A Smart Modular Wireless System for Condition Monitoring Data Acquisition

Smart sensors, big data, the cloud and distributed data processing are some of the most interning changes in the way we collect, manage and treat data in recent years. These changes have not significantly influenced the common practices in condition monitoring for shipping. In part this is due to the reduced trust in data security, data ownership issues, lack of technological integration and obscurity of direct benefit. This paper presents a method of incorporating smart sensor techniques and distributed processing in data acquisition for condition monitoring to assist decision support for maintenance actions addressing these inhibitors

The European commission’s role in marine materials, equipment and components mutual recognition certification

The European Commission has, in the past, updated regulations regarding marine operations in order to enhance safety and protection of the environment. In that respect and with the scope to enhance safety onboard ships, Regulation No 391/2009 and in particular Article 10.1 on certification of ships, suggested that European Union Recognised Organisations (EU ROs) should harmonise their rules and procedures related to certification of materials, equipment and components based on equivalent standards issued by them. As a result the EU ROs Mutual Recognition (MR) scheme was initiated. This paper investigates the current implementation of the requirements of Article 10 through a developed questionnaire and case studies. The results have shown that the current level of implementation is regarded as acceptable and safety is adhered to the highest standard. Moreover, the current implementation needs further improvement and harmonisation of individual rules may be required. EU RO requirements can be further developed in the future as the overall process matures. Additional information and dissemination of the overall MR process is also required engaging additional stakeholders in the marine industry. However, the expansion of the scheme presents challenging issues to overcome including the global acceptance of the MR certification

Condition monitoring for enhanced inspection, maintenance and decision making in ship operations

This paper presents the INCASS (Inspection Capabilities for Enhanced Ship Safety) project which brings innovative solutions to the ship inspection regime 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 in- put to the Decision Support System (DSS). Enhanced inspection of ships includes 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. Condition based inspection tools and methodologies, reliability and criticality based maintenance are introduced. An enhanced central database handles ship structures and machinery data. The development and implementation of the INCASS system is shown in the case of ship machinery systems. In this way the validation and testing of the INCASS framework will be achieved in realistic operational conditions

Vibration edge computing in maritime IoT

IoT and the Cloud are among the most disruptive changes in the way we use data today. These changes have not significantly influenced practices in condition monitoring for shipping. This is partly due to the cost of continuous data transmission. Several vessels are already equipped with a network of sensors. However, continuous monitoring is often not utilised and onshore visibility is obscured. Edge computing is a promising solution but there is a challenge sustaining the required accuracy for predictive maintenance. We investigate the use of IoT systems and Edge computing, evaluating the impact of the proposed solution on the decision making process. Data from a sensor and the NASA-IMS open repository were used to show the effectiveness of the proposed system and to evaluate it in a realistic maritime application. The results demonstrate our real-time dynamic intelligent reduction of transmitted data volume by without sacrificing specificity or sensitivity in decision making. The output of the Decision Support System fully corresponds to the monitored system's actual operating condition and the output when the raw data are used instead. The results demonstrate that the proposed more efficient approach is just as effective for the decision making process

mini-ELSA: using Machine Learning to improve space efficiency in Edge Lightweight Searchable Attribute-based encryption for Industry 4.0

In previous work a novel Edge Lightweight Searchable Attribute-based encryption (ELSA) method was proposed to support Industry 4.0 and specifically Industrial Internet of Things applications. In this paper, we aim to improve ELSA by minimising the lookup table size and summarising the data records by integrating Machine Learning (ML) methods suitable for execution at the edge. This integration will eliminate records of unnecessary data by evaluating added value to further processing. Thus, resulting in the minimization of both the lookup table size, the cloud storage and the network traffic taking full advantage of the edge architecture benefits. We demonstrate our mini-ELSA expanded method on a well-known power plant dataset. Our results demonstrate a reduction of storage requirements by 21% while improving execution time by 1.27x

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

Evaluation of home-based rehabilitation sensing systems with respect to standardised clinical tests

