Towards a model-based condition assessment of complex marine machinery systems using systems engineering
In the present paper, a systems engineering methodology is presented for the analysis and condition assessment of complex marine machinery systems. Two important characteristics of these systems are (i) that they comprise of a multitude of subcomponents which influence the overall system condition/performance and (ii) the continuously varying operating and environmental conditions. The methodology presented herein is capable to evaluate the system level effects of component degradation and faulty states under realistic system operation. By virtue of this, it is employed along with sensor signal data for the identification of degraded states and the allocation of the problem to specific system components. The modelling platform used in this work is the DNVGL COSSMOS (Complex Ship System Modelling & Simulation). At the methodological level, an automated model-based sensitivity analysis is conducted with respect to a set of component degradation/failure modes. The latter is used along with a clustering algorithm for the precise allocation of the failure to specific components and system particulars. The selected case-study is the Diesel-electric marine propulsion system of a 2300 tonnes DWT (deadweight tonnage) anchor handling vessel embedded with its cooling network. Based on the results, the approach is capable to successfully identify faults at various subcomponents of the cooling network system including pumps, regulating valves, heat exchangers and piping. Due to the fact that the system is treated in an integrated manner, a fault can be identified in a component using sensor signals placed in other system locations. 1. BACKGROUND Within the maritime industry there is a strong trend of machinery components and systems becoming more integrated and thereby also more complex. Advanced control and monitoring systems are enablers for safer, greener and smarter maritime transport of goods and people but also add to the level of complexity with regards to design, commissioning and operation. The key drivers behind this trend are stricter legislation related to emissions and the tough competitive market driving performance/cost optimization. When different components and sub-systems from a multitude of suppliers are combined to fulfil one or more functions on board a ship, the need for increased multidiscipline knowledge and system understanding is obvious. It can be hard to determine and verify all possible failure modes and their effect, and thereby also the overall system reliability and safety. During operation, the increased level of integration and dependence of embedded control functions significantly escalates the level of information and possible failure scenarios that the operator needs to understand and handle. The fact that ship machinery systems are operated in a host of different working modes under continuously varying operating and environmental conditions further complicates the situation, and makes it hard to deal with process anomalies in an effective manner. Within this context, a systems engineering approach is required in order to achieve and document a safety level equivalent to the traditional and well proven designs. One of the first studies on marine energy conversion systems dealing with the computer-aided design of the overall system is (Ito & Akagi, 1986). Later, in (Campora & Figari, 2003; Hansen, Adnanes, & Fossen, 2001; Jefferson, Zhou, & Hindmarch, 2003 ; Kyrtatos & Lambropoulos, 2000; Kyrtatos, Theodossopoulos, Theotokatos, & Xiros, Alexandros S. Zymaris et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. EUROPEAN CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2016 2 1999; Pedersen & Pedersen, 2012; Vrijdag, Stapersma, & Terwisga, 2009) process modelling approaches have been used for the analysis of marine propulsion systems. In (Dimopoulos, 2009; Dimopoulos, Kougioufas, & Frangopoulos, 2008; Tostevin & Nealy, 2003) the analysis and optimisation of generic marine energy production systems mostly focusing on steady-state synthesis, design and operation is presented. Moreover, in (Rüde, 2006) the cooling network of a conventional propulsion vessel with a four stroke Diesel engine has been modelled, the purpose of the analysis is the enhancement of safety and reliability studies. COSSMOS (Dimopoulos, Georgopoulou, Stefanatos, Zymaris, & Kakalis, 2014; Dimopoulos & Kakalis, 2010; Kakalis & Dimopoulos, 2012) is DNVGL’s model-based systems engineering simulation platform. What differentiates the aforementioned approaches from DNVGL COSSMOS is that each one of them is dedicated to a specific type of analysis for a specific system/component. On the other hand, COSSMOS is a generic marine machinery systems platform that is capable of performing steady-state and transient analysis as well as more complex tasks such as optimisation and parameter estimation for all machinery components that can be found on-board a vessel. Another megatrend within the maritime industry is the remarkable growth in broadband satellite installations on board large ships over the past few years. Components and systems can be integrated with sensors that measure, log and send ashore operational system data. Access to comprehensive operational data from on board system sensors that is transferred to shore on a live basis will lead to tremendous opportunities for all stakeholders within the maritime industry to make shipping smarter, safer and more cost-efficient. This revolution in ship connectivity will enable the implementation of a host of new applications such as performance and environmental monitoring, energy efficiency optimisation, remote control/autonomy and condition monitoring (CM). CM is the process of monitoring a set of parameters of condition in a system in order to identify a significant change that is indicative of a failure or a developing fault that is affecting it’s function. Within the maritime industry the use of condition monitoring techniques to perform, for instance, condition based maintenance (CBM) on machinery components and systems is still very much in an infant stage. There are some early adopters within segments with high value vessels such as cruise and offshore but for most ships the on board maintenance