The paper proposes an innovative solution that employs online machine learning to continuously train and update models using sensor data from ships and ports. The proposed solution enhances the efficiency of ballast water management systems (BWMS), which are automated systems that utilize ultraviolet light and filters to purify and disinfect the ballast water that ships carry for maintaining their stability and balance. The solution allows it to grasp the complex and evolving patterns of ballast water quality and flow rate, as well as the diverse conditions of ships and ports. The solution also offers probabilistic forecasts that consider the uncertainty of future events that could impact the performance of ballast water management systems. An online machine learning architecture is proposed that can accommodate probabilistic based machine learning models and algorithms designed for specific training objectives and strategies. Three training methodologies are introduced: continuous training, scheduled training and threshold-triggered training. The effectiveness and reliability of the solution are demonstrated using actual data from ship and port performances. The results are visualized using time-based line charts and maps.

Reducing unplanned downtime requires monitoring of equipment health. This may not be possible in many cases as traditional health monitoring systems often rely on the use of historical data and maintenance information which is not always available, especially for small and medium-sized enterprises. This paper presents a practical approach that uses sensor data for real-time equipment health indication. The methodology proposed consists of a set of steps. It starts with feature engineering which may include feature extraction to transform raw sensor data into a format more suitable for analysis. Anomaly detection follows next, where various techniques are employed to find any deviations in the engineered features indicating potential equipment deterioration or abrupt failures. Then comes the most important stages equipment health indication and alert generation. These stages provide timely information about the equipment’s condition and any necessary interventions. These steps make it possible for such an approach to be effective even when there is little or no historical data available. The applicability of this approach is validated through a lab-based case study.

Smart manufacturing technologies (Industry 4.0) as solutions to enhance productivity and improve efficiency are a priority to manufacturing industries worldwide. Such solutions have the ability to extract, integrate, analyze and visualize sensor and data from other legacy systems in order to enhance the operational performance. This paper proposes a solution to the challenge of real-time analysis and visualization of sensor and ERP data. Dynamic visualization is achieved using a machine learning approach. The combination of real-time visualization and machine learning allows for early detection and prevention of undesirable situations or outcomes. The prototype system has so far been tested by a smart manufacturing company with promising results.

Analyzing sensor data from a production environment is quite challenging because of the high-dimensional nature of the data. In addition, the generated data is in the form of time-series, where the sequence of registrations may be of utmost significance. One of the main goals of the paper is to determine if the given time-series of feature combinations is normal or rare. This goal could successfully be achieved by combining multiple machine learning models. In this paper, a sliding window based ensemble method is proposed to detect outliers in a streaming fashion. The proposed method uses a combination of clustering algorithms to construct subgroups (clusters) representing different data structures. These structures are later used in a one-class classification algorithm to identfy the outliers. Thus, if a pattern does not belong to any of the common structures or clusters, it is an outlier. Further, based on the rare pattern classification, machine failures could be predicted in advance.

Due to the emergence of the fourth industrial revolution, manufacturing business all over the world is changing dramatically; it needs enhanced efficiency, competency and productivity. More and more manufacturing machines are equipped with sensors and the sensors produce huge volume of data. Most of the companies do neither realize the value of data nor how to capitalize the data. The companies lack techniques and tools to collect, store, process and analyze the data. The objective of this paper is to propose data analytic techniques to analyze manufacturing data. The analytic techniques will provide both descriptive and predictive analysis. In addition, data from the company's ERP system is integrated in the analysis. The proposed techniques will help the companies to improve operational efficiency and achieve competitive benefits.