In the maintenance of installations and machines, Industry 4.0 opens doors, allowing data to speak and supporting maintenance engineers in making maintenance decisions.

They must guarantee maximum uptimes of installations and machines, preferably also reduce costs, and at the same time, make sure that maintenance is carried out safely. It is therefore high time that we encourage the digital transformation of machines in order to meet these challenges. With the help of predictive analytics and IoT, among other things, we can predict the ideal time to perform maintenance, taking anticipated events into account. You can take a look into the future and decide what it will be yourself.

Visually inspecting machines is the most primitive way of working. One step further is the manual performance of measurements at a specific point in time. A more advanced form is real-time condition monitoring, measuring and storing various parameters such as the oil level, temperature and vibration. These parameters can be a possible indication of things going wrong. If measurements diverge from the expected results, it's time for maintenance. In this way, maintenance is already much more aligned with actual requirements. This is already coming close to future-oriented, predictive maintenance.

Are you ready for predictive maintenance? Test it here.

Its strength lies in the provision of a permanent ear, allowing you to hear what your installations, machines and devices have to tell you. We distill this information through analysis of the data you are already capturing in various places. Through new concepts such as Cloud, Big Data, IoT, AI and the increased opportunities connectivity creates, we can now efficiently and effectively combine the data present in different places, on different systems and from different sensors.

Predictive maintenance helps to predict machine failures and malfunctions based on:

Data

Today, sensor data such as measurements of vibrations, pressure, flow, temperature, etc. can be transferred to an internet platform for analysis by means of the Internet Of Things (IoT). It is also possible to bring together different data sources, such as the  history of the machine's operations, maintenance history, production data (production quality, product failures, production downtime, etc.) and environmental data such as temperature, humidity, air quality, etc. Suddenly, many more parameters can be involved in the comparison with the failure data, revealing new insights and connections.

Predictive Maintenance Model

Based on machine learning algorithms, data scientists arrive at a predictive maintenance model that actually predicts when asset failures occur, based on trends of one or more parameters. As more data is kept, the model can also be further adjusted.

Collaboration with Maintenance Engineers

An important role is reserved for maintenance engineers and reliability engineers who, based on their many years of experience, help determine the asset selection, provide information about available data, and are involved in the creation of the predictive maintenance model from the start of a predictive maintenance project.

Increase of asset availability

Your maintenance strategy supports your production process to help maximize production. Using the PdM model, you can avoid downtime by timely intervention in your maintenance plans, proposing the necessary machine modifications and carrying out appropriate maintenance. Not only is your production staff happier, your end customers also appreciate the timely delivery of their products. Ultimately, your contribution to achieving company objectives increases.

Reduction of maintenance costs

Insight into future machine malfunctions and failures helps you improve the organization of your maintenance. This can mean less unnecessary maintenance, for immediate savings in costs. In addition, the possible negative impact of maintenance work is reduced. Great gains in efficiency are certainly available when you can better plan maintenance work. For scheduled work you can consider the optimum planning of qualified engineers and the availability of both materials and any required downtime for the asset.  You avoid overtime and the unavailability of materials, which can save you a lot of money.

Generating service revenue

For you as the OEM of a device or machine, PdM opens up a whole new world where you can do more than just deliver a product to your customer. You can offer your customers the guaranteed use of your products as a service. For example, you guarantee the running hours of the engine you supply, monitor the operation of the engine via sensors and offer maintenance services via a collaborative platform with your customers and suppliers. This allows you to generate additional revenue streams, because your customers get much better asset availability and service.

Inetum has the right skills and technical expertise to guide you through a PdM pilot project. A characteristic of predictive maintenance are the complex correlations between different technical domains and areas of expertise. Thanks to our years of experience in maintenance with our Rimses maintenance software solution, we are very familiar with how a maintenance department operates. All our expertise with regard to maintenance and safety combined with our highly valued experience as integrator and ICT partner make Inetum the right partner for predictive maintenance.

Our approach for a pilot project, where Inetum possesses the necessary expertise in each of these areas:

• Asset selection: Which assets are critical to your production process or guarantee the proper functioning of your installation or machine? Improvements in the maintenance of these assets will provide you with the most value.
   
• Data identification: Qualitative data is very important. We investigate the availability (historical and current), relevance, reliability and quality of your existing data.
   
• Data exploration: Our data scientists use the data to work with various analysis tools and machine learning to convert the data into a Predictive Maintenance Model. The model uses algorithms based on the trends and history of one or more parameters to indicate when asset failures are imminent.

• Data monitoring: The interaction with the maintenance staff is crucial in testing the model against reality. Dashboards and reporting are important in communication with engineers. As more data is kept, the model can also be further adjusted.
   
• Predict & prescribe: The model helps to improve maintenance. The output of the model can be integrated in the CMMS to control predictive actions. In the final phase, additional artificial intelligence can be employed to take the step to prescriptive maintenance. This involves the system using the CMMS to make proposals with regard to desired actions.