An application of sensor-based anomaly detection in the maritime industry

Brandsæter, Andreas; Manno, Gabriele; Vanem, Erik; Glad, Ingrid K.

doi:10.1109/icphm.2016.7811910

Cited by 13 publications

(8 citation statements)

References 7 publications

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“…However, in this study, a somewhat different approach is investigated, based on signal reconstruction and residual analysis. This resemble more the anomaly detection approaches based on AAKR or DLM as reported in [5,4].…”

Section: ) Anomaly Detection Using Self-organazing Mapssupporting

confidence: 75%

“…One must then either define a threshold for when a residual is construed as large, or one could apply a sequential test such as the sequential probability ratio test (SPRT) as outlined in e.g. [5,4]. In this study, however, a simple threshold approach is taken, and a possible anomaly is flagged whenever the absolute value of the residual is larger than a predetermined threshold.…”

Section: ) Anomaly Detection Using Self-organazing Mapsmentioning

confidence: 99%

“…Previously, different approaches for anomaly detection have been applied to the same dataset, i.e. the use of dynamical linear models (DLM) and sequential testing [4] and the use of auto associative kernel regression (AAKR) [5]. Both these approaches are based on fitting a model to normal data and predict or reconstruct new sensor data and then comparing to the predicted or reconstructed signals.…”

Section: Introductionmentioning

confidence: 99%

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Cluster-Based Anomaly Detection in Condition Monitoring of a Marine Engine System

Vanem

Brandsater

2018

2018 Prognostics and System Health Management Conference (PHM-Chongqing)

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Sensor data can be used to detect changes in the performance of a system in near real-time which may be indicative of a system fault. However, there is a need for efficient and robust algorithms to detect such changes in the data streams. In this paper, sensor data from a marine diesel engine on an ocean-going ship are used for anomaly detection. The focus is on cluster-based methods for anomaly detection. The idea is to identify clusters in sensor data in normal operating conditions and to assess whether new observations belong in any of these clusters. New observations that obviously do not belong to any of the identified clusters, may be regarded as anomalies and call for further scrutiny. The cluster-based approaches to anomaly detection presented in this paper are truly unsupervised, and they are applied to sensor data with no known faults. Being fully unsupervised, however, the cluster based approaches do not need to explicitly assume that all observations in the training data are fault-free as long as the amount of faulty data is not large enough to form a separate cluster. Moreover, anomalies in the training data may be detected. Various clustering techniques are applied in this paper to provide, a simple and unsupervised approach to anomaly detection. This could then be used as an efficient initial screening of the data streams before more detailed analysis is applied to suspicious parts of the data. The methods explored in this study include K-means clustering, Mixture of Gaussian models, density based clustering, self-organizing maps and spectral clustering. The performance of the various methods is reported, and also compared to other methods recently proposed for anomaly detection such as auto associative kernel regression (AAKR) and dynamical linear models (DLM). Overall, cluster-based methods are found to be promising candidates for online anomaly detection based on sensor data.

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Section: ) Anomaly Detection Using Self-organazing Mapssupporting

confidence: 75%

Section: ) Anomaly Detection Using Self-organazing Mapsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cluster-Based Anomaly Detection in Condition Monitoring of a Marine Engine System

Vanem

Brandsater

2018

2018 Prognostics and System Health Management Conference (PHM-Chongqing)

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“…The ML algorithm will then provide a higher reconstruction error on unforeseen trends in faulty degradation data. Brandsaeter et al [12]used Auto Associative Kernel Regression (AAKR) for reconstruction and the Sequential Probability Ratio Test for anomaly detection provided. In order to determine the fault condition, a lower bound and upper bound threshold value was used.…”

Section: Related Workmentioning

confidence: 99%

An Unsupervised Reconstruction-Based Fault Detection Algorithm for Maritime Components

et al. 2019

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In recent years, the reliability and safety requirements of ship systems have increased drastically. This has prompted a paradigm shift toward the development of prognostics and health management (PHM) approaches for these systems' critical maritime components. In light of harsh environmental conditions with varying operational loads, and a lack of fault labels in the maritime industry generally, any PHM solution for maritime components should include independent and intelligent fault detection algorithms that can report faults automatically. In this paper, we propose an unsupervised reconstruction-based fault detection algorithm for maritime components. The advantages of the proposed algorithm are verified on five different data sets of real operational run-to-failure data provided by a highly regarded industrial company. Each data set is subject to a fault at an unknown time step. In addition, different magnitudes of random white Gaussian noise are applied to each data set in order to create several real-life situations. The results suggest that the algorithm is highly suitable to be included as part of a pure data-driven diagnostics approach in future end-to-end PHM system solutions. INDEX TERMS Automatic fault detection, deep learning, maritime industry, prognostics and health management, unsupervised learning.

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“…Consequently, more advanced asset health management systems for hybrid vessel are required that take into account the dynamics that exist in these complex systems. Current literature on intelligent asset health management tends to focus on prognostics of single-type assets [22][23][24][25] or using simulated data to test the reliability of health management models [26,27]. A gap exists in the literature for the analysis of the complex dynamic relationships between multiple assets that simultaneously respond to the needs of vessels in real-world environments.…”

Section: Introductionmentioning

confidence: 99%

Prognostics and Health Management for the Optimization of Marine Hybrid Energy Systems

et al. 2020

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Decarbonization of marine transport is a key global issue, with the carbon emissions of international shipping projected to increase 23% to 1090 million tonnes by 2035 in comparison to 2015 levels. Optimization of the energy system (especially propulsion system) in these vessels is a complex multi-objective challenge involving economical maintenance, environmental metrics, and energy demand requirements. In this paper, data from instrumented vessels on the River Thames in London, which includes environmental emissions, power demands, journey patterns, and variance in operational patterns from the captain(s) and loading (passenger numbers), is integrated and analyzed through automatic, multi-objective global optimization to create an optimal hybrid propulsion configuration for a hybrid vessel. We propose and analyze a number of computational techniques, both for monitoring and remaining useful lifetime (RUL) estimation of individual energy assets, as well as modeling and optimization of energy use scenarios of a hybrid-powered vessel. Our multi-objective optimization relates to emissions, asset health, and power performance. We show that, irrespective of the battery packs used, our Relevance Vector Machine (RVM) algorithm is able to achieve over 92% accuracy in remaining useful life (RUL) predictions. A k-nearest neighbors algorithm (KNN) is proposed for prognostics of state of charge (SOC) of back-up lead-acid batteries. The classifier achieved an average of 95.5% accuracy in a three-fold cross validation. Utilizing operational data from the vessel, optimal autonomous propulsion strategies are modeled combining the use of battery and diesel engines. The experiment results show that 70% to 80% of fuel saving can be achieved when the diesel engine is operated up to 350 kW. Our methodology has demonstrated the feasibility of combination of artificial intelligence (AI) methods and real world data in decarbonization and optimization of green technologies for maritime propulsion.

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An application of sensor-based anomaly detection in the maritime industry

Cited by 13 publications

References 7 publications

Cluster-Based Anomaly Detection in Condition Monitoring of a Marine Engine System

Cluster-Based Anomaly Detection in Condition Monitoring of a Marine Engine System

An Unsupervised Reconstruction-Based Fault Detection Algorithm for Maritime Components

Prognostics and Health Management for the Optimization of Marine Hybrid Energy Systems

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