Advanced ship systems condition monitoring for enhanced inspection, maintenance and decision making in ship operations

Structural and machinery failures in the day-to-day ship operations may lead to major accidents, endangering crew and passengers onboard, posing a threat to the environment, damaging the ship itself and having a great impact in terms of business losses. In this respect, this paper presents the INCASS (Inspection Capabilities for Enhanced Ship Safety) project which aims bringing an innovative solution to the ship inspection regime through the introduction of enhanced inspection of ship structures, by integrating robotic-automated platforms for on-line or on-demand ship inspection activities and selecting the software and hardware tools that can implement or facilitate specific inspection tasks, to provide input to the Decision Support System (DSS). Enhanced inspection of ships will also include ship structures and machinery monitoring with real time information using ‘intelligent’ sensors and incorporating structural and machinery risk analysis, using in-house structural/hydrodynamics and machinery computational tools. Moreover, condition based inspection tools and methodologies, reliability and criticality based maintenance are introduced. An enhanced central database handles ship structures and machinery data. The data is available to ship operators and are utilized by the DSS for ship structures and machinery for continuous monitoring and risk analysis of ship operations. The development and implementation of the INCASS system is shown in the case of a machinery system of a tanker ship. In this way the validation and testing of the INCASS framework will be achieved in realistic operational conditions

Dynamic risk and reliability assessment for ship machinery decision making

The proposed research, through INCASS (Inspection Capabilities for Enhanced Ship Safety) FP7 EU funded research project tackles 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 and assessment of components’risk of failure and reliability degradation by utilizing raw input 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 Chains integrated with the advantages of Bayesian Belief Networks (BBNs). INCASS project developed a measurement campaign, where real time sensor data is recorded onboard a tanker, bulk carrier and container ship. The gathered data is utilized for MRA DSS tool validation and testing. Following research involves components and systems interdependencies and feed the continuous dynamic probabilistic condition monitoring algorithm

On the safety design of radar based railway level crossing surveillance systems

Recent accidents experienced at railway level crossings are pushing researchers to design surveillance systems able to grant safety of passengers and structural integrity of trains at level crossings. The challenge is represented by granting at the same time an appropriate reliability, availability and maintainability degree despite the high safety requirements imposed by the application. The approach proposed in this paper takes into consideration the most common suggested standards used in designing this kind of systems and introduces new general concepts which demystify the use of such standards in actual applications. This paper illustrates the roadmap to be followed in general when designing level crossing monitoring systems, to minimize the risk due to object misdetection occurring on barrier closure when exploiting radar technology

Hidden markov models in reliability and maintenance

Acknowledgements The authors are grateful to two anonymous reviewers and the Editor for many valuable comments and suggestions, which have helped to improve the quality of the article. The authors gratefully acknowledge support from the Spanish Ministry of Science and In- novation - State Research Agency through grant number PID2020- 120217RB-I00. This work is supported in part by the IMAG Maria de Maeztu grant CEX2020-001105-M/AEI/10.13039/50110 0 011033. Funding for open access charge: Universidad de Granada / CBUA.Although the hidden Markov models (HMM) are very popular in many applied areas their use in reliabil- ity engineering is limited. Problems such as the selection of the HMM model by choosing the appropriate number of states, or problems of prediction of failures have not been widely covered in the literature. This paper is concerned with the use of HMMs where the state of the system is not directly observable and instead certain indicators of the true situation are provided via a control system. A hidden model can provide key information about the system dependability such as the failed component of the sys- tem, the reliability of the system and related measures. A maximum-likelihood estimator of the system reliability is obtained and its asymptotic properties are studied. Finally, the maintenance of the system is considered in this context and new preventive maintenance strategies are defined and their efficiency is measured in terms of expected cost. To prove the finite sample performance of the methodology, an extensive simulation study is developed.IMAG Maria de Maeztu CEX2020-001105-M/AEI/10.13039/501100011033State Research Agency PID2020-120217RB-I00Ministerio de Ciencia e InnovaciónUniversidad de Granada/CBU

Hidden Markov Models approach used for life parameters estimations

In modern electronics and in electrical applications design is very important to be able to predict the actual product life or, at least, to be able to provide the end user with a reasonable estimate of such parameter. It is important to be able to define the availability as a key parameter because, although other performance indicators (as the mean time before failures MTBF or mean time to failure MTTF) exist, they are often misused. To study the availability of an electrical, electronic or an electromechanical system, different methods can be used. The most common one relies on memory-less Markovian state space analysis due to the fact that a little information is needed, and under simple hypothesis, it is possible to gather some outcomes on the availability of steady state value. In this paper the authors, starting from classical approach of Markov models, introduce an extension known as Hidden Markov Models approach to overcome the limits of the previous one in estimating the system availability performance over time. Such a technique can be used to improve the logistic aspects connected with optimal maintenance planning. The provided dissertation in general can be used in different contexts without losing in generality

A review and application of hidden Markov models and double chain Markov models

A Dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in ful lment of the requirements for the degree of Master of Science. Johannesburg, 2016.Hidden Markov models (HMMs) and double chain Markov models (DCMMs) are classical Markov model extensions used in a range of applications in the literature. This dissertation provides a comprehensive review of these models with focus on i) providing detailed mathematical derivations of key results - some of which, at the time of writing, were not found elsewhere in the literature, ii) discussing estimation techniques for unknown model parameters and the hidden state sequence, and iii) discussing considerations which practitioners of these models would typically take into account. Simulation studies are performed to measure statistical properties of estimated model parameters and the estimated hidden state path - derived using the Baum-Welch algorithm (BWA) and the Viterbi Algorithm (VA) respectively. The effectiveness of the BWA and the VA is also compared between the HMM and DCMM. Selected HMM and DCMM applications are reviewed and assessed in light of the conclusions drawn from the simulation study. Attention is given to application in the field of Credit Risk.LG201

Establishment of a novel predictive reliability assessment strategy for ship machinery

There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme.There is no doubt that recent years, maritime industry is moving forward to novel and sophisticated inspection and maintenance practices. Nowadays maintenance is encountered as an operational method, which can be employed both as a profit generating process and a cost reduction budget centre through an enhanced Operation and Maintenance (O&M) strategy. In the first place, a flexible framework to be applicable on complex system level of machinery can be introduced towards ship maintenance scheduling of systems, subsystems and components.;This holistic inspection and maintenance notion should be implemented by integrating different strategies, methodologies, technologies and tools, suitably selected by fulfilling the requirements of the selected ship systems. In this thesis, an innovative maintenance strategy for ship machinery is proposed, namely the Probabilistic Machinery Reliability Assessment (PMRA) strategy focusing towards the reliability and safety enhancement of main systems, subsystems and maintainable units and components.;In this respect, the combination of a data mining method (k-means), the manufacturer safety aspects, the dynamic state modelling (Markov Chains), the probabilistic predictive reliability assessment (Bayesian Belief Networks) and the qualitative decision making (Failure Modes and Effects Analysis) is employed encompassing the benefits of qualitative and quantitative reliability assessment. PMRA has been clearly demonstrated in two case studies applied on offshore platform oil and gas and selected ship machinery.;The results are used to identify the most unreliability systems, subsystems and components, while advising suitable practical inspection and maintenance activities. The proposed PMRA strategy is also tested in a flexible sensitivity analysis scheme