the fault developing trend are often highly nonlinear and time-series based. Artificial Neural Networks (ANNs) can be used due to their potential ability in nonlinear time-series trend prediction. Therefore this paper proposes the use of an autoregressive dynamic time series ANN in order to monitor and predict selected physical parameters of ship machinery equipment that contribute to the overall performance and availability, in order to predict their future values that will illustrate their performance state that will eventually lead to the correct maintenance actions and decisions

Using artificial neural network-self-organising map for data clustering of marine engine condition monitoring applications

Condition monitoring is the process of monitoring parameters expressing machinery condition, interpreting them for the identification of change which could indicate developing faults. Data processing is important in a ship condition monitoring software tool, as misinterpretation of data can significantly affect the accuracy and performance of the predictions made. Data for key performance parameters for a PANAMAX container ship main engine cylinder are clustered using a two-stage approach. Initially, the data is clustered using the artificial neural network (ANN)-self-organising map (SOM) and then the clusters are interclustered using the Euclidean distance metric into groups. The case study results demonstrate the capability of the SOM to monitor the main engine condition by identifying clusters containing data which are diverse compared to data representing normal engine operating conditions. The results obtained can be further expanded for application in diagnostic purposes, identifying faults, their causes and effects to the ship main engine

Collection and analysis of data for ship condition monitoring aiming at enhanced reliability and safety

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

Ship sensors data collection and analysis for condition monitoring of ship structures and machinery systems

With the advancements in technology, sensors and predictive maintenance, the concept of smart ships aims in using data to enhance ship performance. The INCASS project aims in integrating robotic platforms, structural and machinery reliability tools in order to enhance ship inspection, maintenance, safety and performance. In order to achieve this, sensors are installed onboard three case studies, for monitoring hull structural characteristics and machinery parameter measurements are also monitored and data are collected in order to inspect and examine machinery systems and parameters behaviour through condition monitoring. Moreover, INCASS also addresses and identifies the methods for transforming the real time monitoring data (raw data), collected from the onboard measurement campaign using permanent sensors or portable equipment or a combination of both, into meaningful, useful data and information that will be utilised in developed structural and machinery reliability analysis and assessment tools. Furthermore, the developed tools using the information from the onboard data collection activity will be capable of calculating and assessing the performance and reliability of the ship, which will provide input into a decision support system capable of addressing emergency decision making and assisting in the overall decision making process for repair, maintenance and optimised ship operations

Combination of reliability tools and artificial intelligence in a hybrid condition monitoring framework for ship machinery systems

Inadequate ship machinery maintenance can increase equipment failure posing a threat to the environment, affecting performance, having a great impact in terms of business losses by reducing ship availability, increasing downtime and moreover increasing the potential of major accidents occurring and endangering lives on-board. With high cost of ownership and overburdened crew, ship maintenance has become one of the major challenges in the marine industry. Though the industry is still predominantly reliant on a time-based, prescriptive approach to maintenance, technological advances, heightened expectation and competitive requirements as to ship availability and efficiency and the influence of the data revolution on vessel operations, have resulted in considerable interest in advanced maintenance techniques and favour a properly structured condition-based maintenance regime. In this respect, this thesis develops a hybrid framework oriented towards ship machinery condition monitoring utilising a combination of reliability tools (Fault Tree Analysis, Failure Modes & Effects Analysis, Reliability Block Diagrams) and data-driven approaches based on artificial neural networks (Self-Organising Maps, Nonlinear Autoregressive, Multilayer Perceptron). The above assist in identifying critical ship machinery systems and components and subsequently monitoring their condition through the employment of data clustering, time series forecasting, diagnostic and health assessment, leading to advisory generation of appropriate maintenance actions and recommendations. The above framework is applied to the case study of a Panamax container ship main engine for system, subsystem and component level and the results are validated with actual data recorded onboard. Sensitivity and cost benefit analysis are also presented. Key results include amongst others the identification of critical systems through a systematic approach, the ability of the Self-Organising Map to cluster data and monitor the status of the main engine and the forecasting capabilities of the Nonlinear Autoregressive time series neural networks to analyse available main engine data with high forecasting accuracy.Keywords: Artificial neural networks, data analysis, reliability tools, condition monitoring, predictive maintenance, maritime industryInadequate ship machinery maintenance can increase equipment failure posing a threat to the environment, affecting performance, having a great impact in terms of business losses by reducing ship availability, increasing downtime and moreover increasing the potential of major accidents occurring and endangering lives on-board. With high cost of ownership and overburdened crew, ship maintenance has become one of the major challenges in the marine industry. Though the industry is still predominantly reliant on a time-based, prescriptive approach to maintenance, technological advances, heightened expectation and competitive requirements as to ship availability and efficiency and the influence of the data revolution on vessel operations, have resulted in considerable interest in advanced maintenance techniques and favour a properly structured condition-based maintenance regime. In this respect, this thesis develops a hybrid framework oriented towards ship machinery condition monitoring utilising a combination of reliability tools (Fault Tree Analysis, Failure Modes & Effects Analysis, Reliability Block Diagrams) and data-driven approaches based on artificial neural networks (Self-Organising Maps, Nonlinear Autoregressive, Multilayer Perceptron). The above assist in identifying critical ship machinery systems and components and subsequently monitoring their condition through the employment of data clustering, time series forecasting, diagnostic and health assessment, leading to advisory generation of appropriate maintenance actions and recommendations. The above framework is applied to the case study of a Panamax container ship main engine for system, subsystem and component level and the results are validated with actual data recorded onboard. Sensitivity and cost benefit analysis are also presented. Key results include amongst others the identification of critical systems through a systematic approach, the ability of the Self-Organising Map to cluster data and monitor the status of the main engine and the forecasting capabilities of the Nonlinear Autoregressive time series neural networks to analyse available main engine data with high forecasting accuracy.Keywords: Artificial neural networks, data analysis, reliability tools, condition monitoring, predictive maintenance, maritime industr

