One of the great things about Model-Based Systems Engineering (MBSE) is that the system model enables us to run all kinds of analyses, for example regarding the consistency, or compliance with company guidelines, to create reports and perform simulations. Often
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Managing the complexity of cyber-physical systems is a real challenge. Adding reuse and hundreds of product variants doesn't help either. Model-Based Systems Engineering (MBSE) helps you to get a grip on your system. I have been involved in several
Anomaly Detection Anomaly Detection refers to the problem of finding anomalies in (usually) large datasets. Often, we are dealing with time-dependent or at least sequential data, originating, for example, from logs of a software or sensor values of a machine or
We observe that numerous cyber-physical systems are rapidly gaining functionality and thus development gets more and more complex. Innovations are made possible in many areas by a complex interaction of sensor systems and software. Consider the development of autonomous driving, in which a multitude of different system functions must interact safely with one another in order to make complex decisions with the highest quality in order to transport people safely. In order to master the complexity, the classical, document-centered approaches of system engineering are no longer sufficient and are increasingly being replaced by model-based systems engineering (MBSE) approaches. The SPES modeling framework provides a comprehensive, tool- and modeling language-independent method for MBSE. It offers a whole range of concrete models, modeling techniques and activities. In this blog post, I will introduce you gently to SPES. I will explain the basic principles of SPES and give some pointers where to find more.