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.
Sea state estimation is a fundamental problem in the development of autonomous ships. Traditional methods such as wave buoy, satellites, and wave radars are limited by locations, clouds and costs, respectively. Model-based methods are prone to incorrect estimations due to their high dependency on mathematical models of ships. As previous data-driven studies for sea state estimation only consider wave height and use the motion data from dynamic positioning vessels, this paper introduces a new, deep neural network (SSENET) to estimate sea state in light of both wave height and wave direction, and extends the generality of sensor data from ship motion with forward speed. SSENET is built on the basis of stacked convolutional neural network blocks with dense connections between different blocks, channel attention modules and a feature attention module. The dense connections build shortcut paths between input and all subsequent convolutional blocks, which can make full use of all the hierarchical features from the original time series sensor data. The channel attention modules aim to enhance the features extracted by each convolution block. The feature attention module focuses on combining the feature fusion of hierarchical features in an adaptive manner. Benchmark experiments show the competitive performance against state-ofthe-art approaches. Applying the SSENET on two datasets of zigzag motion for comparative studies shows the effectiveness of the proposed method.
The maritime industry widely expects to have autonomous and semi-autonomous ships (autoships) in the near future. In order to operate and maintain complex and integrated systems in a safe, efficient and cost-beneficial manner, autoships will require intelligent Prognostics and Health Management (PHM) systems. Deep learning (DL) is a potential area for this development, as it is rapidly finding applications in a variety of domains, including self-driving cars, smartphones, vision systems, and more recently in PHM applications. This paper introduces and reviews four well-established DL techniques recently applied to various practical PHM problems. The purpose is to support creativity and provide inspiration towards PHM based on DL (PHMDL) in autoships and the maritime industry. This paper discusses benefits, challenges, suggestions, existing problems, and future research opportunities with respect to this significant new technology. Index Terms-Autonomous ships, deep learning, maritime industry, prognostics and health management.
Today, most research studies that aim to predict the remaining useful life (RUL) of industrial components based on deep learning techniques are using piecewise linear (PwL) run-to-failure targets to model the degradation process. However, this PwL degradation model assumes a constant initial RUL value in which only time is needed to model normal operating conditions. Thus, it ignores the entire diagnostics aspect. To provide high and reliable RUL prediction accuracy, a prognostics algorithm must incorporate diagnostics information. This paper will provide the Prognostics and Health Management Community an empirical study that validates the PwL degradation model against other, more recent data-driven labeling approaches. We compare three different data-driven labeling approaches for RUL predictions. First, an unsupervised reconstruction-based fault detection algorithm is used to provide valuable diagnostics information. Then, optimized initial RUL values are calculated based on this information. Finally, these values are used to construct PwL, descriptive statistics, and anomaly score function run-to-failure targets for subset FD001 in the popular and publicly available C-MAPSS data set. A deep network structure is proposed and trained on the three different run-to-failure targets in order to predict the RUL. During the training process, a genetic algorithm approach is used to tune a selected search space of hyper-parameters. The results suggest that the network trained on PwL run-to-failure targets with the optimized initial RUL values performs the best and provides the most reliable RUL prediction accuracy. This network also outperforms the most robust results in the literature. INDEX TERMS Data-driven labeling approaches, deep learning, fault detection, prognostics and health management, remaining useful life.
Enthusiasm for ship autonomy is flourishing in the maritime industry. In this context, data-driven prognostics and health management (PHM) systems have emerged as the optimal way to improve operational reliability and system safety. However, further research is needed to enhance the essential actions relating to such a system. Fault detection is the first and most crucial action of any data-driven PHM system. In this article, we propose a fault-type independent spectral anomaly detection algorithm for marine diesel engine degradation in autonomous ferries. The benefits of the algorithm are verified on three fault types where the nature of degradation differs. Both normal operation data and faulty degradation data have been collected from a marine diesel engine using two different engine load profiles. These profiles aim to replicate real autonomous ferry crossing operations, environmental conditions that the ferry may encounter. First, the data are subjected to a feature selection process to remove irrelevant and redundant features. Then, a multiregime normalization method is performed on the data to merge the engine loads into one context. Finally, a variational autoencoder is trained to estimate velocity and acceleration calculations of the anomaly score. Generic and dynamic threshold limits are simultaneously established to detect the fault time step online. The algorithm achieved an accuracy of 97.66% in the final test when the acceleration was used as the fault detector. The results suggest that the algorithm is independent of fault types with different nature of degradation related to the marine diesel engine.
The maritime industry generally anticipates having semi-autonomous ferries in commercial use on the west coast of Norway by the end of this decade. In order to schedule maintenance operations of critical components in a secure and costeffective manner, a reliable prognostics and health management system is essential during autonomous operations. Any remaining useful life prediction obtained from such system should depend on an automatic fault detection algorithm. In this study, an unsupervised reconstruction-based fault detection algorithm is used to predict faults automatically in a simulated autonomous ferry crossing operation. The benefits of the algorithm are confirmed on data sets of real-operational data from a marine diesel engine collected from a hybrid power lab. During the ferry crossing operation, the engine is subjected to drastic changes in operational loads. This increases the difficulty of the algorithm to detect faults with high accuracy. Thus, to support the algorithm, three different feature selection processes on the input data is compared. The results suggest that the algorithm achieves the highest prediction accuracy when the input data is subjected to feature selection based on sensitivity analysis.
Maintenance is the key to ensuring the safe and efficient operation of marine vessels. Currently, reactive maintenance and preventive maintenance are two main approaches used onboard. These approaches are either cost-intensive or labor-intensive. Recently, Prognostics and Health Management has emerged as an optimal way to manage maintenance operations. In such a system, fault prognostics aims to predict the remaining useful life based on the sensor measurements. In this paper, the feasibility of applying data-driven fault prognostics to marine diesel engines is investigated. Real-world run-to-failure data of two independent fault-types in two different engine load profiles are collected from a hybrid power lab. The first profile is used for training and validation, while the second is used for testing. The LSTM networks are used to construct the fault prognostics model. Experiments and comparisons are performed to obtain the optimal structure of the networks. Results show that the proposed method generalizes well on the second profile and provides remaining useful life predictions with high accuracy.
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