Condition monitoring refers to the permanent or regular monitoring of the condition of a machine using 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, downtimes can be effectively reduced. This process forms the basis for the step from preventive maintenance to maintenance strategies from the area of Predictive maintenance (or even prescriptive maintenance). Maintenance strategies based on condition monitoring can not only rectify faults, but also analyze their causes. In this way, failures can even be predicted and prevented by initiating maintenance measures in good time.
How does condition monitoring work?
All condition monitoring systems work with the help of sensors that record relevant measured variables such as temperature, fill levels, vibrations and more. This indicates at an early stage if a machine is no longer running "smoothly". If the data supplied exceeds a certain limit value, the system triggers an alarm if necessary and informs the user of the respective fault on the machine.
These limit values are usually defined in advance by a technician or the development department. However, modern data analysis methods can also be used to do this on the basis of artificially intelligent algorithms. In this case, deep learning processes can be used to AI-supported predictive maintenance solutions to recognize trends and predict the 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 the machine's condition has a number of advantages. The failure of various wearing or used parts can be effectively predicted, which reduces the number of emergency repair calls. In the event of a fault, the technician also knows more quickly where the problem lies. Spare and wear parts can be kept in stock and planned for the long term.
It is easier to identify and reduce harmful influences. This also increases the operational reliability of systems. In addition, companies can operate intact components beyond their regular service life and conserve resources. The extended service life is therefore a major advantage for environmental protection and reduces the maintenance cost budget.
Condition monitoring and innovative business models
The increasing processing of data is creating the space for innovative digital business models and Value Added Services in the 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 there are hurdles that need to be overcome first.
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 greater reliability and a longer service life for production facilities. To achieve this, an economic incentive must be created for the participating players to share data with others across competitive boundaries on a neutral platform.
Using increasing complexity and larger data volumes effectively
Machines and systems are becoming increasingly complex as technology advances. Individual manufacturers now only design 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 manufacturers often lack the necessary expertise to effectively monitor all parts of their systems. In addition, the possible combinations of different components result in an increasing number of applications for which condition monitoring must be redefined and worked out on a case-by-case basis.
It will therefore be necessary to pool the expertise of the individual partners in future, particularly in the case of complex systems. This is the only way to develop and offer optimal availability, data analysis and consulting services for the end customer. However, individual players can also benefit from cooperation within the framework of collaborative condition monitoring. For example, component manufacturers often do not have access to the operating data of their parts as they are installed in higher-level systems. The machine manufacturer usually only provides this in the event of an escalation. A constant exchange of data would provide a remedy here. Ultimately, the machine manufacturer would also benefit from this, as their supplier would be able to offer them improved products.
Entering the age of the smart factory with collaborative condition monitoring
The future of industry will develop in the direction of networked production systems. All machines and systems integrated into the value chain will communicate with each other and exchange operating data, regardless of which company is responsible for operating the elements. This not only ensures the efficient operation of individual systems, but also enables the interfaces between the machines to be optimally monitored.
Only "barrier-free" data structures can provide a meaningful basis for enabling qualified analyses and AI applications. We must therefore move away from a bilateral exchange between two partners towards scaling across different companies. In doing so, we are of course shaking up paradigms that have been built up over decades, such as the protection of intellectual property and securing our own technological lead. This step is currently 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 due to competition on 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 pass on the services for the entire system to the end customer. However, this is still gray theory at the moment. Of course, everyone is trying to push their own solution and recoup their investment on the market as quickly as possible without consulting other players beforehand.
New types of business models are needed in which economic incentives are created for the provision of operating data in order to motivate stakeholders who do not initially benefit from sharing their data. This is because the cost-benefit calculation for the mutual exchange of data varies greatly between the individual parties involved. The entire 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 that are embedded in new structural framework conditions. Conclusive answers must also be found to questions relating to data protection within these new structures.
Requirements for the implementation of Collaborative Condition Monitoring
The basis for the development of the aforementioned data marketplaces is the guarantee of neutral platforms that are based on international standards and have general, clearly predefined rules. These cooperative integration platforms must have a sustainable business model. Common standards are required so that everyone can not only share their data, but also use the data of others. This standard must be manufacturer- and domain-neutral. A trustworthy environment is required in which individual participants can retain sovereignty over their data and still benefit from its provision.
All data may only ever 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 differentiating between brands and products.
Possible dangers of collaborative systems
The question arises as to who should create the necessary structures for such a marketplace and how the required infrastructure can be created. Who regulates the distribution of costs among possibly several thousand companies? One solution would be to set up service providers that 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 consideration is needed here to ensure neutrality and data sovereignty in the data marketplace of the future.
A look into the future
Despite the difficulties arising from the collaborative systems mentioned above, there will be no getting around developing sensible solutions in this area in the future. Implementation offers a great opportunity to strengthen the domestic industrial location and to make our own products and services more efficient overall. However, a large number of considerations are needed to ensure that this development is fair. Collaborative condition monitoring turns data into a sales product - ideally to the benefit of everyone involved.
see also: Collaborative data-based business models
