Condition monitoring and the future of digital business models

Condition monitoring is the permanent or regular monitoring of the condition of a machine by means of sensor-based data. This opens up completely new cooperation possibilities for industrial companies and opportunities for collaborative business models.

By collecting and evaluating data from ongoing machine operation, downtime can be effectively reduced. This process forms the basis for the step from preventive maintenance to maintenance strategies from the field of Predictive Maintenance (or even Prescriptive Maintenance). Maintenance strategies based on condition monitoring not only enable faults to be eliminated, but also their causes to be analyzed. In this way, failures are even predictable and can be prevented by maintenance measures initiated in good time.

How does condition monitoring work?

All condition monitoring systems work with the aid of sensors that record relevant measured variables such as temperature, fill levels, vibrations and more. This provides an early warning when a machine is no longer running "smoothly". If the data supplied exceeds a certain limit, the system triggers an alarm if necessary and informs the user about the respective fault on the machine.

These limits are usually defined in advance by a technician or the development department. However, modern data analysis methods can also do this on the basis of artificially intelligent algorithms. In this case, deep learning methods can be used to AI-supported predictive maintenance solutions to develop tools that identify trends and make a prediction about the time period in which a parameter will leave the regular range and move into the critical range.    

The advantages of a condition monitoring system

Constant monitoring of machine condition results in a number of benefits. The failure of various wear or service parts can be effectively predicted, reducing the number of emergency repair calls. The technician also knows more quickly where the problem lies in the event of a malfunction. Spare and wear parts can be stocked and scheduled for the long term.

It is easier to identify and reduce harmful influences. This also increases the operational reliability of plants. In addition, companies can operate intact components beyond their regular service life and conserve resources. The extended service life thus represents a major advantage for environmental protection and is easy on the maintenance cost budget.

Condition monitoring and innovative business models

The increasing processing of data creates the space for innovative digital business models and Value Added Services in industry. The large volumes of data that are already being generated today and will become even more extensive in the future currently remain unused in many areas. This is also due to a lack of cooperation between individual companies. Data-based business models can help here, but they are associated with hurdles that must first be removed.

Approaches from the field of Collaborative Condition Monitoring describe promising solutions in which a wide variety of players can exchange and sell data and achieve higher reliability and a longer service life for production plants in a network. To achieve this, an economic incentive must be created for the participating players to share data with others on a neutral platform on a cross-competitive basis.  

Effectively use increasing complexity and larger data volumes

Machines and plants are becoming increasingly complex as technology advances. Individual manufacturers now design only a fraction of the parts and components themselves and source the rest from suppliers, who are thus integrated into the value chain. As a result, machine builders often lack the necessary expertise to effectively monitor all parts of their plants. In addition, the combination possibilities of the most diverse components result in an increasing number of use cases, for each of which condition monitoring must be redefined and elaborated on a case-by-case basis.

It will therefore be necessary, especially in the case of complex systems, to bring together the know-how of the individual partners in the future. Only in this way can optimum availability, data analyses and consulting services be developed and offered to the end customer. But individual players can also benefit from collaboration within the framework of Collaborative Condition Monitoring. For example, component manufacturers often do not have the operating data of their parts because they are installed in higher-level systems and there is no access to it. The machine manufacturer usually only grants this in case of escalation. A constant exchange of data would remedy this situation. Ultimately, the machine manufacturer would also benefit from this, as his supplier would be able to offer him improved products.  

Enter the age of the smart factory with collaborative condition monitoring

The future of industry will move in the direction of networked production systems. All machines and plants integrated into the value chain will communicate with each other and exchange operating data, regardless of which company is responsible for operating the elements. In this way, not only is the efficient operation of individual plants ensured, but the interfaces between the machines can also be optimally monitored.

In this context, only "barrier-free" data structures can provide a meaningful basis to enable qualified analyses and AI applications. We must therefore move away from a bilateral exchange between two partners toward scaling across different companies. This, of course, shakes paradigms built up over decades, such as protecting intellectual property and safeguarding one's technological edge. This step is still very difficult for many players on the market. In the long term, however, it is precisely at these interfaces that the greatest potential for efficiency can be leveraged.

What are the hurdles of collaborative condition monitoring systems?

The unwillingness of individual companies to cooperate is caused by competition in the market and is one of the biggest obstacles on the way to a digital future in which individual companies share their data with each other and jointly develop value-added services for their customers. For example, what if both the machine manufacturer and the component manufacturer offer predictive maintenance solutions? In this case, the customer would pay twice for the service. The component manufacturer's data service would have to be paid for by the machine manufacturer, who would then charge the end customer for services for the entire system. However, this is still gray theory at the moment. Because of course everyone is trying to push their own solution and recoup the investment as quickly as possible on the market without first consulting other players.

New types of business models are needed in which economic incentives are created for the provision of operational data in order to also motivate players who do not initially profit from sharing their data. This is because the cost and benefit calculation for the mutual exchange turns out very differently on the side of the individual participants. The total profit must be distributed to individual players within the framework of collaborative systems according to the laws of the market. This requires innovative payment models embedded in novel structural frameworks. Conclusive answers must also be found to questions concerning data protection within the framework of these new types of structures.

Requirements for the implementation of Collaborative Condition Monitoring

The basis for the emergence of the data marketplaces addressed is the guarantee of neutral platforms that are oriented to international standards and have general, clearly predefined rules of the game. These cooperative integration platforms must have a sustainable business model. Common standards are needed so that everyone can not only share their data, but also use the data of others. This standard must be vendor and domain neutral. A trusted environment is needed in which individual stakeholders can retain sovereignty over their data and still benefit from its provision.

All data may only be shared to the extent and level of detail permitted by the data creator. Digital identities must be authenticatable. This limits access and use to the authorized group of people and makes the use of the data traceable. At the same time, the data must not be recognizable as brand- and product-differentiating.

Potential dangers of collaborative systems

The question arises as to who should create the necessary structures for such a marketplace and how the infrastructure required for it can be created. Who regulates the distribution of costs among possibly several thousand companies? One solution would be to set up service providers here who charge a certain price for participation or use. However, these organization(s) would very quickly develop great market power, which they could direct against the individual participants in the marketplace. Further critical thinking is needed here to ensure neutrality and data sovereignty in the data marketplace of the future.

A look into the future

Despite the difficulties posed by the collaborative systems mentioned, there will be no way around developing sensible solutions in this area in the future. Their implementation offers a great opportunity to strengthen the domestic industrial base and to make our own products and services more efficient overall. However, a great deal of thought is needed on how to make this development fair. Collaborative condition monitoring turns data into a sales product - in the best case for the benefit of all parties involved.