The compressors used in today’s natural gas production industry have an essential role in maintaining the production line operational. Each of the compressors’ components has routine maintenance tasks to avoid sudden failures. Hence, the significant advantages and benefits of performing preventative maintenance tasks in time are decreasing equipment downtime, saving additional costs, and improving the safety and reliability of the whole system. In this paper, anomaly classification and detection methods based on a neural network hybrid model named Long Short-Term Memory (LSTM)-Autoencoder (AE) is proposed to detect anomalies in sequence pattern of audio data, collected by multiple sound sensors deployed at different components of each compressor system for predictive maintenance. In research methodology, this paper has conducted experiments that employed different RNN architectures such as GRU, LSTM, Stacked LSTM, and Stacked GRU with various functions to create a baseline for model evaluation. Each architecture used audio signals dataset received from the compressor system for experiments to consider each neural network model’s performance. According to performance results, an optimal model for anomaly detection with the best performance scores has been proposed in this research. Experiments combined one-dimensional raw audio signal features using SC and Mel spectrogram features were fed to deep learning models to evaluate performance. Hence, such hybrid methods can effectively detect normal and anomaly audio signals collected from a compressor system, increasing the compressor system’s reliability and the sustainability of the gas production line. The combination of multiple-resource features in the proposed hybrid model showed a 100% score in all four-evaluation metrics such as accuracy, precision, recall, and F1 in LSTM-based autoencoder in both test and train results.

Resiliency measurement is a great tool for evaluating system performance and proposing solutions to prevent damage and to recover from disruptive events. This study proposes an analytic approach to quantify narrow waterway systems’ resiliency during disasters. First, metrics are introduced to quantify the resiliency before, during, and after a disruption. The existing Key Performance Indicators (KPIs) for Maritime Transportation Systems (MTS) are examined, and two metrics, 1) the number of inbound and outbound vessels and 2) Total Stopped Vessel-Hours, are selected to measure the resiliency of a waterway system. Second, a heuristic method is developed to derive the KPIs from the Automatic Identification System (AIS) data. Finally, the proposed methodology is performed for the Houston Ship Channel (HSC) AIS data before, during, and after Hurricane Harvey, in August 2017. The results are presented for the entire channel and highlight useful information about the performance of individual docks, terminals, and waterway zones within HSC. This study helps decision-makers identify the weaknesses and potential bottlenecks in a waterway confronting a disruption and come up with remedies.

In maritime logistics, accurately predicting the Estimated Time of Arrival (ETA) of vessels is pivotal for optimizing port operations and the global supply chain. This study proposes a machine learning method for predicting ETA, drawing on historical Automatic Identification System (AIS) data spanning 2018 to 2020. The proposed framework includes a preprocessing module for extracting, transforming, and applying feature engineering to raw AIS data, alongside a modeling module that employs an XGBoost model to accurately estimate vessel travel times. The framework’s efficacy was validated using AIS data from the Port of Houston, and the results indicate that the model can estimate travel times with a Mean Absolute Percentage Error (MAPE) of just 5%. Moreover, the model retains consistent accuracy in a simplified form, pointing towards the potential for reduced complexity and increased generalizability in maritime ETA predictions.

This research introduces an online system for monitoring maritime traffic, aimed at tracking vessels in water routes and predicting their subsequent locations in real time. The proposed framework utilizes an Extract, Transform, and Load (ETL) pipeline to dynamically process AIS data by cleaning, compressing, and enhancing it with additional attributes such as online traffic volume, origin/destination, vessel trips, trip direction, and vessel routing. This processed data, enriched with valuable details, serves as an alternative to raw AIS data stored in a centralized database. For user interactions, a user interface is designed to query the database and provide real-time information on a map-based interface. To deal with false or missing AIS records, two methods, dead reckoning and machine learning techniques, are employed to anticipate the trajectory of the vessel in the next time steps. To evaluate each method, several metrics are used, including R squared, mean absolute error, mean offset, and mean offset from the centerline. The functionality of the proposed system is showcased through a case study conducted in the Gulf Intracoastal Waterway (GIWW). Three years of AIS data are collected and processed as a simulated API to transmit AIS records every five minutes. According to our results, the Seq2Seq model exhibits strong performance (0.99 R squared and an average offset of ~1400 ft). However, the second scenario, dead reckoning, proves comparable to the Seq2Seq model as it involves recalculating vessel headings by comparing each data point with the previous one.

Proactive maintenance improves the operational availability of a system and increases the owner's profit. To create more economic value along this direction, it is popular to outsource maintenance services to a service agent. However, due to various uncertainties in equipment failures and spare part logistics, making a service agreement between the owner and the service agent can be quite complex. In this paper, a game-theoretic cooperative model is presented to facilitate contract negotiation in such a joint decision-making process. In particular, the owner determines the time for preventive replacement and the service agent makes up a plan on ordering a spare part such that their overall profit is maximized. To sign a long-term contract, the two players need to share the overall profit in a fair manner based on Shapley values that satisfy certain axioms of fairness. Numerical examples are provided to illustrate the solution methodology and to highlight the managerial implications in reliability and maintenance management.