Achieving predictive maintenance and improved production efficiency through the use of real-time data in the energy industry

Harri Lääveri
data district heat predictive maintenance

Data is at the core of achieving predictive maintenance and efficiency. In our previous blogs, we have discussed the role of data in improving emission efficiency. The same data can also be used in other ways. One such use is maintenance. In the same way that data can be used to monitor and predict factors directly related to production, it can be used to anticipate and plan maintenance measures.

Since the performance of processes and machines plays such a significant role in production efficiency, it is important to consider preventive maintenance as an integral part of production efficiency.


The current situation

Maintenance plans are drawn up in many production facilities largely around the calendar. Predictive maintenance involves replacing wearing parts well in advance of the manufacturer's recommended replacement time. Maintenance outages are carefully planned in advance and all precautionary measures are taken within that time window. In addition, through on-call shifts of experienced maintenance workers, potentially hazardous situations can be avoided. 

Previously this method has been justified, as many institutions simply haven’t had any better options. 


What are the shortcomings of the current approach?

The main limitation of this approach is that it does not take into account the decline in the efficiency of the machines or abnormal production conditions that may be caused by events such as pressure differences in the pumps in the district heating network. What if these events go unnoticed?

The solution lies in the data and its utilisation. Depending on the company and the production facility, there may already be control room systems in place that collect data on production performance. In our other blogs, we have discussed how that data should be further processed and used to monitor and optimise emissions and capacities throughout the district heating network.

The same data can also be utilised for maintenance.


How can data be utilised?

From the moment when data begins to accumulate in power plants, it can be utilised in maintenance (although historical data is still necessary for reliable forecasting). When enough data accumulates - and this may already be gathered over a relatively short six-month period - we can begin to analyse it. For example, we can analyse the condition of the district heating network pumps based on small changes in pressure in the pumps.

When machine learning and other data gathered from the district heating network are added to the equation, maintenance outages can be strategically scheduled. This also allows for production to be compensated during an outage, for example by increasing the capacity of other plants or by purchasing additional power from abroad. 

The data indicates actions to be taken, which can be turned into work orders in the calendars of maintenance personnel. 


Data utilisation throughout the organisation

It is important to monitor and collect production data from the distribution network, production units, processes and individual equipment. With the help of the Internet of Things (IoT), it is possible to do this very comprehensively nowadays. 

Although the main purpose of data collection would be to improve production and emission efficiency, the same data can also be used for many other objectives. One notable example of this is predictive maintenance.

The widespread use of data in the energy industry is not only a way to meet today’s challenges, but also a way to increase the efficiency of one’s own business.

Harri Lääveri

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