FCE Frankfurt Consulting Engineers GmbH (FCE) is a mathematically oriented engineering firm based at Frankfurt Airport in the “House Of Logistics and Mobility (HOLM).” FCE was founded in 2004 as a German limited liability company based on the 12 years of experience of founder Dr. Wolfgang Mergenthaler as an independent engineer and 20 years as an employee in the automotive industry in Germany and the USA. FCE has extensive experience in the industrial application of mathematical statistics, mathematical optimization, pattern recognition, etc. From the very beginning, the research area consisted mainly in the analysis of multivariater stochastic sensor time series and, in particular, in the prediction of time series for a final planning horizon. If the sensor values are accompanied by certain significant events worth predicting, the above task may be limited to predicting those events. Another area of activity of FCE for many years has been the task of mathematically modeling certain aspects of production, especially with regard to the design of optimal production processes, for example in the automotive world. Furthermore, the entire field of multivariate statistics stands with all its effects such as parameter estimation, non-linear regression, etc. At the heart of FCE’s expertise.
The main task of FCE is to use state-of-the-art computer mathematics and computer science to solve industrial problems. In industrialization history, mathematics has often been shown to be an indispensable tool in mechanical engineering and production. An example of this is the Traveling Salesman Problem (TSP) as a modelling backbone in many sequencing and planning processes. FCE sees itself as a pioneer in the field of predictive maintenance. FCE has been researching and developing new machine learning techniques for more than 10 years.
The future work of FCE will be strongly influenced by our general research activities in the fields of pattern recognition and machine learning, as well as from the needs of industry, as well as from our partners in automotive, aviation, rail and rail. Energy industry. Another promising area of industrial data analysis emerges from the naval industry. As an example, one can imagine the engine of a container ship that generates large flows of signal data, the important information about fuel efficiency, emissions, machine vibrations, etc. Contain. The ship operator might be interested in the relationship between environmental conditions and control variables. It also allows fuel efficiency. State-of-the-art non-linear regressions and data visualization techniques allow the operator to understand how his engine works to find optimal operating conditions under certain operational constraints. The development of the Internet of Things (IoT) is one of the most exciting industrial possibilities at the beginning of the 21st century. Century. Monitoring the efficiency of moving or non-moving objects can become a central task of an IoT service provider sooner than or later.
FCE targets an industrial market characterised by one or more of the following features. Companies that work with large plants produce goods through a range of workplaces, particularly branches, which collect a large amount of measurement data throughout operations, knowing the value of the data and its analysis.
In the past, the FCE has been active mainly in the following markets:
- Rail transport
The FCE expects all of the above and many other industries to remain interested in collecting data for their business. As far as is customary in the media, it should also be noted that only through an appropriate and continuous analysis of the collected data can the data achieve added value from their efforts. The global MRO market in the aviation industry will grow to $74.3 billion in 2017 from $63.2 billion in 2016.
Intelligent maintenance and process optimizations are required, especially for special data evaluation routines. It is well known that the Internet of Things (IoT) paradigm is a perfect platform for organizing and automating intelligent maintenance and process optimization.