Predictive maintenance is one of the earliest use-cases of data-driven innovations and the adoption of this principle is still slowly but steadily increasing in many industries. Put simply,  machine learning techniques working on data  support engineers in making fast and accurate decisions when it comes to maintaining installations and machines. Using predictive analytics and ideas from Industry 4.0. among other things, ML2Grow creates smart algorithms that can predict the ideal time for maintenance and can spot anomalous behaviour before damages or scrap occur.

From visual inspections to predictive maintenance

The most simple way of inspecting machines is still visually inspecting their parts for wear or damages. Going one step further is the manual inspection of measurements of sensors at specific times by operators. Indeed many machines now include real-time monitoring where several parameters such as fluid level(s), pressure, temperature and vibrations are measured and captured. If the measurements deviate from what is ‘expected’, intervention or maintenance can be required. This monitoring process is typically visualised in a dashboard. However, continuously observing dashboard measurements is a repetitive task and costly way of working.   This brings us closer to predictive maintenance, the next step in the automation of maintenance. With this solution, we use historical data and smart algorithms to continuously process real-time data streams to gain insights from the sensor data on the health and operating condition of parts, machines and fleets.

A permanent eye

The real game-changer is the fact algorithms don’t sleep, an AI can be seen as digital supervisor which works 24/7 , often processing data at 1-100 millisecond level. This continuous and fine-grained  tool allows you to check your installations, machines and appliances on many different conditions. Further intelligence can be achieved by merging data coming from other sources. In the spirit of IoT and AI adoption in Industry 4.0.,  sensor data from multiple machines is pooled together with e.g. production data and operating parameters. With this increased connectivity, we can then combine the data from different places through various systems and other sensors making solutions such as predictive maintenance, automated parameter optimization and anomaly detection.

To do this key components need to be brought together:

Data

Sensor data such as measurements of vibrations, pressure, flow, the temperature can today be transferred to databases or a data  lake for analysis. It is also possible to bring together different data sources such as the history of machine operation, maintenance history, production data (production quality, product failure, production downtime, …) and environmental data such as temperature, humidity, air quality, etc. Suddenly, many more parameters can be involved compared to the failure data, resulting in new insights and connections.

Predictive maintenance and anomaly detection model

Based on machine learning algorithms, our data scientists develop a predictive maintenance model that predicts when asset failures are announced based on trends in one or more parameters. As more data is kept, the model can also be further improved.

Collaboration with the maintenance engineers

Maintenance engineers still play an essential role both in the development of the techniques and after deployment because of their many years of experience. They are the perfect fit to help determine the asset selection from the start of a predictive maintenance project and  to provide information about the available data and are involved in creating the predictive maintenance model. 

What are the benefits?

Greater availability of your assets

Your maintenance strategy supports your production process to maximise production. Using the predictive maintenance model, you can avoid downtime by intervening in your maintenance plans on time, proposing the necessary machine modifications and carrying out appropriate maintenance. Not only are your production employees satisfied, but your end customers also appreciate that their products are delivered timely in times of shortages.

Reduction of maintenance costs

Insight into future malfunctions and failures of machines helps you better organise maintenance. This can translate into less unnecessary maintenance, which immediately saves you costs. In addition, the chance of a possible negative impact of maintenance is reduced. You will certainly have major efficiency benefits if you can plan the maintenance work better. For scheduled maintenance, you can consider the optimal planning of qualified technicians and the availability of both materials and any required downtime of the asset. Avoiding overtime and the unavailability of materials can save your business a lot of money.

Ensuring quality and service to your customers

As an original equipment manufacturer of a device or machine, predictive maintenance opens up a new world for you, where you can go further than just supplying a product to your customer. You can guarantee the use of your products as a service. For example, the running hours of the motor you supply, monitor the engine’s operation via sensors and offer maintenance service via a shared platform with your stakeholders. This could allow your business to improve its market position as your customers get better asset availability and service.

Are you interested to learn more about predictive maintenance? Get in touch with our team of experts and have a chat!