Application of NARX neural network for predicting marine engine performance parameters

Though the maritime industry is still predominantly reliant on a time-based, prescriptive approach to maintenance, the increasing complexity of shipboard systems, heightened expectation and competitive requirements as to ship availability and efficiency and the influence of the data revolution on vessel operations, favour a properly structured Condition Based Maintenance (CBM) regime. In this respect, Artificial Neural Networks (ANNs) can be applied for predictive maintenance strategies assisting decision makers to select appropriate maintenance actions for critical ship machinery. This paper develops a Nonlinear Autoregressive with Exogenous Input (NARX) ANN for forecasting future values of the exhaust gas outlet temperature of a marine main engine cylinder. A detailed sensitivity analysis is conducted to examine the performance and robustness of the NARX model for variations in the time series data, demonstrating virtuous performance and generalisation capabilities for forecasting and the ability to employ the model for monitoring and prognostic applications

Implementing unsupervised learning algorithm for marine engine data clustering applications

Data preparation and processing is of great importance in a ship condition monitoring tool, as inaccurate and misinterpretation of data can significantly affect the condition monitoring accuracy and performance. Data for performance parameters related to the case study of a Panamax container ship main engine are clustered using an artificial neural network, the Self-Organizing Map (SOM). Neighbouring clusters are compared through a distance metric to examine the existence of data similarities. Additionally, the SOM has a supplementary functionality of identifying data clusters exceeding thresholds, consequently providing diagnostics connected to a Failure Mode and Effects Analysis (FMEA) for the main engine, providing valuable insight and information regarding potential faults. The SOM model is validated through actual data extracted from the case study. Moreover, simulated data representing data exceeding alarm levels for the engine fuel oil system demonstrate the capabilities of the SOM clustering process in combination with the associated FMEA results

Ship sensors data collection and analysis for condition monitoring of ship structures and machinery systems

With the advancements in technology, sensors and predictive maintenance, the concept of smart ships aims in using data to enhance ship performance. The INCASS project aims in integrating robotic platforms, structural and machinery reliability tools in order to enhance ship inspection, maintenance, safety and performance. In order to achieve this, sensors are installed onboard three case studies, for monitoring hull structural characteristics and machinery parameter measurements are also monitored and data are collected in order to inspect and examine machinery systems and parameters behaviour through condition monitoring. Moreover, INCASS also addresses and identifies the methods for transforming the real time monitoring data (raw data), collected from the onboard measurement campaign using permanent sensors or portable equipment or a combination of both, into meaningful, useful data and information that will be utilised in developed structural and machinery reliability analysis and assessment tools. Furthermore, the developed tools using the information from the onboard data collection activity will be capable of calculating and assessing the performance and reliability of the ship, which will provide input into a decision support system capable of addressing emergency decision making and assisting in the overall decision making process for repair, maintenance and optimised ship operations