With increased demand for tele-rehabilitation, many autonomous home-based rehabilitation systems have appeared recently. Many of these systems, however, suffer from lack of patient acceptance and engagement or fail to provide satisfactory accuracy; both are needed for appropriate diagnostics. This paper first provides a detailed discussion of current sensor-based home-based rehabilitation systems with respect to four recently established criteria for wide acceptance and long engagement. A methodological procedure is then proposed for the evaluation of accuracy of portable sensing home-based rehabilitation systems, in line with medically-approved tests and recommendations. For experiments, we deploy an in-house low-cost sensing system meeting the four criteria of acceptance to demonstrate the effectiveness of the proposed evaluation methodology. We observe that the deployed sensor system has limitations in sensing fast movement. Indicators of enhanced motivation and engagement are recorded through the questionnaire responses with more than 83% of the respondents supporting the system’s motivation and engagement enhancement. The evaluation results demonstrate that the deployed system is fit for purpose with statistically significant ( ϱc>0.99 , R2>0.94 , ICC>0.96 ) and unbiased correlation to the golden standard

The European Commission’s role in marine materials, equipment and components certification mutualisation

The European Commission in the past has updated the regulations regarding marine operations in order to enhance safety and protection of the environment. In that respect and with the scope to enhance safety onboard ships, Regulation No 391/2009 and in particular Article 10 on certification of ships suggested that EU Recognised Organisations (EU ROs) should harmonise their rules and procedures related to certification of materials, equipment and components based on equivalent standards issued by them. As a result the EU ROs Mutual Recognition (MR) scheme was initiated. This article investigates the current implementation of the requirements of Article 10 through the developed questionnaire and case studies. The results have shown that while safety is considered at the highest level, the current implementation needs further improvement and harmonisation of individual rules which can be delivered as the process matures. Additional information and dissemination of the overall MR process is also required engaging all marine industry. The current implementation is regarded as acceptable; however, the expansion of the scheme is a cause for concern. Finally, global acceptance of the MR scheme remains a challenge to be overcome

CAEFL: composable and environment aware federated learning models

Federated Learning allows multiple distributed agents to contribute to a global machine learning model. Each agent trains locally and contributes to a global model by sending gradients to a central parameter server. The approach has some limitations: 1) some events may only occur in the local environment, so a global model may not perform as well as a specialized model; 2) changes in the local environment may require an agent to use some dedicated model, that is not available in a single global model; 3) a single global model approach is unable to derive new models from dealing with complex environments. This paper proposes a novel federated learning approach, CAEFL, that is local environment aware and composes new dedicated models for new complex environments. CAEFL is implemented in Elixir to exploit transparent distribution, pattern matching, and hot-code-swapping. Pattern matching is used to transform environment sensors data to corresponding tags and aggregate data with the same environment tags on agents. It is also used on parameter server to match client’s push/pull request for these tagged models. It enables a declarative way for environment aware federated learning approach. CAEFL outperforms state of the art federated learning by 7-10% for the MNIST dataset and 2% for the FashionMNIST dataset in specific and complex environments

Real-time Recursive Risk Assessment Framework for Autonomous Vehicle Operations

Existing risk assessment (RA) methodology used for autonomous vehicle (AV) development and validation is insufficient for future AV operations. Existing frameworks operate based on processes such as hazard analysis and risk assessment (HARA) where risk is defined based on functional hazardous event severity and the likelihood of occurrence. This is a static process performed during the development stage and relies on prior lessons learnt and know-how. A drawback of this is the omission of potential complex environments that could occur during real-time – especially with more stringent safety requirements for AV operating at higher automation levels. Therefore, there is a need for an additional framework to further enhance the safety levels of the AV, focusing on real-time instead of static risk assessment during development. In this paper, a novel real-time recursive RA framework (ReRAF) addresses the gap by creating a novel risk representation, predictive risk number (PRN), and eventual safety levels (SLs) in the temporal and spatial domain. This approach focuses on risk assessment based on AV collision to the detected hazardous object and controllability of the AV. A dynamic recursive RA continuously captures potentially hazardous events in real-time and compares them with past occurrences to predict future safety actions. ReRAF provides a continuous improvement on the RA and acts as an additional safety layer for AV operations