# A Machine Learning Framework for Condition-Based Maintenance of Marine Diesel Engines: A Case Study
### Leveraging Data to Predict Maintenance  
In this context, a new kind of maintenance has been introduced, predictive maintenance (PdM) [30; 31; 32], which is perceived as a groundbreaking approach for maritime operations, in which RM and PvM are no longer sufficient [25; 28]. In this case, the aim is to predict failures by using sensors that are installed onboard. Some authors prefer using the prognostic and health management (PHM) definition [23]. The medical term \"prognostic\" highlights that it is not only necessary to provide information on the health status of machines but also to predict incoming failures. Some authors also call PdM condition-based maintenance (CBM) [32; 33]. In shipping, one of the main problems of CBM is to guarantee continuous data storage and communication between the ship and onshore operators. To address this issue, a Hierarchical Data Format version 5 was proposed to store a large amount of data [34]. Herein, the authors will use the acronym PdM to refer to both PHM and CBM. The advantage of the PdM approach is to give information about when components and machines actually need maintenance. In this way, the audit timetable is not fixed in advance but depends on the current, real machinery health status. This approach reduces the idle time and wasted money for unnecessary inspection. Moreover, an intelligent and continuous parameter monitoring allows us to decrease sudden failures, thereby increasing the safety for ships, transported goods, and passengers. To be more specific, PdM can be performed in two different ways, depending on how data are collected:  
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2. Offline monitoring: Data are acquired when the machinery is not in service or at regular time intervals using measurement systems that are not integrated with the equipment. Examples are vibration analysis [35], oil analysis [36], and ultrasound monitoring [37]. Many times, offline predictive maintenance demands industry professionals to perform data collection manually. In [35], a comprehensive analysis of the importance of vibration analysis for diagnostic purposes of an internal combustion engine is outlined. In [36], the authors find the best oil analysis interval scheme for a marine engine to investigate the degradation trends from off-line oil samples. In [37], once the engine is not in service, wave spectrum analysis through ultrasonic signals is used as a condition-based technique to analyze the health status of the engine. The main advantage of this technique is to provide very precise and helpful information on the engine's health status; nevertheless, the engine needs to be out of service, thus limiting the applicability of the approach, because failures can happen during working hours, e.g., when ships are sailing. 3. Online monitoring: Data are collected during machinery operations [38]. Companies of the Industry 4.0 movement tend to combine IoT sensors installed in the machines with artificial intelligence, and collect data automatically, 24 h a day, every day. Examples are given in [39; 40], where the main approach consists in training the predictive algorithm on previously acquired data, and then, applying it during the operation of the monitored device. The main advantage of this method is that it provides information on the engine's health status continuously. In this way, it is easier to catch failures earlier. However, issues related to interruption of data acquisition and noise may have to be addressed. It is important to be aware of the differences between the two methods and to use valid reasoning to select the best for monitoring industrial assets. A well-motivated decision determines good asset maintenance and has an impact on costs and profitability. In offline monitoring, the collection of data requires a significant amount of time and expert technicians as well as a measurement plan, and sometimes assets are difficult to access, posing possible risks for the technicians. The method is simple but requires high expertise and care.