Steps to implement predictive maintenance systems
Predictive maintenance systems have transformed preventive and corrective tasks for industrial equipment. Working with predictions of what is highly likely to happen to equipment gives maintenance activities a high level of effectiveness, which in turn impacts the rest of the supply chain. For example, based on objective data, it is possible to determine the optimal times to carry out scheduled shutdowns or to anticipate potential failures or breakdowns in machinery.
In previous posts that you can find on our blog, we have already discussed the characteristics of predictive maintenance and its countless benefits. To briefly recap, we simply note here that advances in Artificial Intelligence, Big Data, and the Internet of Things (IoT – IIoT) have enabled the development of predictive systems that, when applied to the business world, make it possible to design strategies based on real and objective information—both present and future—moving away from earlier models based on operators’ intuition and experience. And what does this mean? A significant improvement in the design of business strategies, which now have high success rates because they are built on ‘knowledge of the future’.
In the specific field of maintenance, the rapid development of new technologies and work based on forecasts provide the following advantages (if you would like to explore this further, you can read the post “10 benefits of predictive maintenance”):
- Real-time equipment monitoring
- Optimization of parts and spares
- Early anomaly detection
- Increased productivity
- Process optimization
- Improved planning
- Simplified decision-making
- Freeing up staff for other tasks
- Increased productivity
- Cost savings
Having summarised its main characteristics, we will now look at the steps required to implement predictive maintenance systems in the company, the topic of this post. To do so, we will focus on the processes we follow, and therefore those we know best and consider optimal for integrating predictive maintenance into business operations.
Requirements analysis
In an initial workshop, we define the client’s requirements and needs, the characteristics of the equipment to be monitored, and the data required to assess wear and anomalies.
Data acquisition
The next step is to implement the tools required to acquire data from industrial equipment. If necessary, we also add additional sensors that can measure unmonitored data such as movement, temperature, acceleration, consumption, and position. In our case, we aim to make the data collection process as non-intrusive as possible by connecting directly to the industrial hardware.
Data analytics
Once the data has been extracted, we develop relevant indicators using advanced analytics algorithms. These developments allow us to identify failures associated with wear, deviations in operation, and variations in energy consumption by working with incident histories and establishing a benchmark of standardised values that makes it possible to anticipate a potential production stoppage.
Dashboard development
Using the analytical indicators, we develop dashboards that make it possible to check equipment performance and generate alerts when there are deviations from the values established as optimal. In this way, management and decision-making are simplified through the design of simple, intuitive visual environments that present information clearly and directly.
Pilot development
To continue moving forward, a small number of industrial assets are selected to implement a pilot test that speeds up the go-live process and fine-tunes the analytical algorithms.
Go-live
Once the system is fully aligned and the pilot performs as expected, the system is rolled out into full production.