Demystifying Predictive maintenance.

Operating costs for merchant ships can vary depending on various factors such as the size of the vessel, its age, the type of cargo it carries, and the routes it operates. However, you may see in the image below a general breakdown of the typical costs involved. From the given data we can conclude that the 3 major operating expenses are Crewing (30% to 50%), Fuel cost (20% - 40%), and Maintenance (10% - 30%). Once we have the data the next thing to do is to optimize and place control measures. The shipping industry also like any other industry has shifted its focus from conventional Preventive maintenance to Predictive maintenance. In this blog, we’ll explore to understand what Predictive maintenance is and how it is advantageous over traditional preventive maintenance.

What is Predictive Maintenance?

Predictive maintenance started gaining popularity in the late 1990s and early 2000s. Today, predictive maintenance is widely adopted across various industries where the performance and reliability of equipment are critical for operations.

Predictive maintenance is a proactive maintenance strategy that uses data analysis and machine learning algorithms to predict equipment failures and schedule maintenance activities before a breakdown occurs. It involves monitoring the condition and performance of equipment in real-time, collecting data on factors such as temperature, vibration, and energy consumption also including historical maintenance records. The collected data is then processed and analyzed to identify patterns, anomalies, and potential failure indicators. Machine learning algorithms are trained using historical data to predict equipment failures and estimate remaining useful life. Predictive models are developed to generate alerts and notifications when the equipment's condition deviates from normal operating parameters. Maintenance activities are scheduled based on the predictions and recommendations provided by the predictive maintenance system. Finally, Continuous monitoring and feedback loops are established to refine and improve the accuracy of the predictive models over time.

Predictive maintenance techniques.

1) Vibration monitoring is a predictive maintenance technique that involves using sensors to measure the vibration levels of equipment, helping to detect abnormalities and potential failures. 

2) Thermography is another technique used, where infrared cameras are used to detect temperature variations in equipment. This can help identify overheating components or electrical issues. 

3) Tribology is the study of friction, wear, and lubrication, and it plays a crucial role in predictive maintenance. By analyzing lubricant properties and conducting oil analysis, potential equipment failures can be identified and prevented. 

4) Ultrasonics is a technique that uses high-frequency sound waves to detect anomalies in equipment. It can be used to identify leaks, blockages, or other issues that may not be visible to the naked eye. 

5) Visual inspections are a simple yet effective technique where maintenance personnel visually inspect equipment for signs of wear, damage, or abnormalities. This can help identify issues that may require maintenance or repair. 

Advantages of predictive maintenance.

1) Predictive maintenance helps in reducing unplanned downtime by identifying potential equipment failures in advance. 

2) It allows for more efficient and targeted maintenance activities, as maintenance is performed based on the actual condition and performance of the equipment. 

3) By optimizing maintenance schedules, predictive maintenance helps minimize maintenance costs and maximize the lifespan of equipment. 

4) It enables proactive planning and execution of maintenance activities, leading to improved operational efficiency and productivity. 

5) It allows for better resource allocation, as maintenance activities are scheduled based on the predictions and recommendations provided by the predictive maintenance system. 

6) By leveraging data analysis and machine learning algorithms, predictive maintenance can continuously learn and improve its accuracy over time.

What are the disadvantages of predictive maintenance?

1) One disadvantage of predictive maintenance is the initial cost of implementing the necessary sensors, data collection systems, and predictive analytics software. 

2) Another challenge is the need for a large amount of historical data for accurate predictions, which may not be available for older equipment or in industries with limited data collection practices. 

3) Predictive maintenance also requires skilled personnel who can analyze the data and interpret the results, which may require additional training or the hiring of specialized staff. 

4) False alarms and false positives can be a drawback of predictive maintenance, leading to unnecessary maintenance actions or disruptions in operations. 

5) There can be limitations in the accuracy of predictive models, as they are based on historical data and assumptions, and may not account for unforeseen circumstances or changes in operating conditions. 

is predictive maintenance costly?

Predictive maintenance can help reduce overall maintenance costs by minimizing unplanned downtime and optimizing maintenance schedules. While there may be initial costs associated with implementing predictive maintenance systems, the long-term benefits can outweigh these costs. The cost-effectiveness of predictive maintenance depends on factors such as the complexity of the equipment, the availability of data, and the accuracy of the predictive models used. 

Overall, predictive maintenance can be a cost-saving strategy in the long run, as it allows for proactive planning and targeted maintenance activities based on the actual condition of the equipment.

Implementation of predictive maintenance requires a combination of data collection sensors, data analytics tools, and domain expertise to interpret the results and make informed maintenance decisions. It is important to have skilled personnel who can interpret the results of predictive analytics and make informed decisions regarding maintenance actions. Seafarers forming a crucial part in the maintenance of a vessel whether onboard or providing support from ashore must upskill themselves to be able to contribute and make shipping sustainable and stay ahead of their career graph.