# Towards Flexible and Cognitive Production--Addressing the Production Challenges
## 6 Cognitive Data-Driven Approach to Improve the Assembly Line
### Predictive Maintenance  
The success of an overall assembly line largely depends on quality, reliability, and productions costs that are affected directly by the complexity of this manufacturing process. Implementing predictive maintenance (PdM) strategy can bring all these benefits together by ensuring that the product is operating in an ideal condition so as to avoid unexpected breakdowns. PdM approaches primarily detect early signs of defects and also foresee when a defect is about to happen [79].  
The quality of PdM approaches depends to a great extent on the quality of the available data [80]. The importance of this aspect has been clearly shown in the previous subsection (data contextualization). The challenges to ensure successful and applicable PdM are threefold. First, various types of data, such as assembly data (IMU and depth sensor, and head worn camera data), process sensory, log, and service data are collected throughout the production process [81]. In our use-case, the sensor data contains heterogeneous data  
Figure 8: The figure presents exemplary patterns describing micro-activities of macro-worksteps that have occurred during the assembly of a product. The figures on the right are reproduced with permission from [78], IEEE Transactions on Instrumentation and measurement, 2010. From top to down: wrenching, electrical screwing, hand screwing, manual screwing.  
including time series, numerical, categorical, and unstructured data. Secondly, some of these data are often missing, i.e., completely or partially. This leads to the lack of extracting the knowledge, which can be used as a basis later in the predictive models [80, 82]. Finally, the high variety of produced products leads to a well-known small data challenge [74]. In [82, 83, 84], PdM in the context of multi-component systems (MCS) has been analysed, which is a promising approach to handle the use-case scenario challenges. In particular, MCS appears to be an encouraging solution to handle the small data challenge and a high variety of products. Additionally, [PERSON] et al. in [74] support the argument that a great variety of products and the small data sets of reference measurements is a challenge for data driven approaches.  
In an industrial setting of End-of-Line (EoL) testing, where the last check of complex products is carried out after the complete assembly line, multiple tests are performed on the product in the course of the quality test. Devices that pass the quality test are considered as devices with correct functions (see Figure 9). The decision on the state of product after a quality test was performed and was previously left to the experience of domain experts [85]. However, with the introduction of mass customization, shorter product life cycles and digital supply chains, this approach has reached its limits, and the need for new approaches has become clear. PdM appears to be promising in this context. Several studies have been carried out with the aim for improving these challenges by employing PdM solutions. [PERSON] et al. [86] introduced a new predictive approach based on data-driven solution, which has also been implemented in the use-case. In particular, this approach demonstrates how EoL test-based quality control can be enhanced in case of missing usage data. Moreover, an MCS perspective in context of PdM is proposed to remedy the small-data challenge. Finally, defect prediction of low-quality products over time is conducted when contextual information is available. In this context, engineers are assisted to effectively perform quality control and fault diagnosis. However, the application of these approaches require a high level of explainability. One way to tackle this challenge is using, previously described, interactive visual approaches [87], as a tool to increase understanding, trust, and, as a result, the acceptance rate from engineers.