## 2 Background  
### Machine Industry  
At present, Industry 4.0 is one of the most important topics in the Swiss machine industry. Research and development is moving in this direction. The main points of interest are connected and optimised systems, smart machines, and sensors. Regarding data science, there are tasks in condition monitoring, predictive maintenance, and automated data analyses to solve. Artificial intelligence is quite new to the machine industry and based on the missing understanding, the scepticism is quite high. To counter this, it is crucial to create confidence with understandable explanations, simple models, and a high reliability of the system.  
The biggest difficulty for data science in the machine industry are the limited resources. The development time of a machine or a system can lie between months or a few years depending on the size and the complexity. In addition to that, the lifetime of the machine system can be up to 20 years or even more. During the whole lifetime, different components wear out and must be replaced. Therefore, a specific maintenance interval for each component is defined. The length of every interval is based on the experience and calculation of wear, including defined load spectra and operational conditions. Independent from the type of component, it is common that the end of life is not reached at the time of maintenance, and parts are replaced too early. An even worse scenario is when the component fails before the defined end of live. This results in downtime of the machines with a huge financial impact. In both cases, the scheduled maintenance costs money due to unnecessary or late maintenance. To prevent this, knowledge about the state of wear is essential. One possibility is to perform condition monitoring (estimating the state of wear) and another is to do predictive maintenance--in addition to the condition monitoring--that estimates the remaining lifetime and schedules the maintenance [15].  
Apart from the whole topic of time and lifetime, there are other limiting factors that are faced during development of the system such as costs, space, and feasibility. Most of the sensors are not expensive, but each added sensor leads to a higher complexity of the system and makes the development costlier. The most expensive part is the data collection, where the system must be in operation and measurements over the whole lifetime are taken. The machines are designed as small as possible as necessary. The location of the sensors is given by the available space and the structure. A general advice is to use the sensors that are essential for the basic functions of the machine or the whole systems. These points are valid for existing machines as well as machines in the development phase. Apart from the technical difficulties, there are other concerns such as those about security and data protection. These are not relevant for this paper.  
The main limitation that affects machine learning in the machine industry is the limited data based on the long lifetime and limited number of sensors that are relevant for the specific task. In most cases, artificial intelligence needs a huge amount of data to be trained and perform well. The lack of the required amount of data can lead the algorithms to fail. To obtain good results with little data, hybrid machine learning models can be used. These combine a conventional part with calculation or transformation with machine learning. With this method, additional knowledge is added and leads to more (specific) information that the algorithm can use and perform better. Furthermore, if physical knowledge is added, the algorithm may work for extrapolations as well, which is not the case for pure deep learning approaches.