# Systems Engineering: Availability and Reliability
## 5 Systems Analysis, Simulation, Design and Modeling  
The activities performed by the maintenance department are usually a combination of technical, administrative and management activities carried out during the life cycle of a given technical object. The effective implementation of these activities requires appropriate data management--data collection, proper analysis and the use of appropriately effective models to support decision making. We use the data for many different purposes, e.g., to determine the moment of generating service orders, monitor the quality of performed activities, optimize and plan activities or develop plans for the delivery dates of materials and spare parts, i.e., in the field of broadly understood logistics for the operation of technical facilities and systems. This is why proper data management and knowledge management are extremely important issues for the implementation and effective maintenance strategies when it is possible not only to determine the current wear of devices or their structural elements but also to predict whether and when a failure or catastrophic wear will occur. It is also possible to effectively detect the cause of a failure or identify performance or product quality problems. To achieve this, adequate analysis, design and modeling methods are required. This is why [PERSON] and [PERSON] [11] have focused on developing a framework using I4.0 enabling technologies to improve reliability and safety in human-robot collaboration applications. The proposed framework allows a robot's condition to be continuously monitored during human-robot collaboration. The monitoring deploys IoT connectivity, a data acquisition system, physical cyber-systems and ML tools to perform analytics. The paper is divided as follows: the relevant equipment parameters are first identified, a description of the data acquisition framework is then given, an application to an assembly case study in which all the necessary data are collected is presented, and finally, the analysis results of the considered case study are presented and discussed. The case study was performed on benchmark tasks for collaborative assembly processes. An automatic machine learning tool was used to perform online monitoring and predict outages of the industrial robots during a human-robot collaboration process. Such an online monitoring system allows more reliable human-robot collaboration applications to be created, unplanned down-time during task execution to be eliminated, and the trust of humans during interaction with a robot and the lifetime of the robot to be maximized. The proposed framework demonstrates data management techniques on an industrial robot that is considered as a physical=cyber system. Achievements of accurate robotic arm-based bike frame quality checks with the use of a 3D mathematical model are discussed by [PERSON] et al. [12]. Unlike the traditional way to find coefficients of a space sphere, the proposed model requires only three check point coordinates to achieve the sphere axis coordinate and its radius. In the practical work, the contact sensor combined with the robotic arm is used to realize the compliance items measurement in shaft length, internal diameter, verticality, parallelism, etc. The proposed model is validated based on both mathematic verification and an actual bike frame check. The stylus probe used in the proposed model presents a simple and accurate performance. However, successful measurement depends on the activity range of the robotic arm that certain features of bike frames should be reached by the stylus probe. In future work, the optical sensors may provide an alternative solution, although more complex signal processing algorithms should be addressed. The quality and reliability of consumables, including gear oils, resulting in the failure-free operation of the transmission components in heavy trucks are discussed by [PERSON] et al. [13].