IoT, Industry 4.0 and Predictive Maintenance

Predictive Maintenance - How it works in practice

If you want to use Predictive Maintenance effectively and in the long term in your company, you should follow three steps:

• the collection, digitalization and transmission of data

• the storage, analysis and evaluation of the collected data

• the calculation of probabilities of occurrence for certain events

Predictive Maintenance categorizes the condition of assets and continuously checks them online. A large part of the inspections can be carried out parallel to the operation of the plant. Thus, interruptions of the regular system operation can be kept to a minimum. When assessing the actual condition of a machine, sensor technology checks using infrared, acoustics (partial discharge and ultrasound), corona detection, vibration analysis and sound level measurements are used. The device is not impaired or damaged in its functionality by such test procedures. In a next step, the knowledge gained from these tests will be linked to process performance data, which is especially available in collaborative process automation systems (CPAS).

Predictive Maintenance, Big Data and Machine Learning

Predictive maintenance thrives on data and a high degree of automation. In essence, it is therefore a matter of continuously monitoring the condition of machines and optimizing operation and maintenance intensity on this basis. For this to succeed, the collected measurement data must be interpreted automatically. Machine learning algorithms play a decisive role in ensuring a reliable diagnosis of the condition of the monitored system and the most reliable possible prediction of its remaining useful life, the Remaining Useful Life (RUL).

In order to be able to make reliable statements about the condition of machines and systems, large amounts of data are collected and then stored, processed and analyzed using intelligent algorithms. The data includes not only the condition of the machines and systems themselves, but also the environment: parameters such as temperature or humidity play an important role. Due to the many different data and formats and the large volumes of data, high-capacity databases must be provided for predictive maintenance in practice. An effort that is indispensable: The size of the database and the intelligence and performance of the analysis algorithm are essential for the quality of the knowledge gained. After acquisition, measured values and diagnostic data are transmitted via networks to service centers or to the manufacturers themselves. The collected data volumes are constantly updated and processed - so trends and developments can be read from possible changes.

Predictive Maintenance Advantages and examples

The correct and effective use of Predictive Maintenance offers immense advantages for manufacturers and users:

• Improvement of profitability by reducing downtimes

• Reduction of costs for unplanned downtime

• Increasing the service life of machines and systems through effective maintenance

• Improvement of machine performance and higher productivity through the permanent analysis of the collected data

Predictive maintenance is therefore a must, not an option: it is already being used in many areas. Predictive maintenance is interesting for many industries: for the entire manufacturing industry, for all mobility sectors such as aviation, automotive or rail transport, or for the energy sector, for example.

Conclusion: Predictive maintenance - indispensable key technology in IIoT

The principle of predictive maintenance and repair using future-oriented IoT technologies is one of the key innovations of Industry 4.0 - and a must for all machine-intensive industries. Particularly in complex systems, it allows expensive machine failures to be avoided and maintenance costs to be reduced at the same time. The basis for this is a well thought-out architecture and the intelligent use of machine learning algorithms: Both components make it possible to specifically determine failure probabilities and generate reliable predictions. Companies that want to increase their added value with predictive maintenance therefore cannot avoid dealing with artificial intelligence - effective predictive maintenance is not possible without machine learning.