What is Predictive Maintenance
– A short introduction for maritime industry
By Michael Paarup
Predictive maintenance is a maintenance strategy where the condition of an asset is analyzed to predict and mitigate an upcoming breakdown.
Predictive maintenance can rely on historical and real time data to analyze and predict anomalies from the baseline behavior. Data can be from built-in sensors, surroundings but also other data sources like maintenance history and spare part usage. In general, the more data the better, but the quality of data is of equal importance.
The purpose of predictive maintenance is to determine the optimum time to perform maintenance to avoid breakdown. This is done to keep the maintenance frequency low while the reliability of machines and equipment remains high.
Advanced statistical methods, such as machine learning, are used to dynamically define when an asset is under normal condition or need to be maintained.
The road to predictive maintenance can be long and requires strategy, people involvement, asset knowledge, data science and continuous monitoring and improvement of the prediction model.
Industrial IoT and how predictions of maintenance work
Industrial IoT and data science are the technical aspects that make predictive maintenance possible. In short, IoT is a group of objects containing sensors, software and other technologies.
These embedded technologies allow IoT solutions to connect and exchange data with other systems over the internet. Such data could be voltage, temperature, vibrations, humidity and the like.
How does Predictive Maintenance differ from Preventive Maintenance?
Some might say that predictive maintenance is an extension of preventive maintenance. Even though both methods strive to prevent the likelihood of breakdowns or failures, the two are not to be confused.
Preventive maintenance is often time, condition or counter triggered. This means that maintenance is planned based on fixed intervals, predefined limits or counter values. Equipment might, however, require maintenance before the scheduled alarm triggers.
At the same time, the maintenance crew might spend time on maintaining the equipment even if it does not need maintenance – with the risk of introducing malfunctions.
Contrary to this, predictive maintenance will only trigger maintenance when data indicates that maintenance is actually needed. The predictive algorithm continuously analyzes the asset data to predict upcoming breakdowns. Based on this analysis, the system is able to schedule a maintenance job before the equipment breaks and avoid expensive downtime.
What are the benefits of Predictive Maintenance?
There are many benefits of predictive maintenance. As stated above, the method can prevent breakdowns or failures from happening. This can result in cost-savings as well as extended lifetime of the equipment.
From a resource perspective, predictive maintenance optimizes productivity in the maintenance team and minimize the stress related to breakdowns as the crew can focus on maintenance of equipment in advance of predicted breakdowns.
In a nutshell, predictive maintenance can be used to:
- Prevent breakdowns
- Expand lifetime of onboard equipment
- Save time in maintenance routines
- Reduce costs from excess maintenance and spares