tasks are carried out based on basis of either calendar or running hours (Coull, 2015). The need to stay competitive in a tough market is however pushing more and more ship owners and managers towards improving maintenance procedures, boosting uptime and cutting costs. The uptake of CBM is therefore expected to accelerate over the coming years. Diagnostics and prognostics algorithms for implementing a condition monitoring system are based on one of two main approaches (G Manno, Knutsen, & Vartdal, 2014)  A model-based (first principles physical model) approach (Isermann, 2011)  A data-driven (statistical and data mining) approach (Baraldi, Maio, Genini, & Zio, 2015; Ge, Song, & Gao, 2013; Wang, 1999; Yin, Ding, Xie, & Luo, 2014) One major challenge for the maritime industry is the lack of reliable statistical failure data for ship equipment similar to the OREDA database for the offshore industry (SINTEF, 2002). Additionally, machinery systems are run very differently from ship type to ship type and also under continuously varying operating and environmental conditions. These facts strongly support the use of model– based approaches within shipping since it requires knowledge about the physics and function of the system and not large amounts of previous failure data. In (Grimmelius et al., 1999) a comparison between data-driven and first principle model-based CM approaches is presented for two marine machinery case studies. The systems studied are: (i) a Diesel engine, using only the torsional vibration of the crank shaft, and (ii) a compression refrigeration plant, using many different sensors. Both CM approaches (data-driven and model-based) show promising results. In (Monnin, Voisin, Leger, & Iung, 2011) and (Voisin, Medina-Oliva, Monnin, Leger, & Iung, 2013) a knowledge structuring scheme for achieving fleet-wide PHM utilizing both modelling and monitoring is proposed. Utilizing contextual information about the system and components by means of semantic modelling is identified as a key issue in order to allow for consideration of fleet component similarities and heterogeneities. Monitoring data from a given component are considered within their context and thereby enhancing the identification of the corresponding health condition. The fleet dimension can provide knowledge and data to improve both diagnostic and prognostic models. In the maritime sector a fleet could both be a ship owner fleet of typically 10 to a few hundred vessels or the entire fleet of a classification society such as DNVGL with a fleet of more than 13,000 vessels. Moreover, in (Grimmelius et al., 1999) the aspect of sensor redundancy using virtual sensing is investigated. Modelbased technologies are a key enabler for virtual sensing / analytical redundancy (Chen & Patton, 1999). In (Blanke, 2001) a model-based methodology for the diagnosis of faults in ship propulsion systems and fault-tolerant control is presented. In this work mathematical models for ship speed, propeller and prime mover (i.e. Diesel engine) have been utilised. Diesel engines are among the most expensive, critical and maintenance intensive components on board the ship, and as a consequence many first principle condition monitoring approaches have been proposed in literature (Kouremenos & Hountalas, 1997; Watzenig, Sommer, & EUROPEAN CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2016 3 Steiner, 2013). Although Diesel engine CM is a well investigated subject, there are less studies found for the auxiliary cool
Anomaly Detection Using Dynamical Linear Models and Sequential Testing on a Marine Engine System
This paper presents a study on the use of Dynamical Linear Models for anomaly detection and condition monitoring of a marine engine system. Various sensors are installed at different places within the engine system and records essential parameters such as power output from the engine, engine speed, bearing temperatures and various other temperatures, speeds and pressures for selected engine components. The idea is to utilize the information in these sensor signals in order to monitor the condition of the engine. Such a condition monitoring system should include means of fault detection, diagnosis and prognostics, where robust anomaly detection is a prerequisite for reliable management of the system. Dynamical Linear Models (DLM) constitute a flexible framework for modelling of sensor signals, where the sensor signals are modelled conditional on some latent states, and the model provides forecasts of the signals that can be compared to new sensor readings. Statistical sequential model testing will then be performed on the forecast errors and model breakdown can be an indication of deviation from normal conditions and possible impending failures of the engine system. This will then call for further diagnostics and prognostics tasks to interpret the nature of the deviations. The Dynamical Linear Model framework can accommodate a range of candidate models. However, very complicated models in high dimensions may be computationally expensive to estimate and apply, so various pre-processing techniques are investigated in this paper to improve model performance, including simple regression models, cluster analysis and principal component transformation. Erik Vanem et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Laser ignition of explosives: Effects of gas pressure on the threshold ignition energy
Abstract This paper describes a method for measuring the effect of initial gas pressure and the type of gas on the threshold ignition energy of several high explosives. The method is based on a 300 W CW CO2-laser. An extensive study of these parameters for PETN and RDX is also presented. These high explosives all show a strong decrease in ignition energy/power with increasing initial gas pressure. The gases tested are air and nitrogen. The studied pressures are in the interval 0.1 to 10 MPa. The ignitability of PETN and RDX was tested at a laser pulse width of 1.2 ms. All tested high explosives had a lower ignition energy/power in air than in nitrogen at low pressures ( 4 MPa). For TNT the time to ignition, measured as a function of air pressure, decreased with increasing pressure. These results are interpreted as a multiple phase ignition process. The method was also used for obtaining ignition data for ...