Predictive Maintenance: Efficiency in industry 4.0

Three important steps for using Predictive Maintenance

If you want to rely on the principle of Predictive Maintenance in your own company, you must bear in mind that three important steps lead to effective and therefore long-term superior Predictive Maintenance:

• The collection, digitization and transmission of all relevant data

• The storage, analysis and evaluation of the collected data sets

• Calculation of probabilities for defined critical events

The first step is to create a database and to install the corresponding sensors on and in the machines. Since many manufacturers are now active in the field of industry 4.0 and its standards, such sensors are already integrated in many machines and systems. In the course of this, however, all relevant data must also be collected by the experts. Who only measures the naked data of the machine, but ignores for example values such as room temperature and humidity, the data often cannot be put into a meaningful context. Predictive Maintenance therefore always involves collecting as much data as possible.

In the second step, the data must be combined in a database and put in relation to each other. Many providers on the market offer practical solutions, which can be operated either in the data center of the provider or directly in the company itself. This is where the quality of the data and its possible applications are decided. Only through targeted and structured storage and fast access to the immense data sets, these can be analyzed by the intelligent algorithms.

The last step is crucial for Predictive Maintenance. In this step, default probabilities are calculated for all relevant components on the basis of the data collected. The better the data, the more accurate the probabilities can be calculated. Predictive Maintenance is based on these probabilities and predictions. Before an event X occurs, component Y can be replaced using Predictive Maintenance to prevent event X.

Machine Learning

Machine Learning - Definition and application examples

Overview of the advantages of Predictive Maintenance

Predictive Maintenance offers considerable advantages over other methods of maintenance: On the one hand, unplanned and thus often very expensive machine downtimes can be prevented. Thanks to comprehensive knowledge about the condition of the machines and their individual components, maintenance can be planned exactly as required, which noticeably and significantly increases the productivity of the machines. In addition, the field service assignments of employees can be planned better and thus made more efficient, as maintenance can be carried out specifically and at an intelligent interval. Spare parts management can also be considerably simplified, as the required spare parts are already known in advance and can be kept in stock accordingly. In addition, the continuous analysis of the machines and the read-out data offers the possibility of successively increasing the efficiency of the individual machines and thus optimising capacity utilisation. This allows the machines to amortize their investment costs more quickly and thus contribute to the economic success of the company. Overall, the benefits of Predictive Maintenance are enormous, even if the effort initially seems inconvenient for many users. However, once the system has established itself and the data is not only collected but also evaluated in a targeted manner, Predictive Maintenance can achieve enormous leaps in performance for many machines and systems. Predictive Maintenance is therefore once again a clear advantage for smart production, which is to adapt its performance to current demand and requirements. After all, the companies' flexibility is increasing enormously.

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