Innovation is the key to success as it will improve our competitiveness by providing leading-edge technologies. We must always be one step ahead of the competition in terms of technology and know-how, particularly in energy efficiency, productivity, asset utilization, infrastructure and train availability improvements, passenger travel experience, and, last but not least, climate and environmental friendliness.
In recent years, I have concentrated on applied research in Rail Systems Engineering Modeling (RSEM). RSEM can enable the Design, Build, Operation, and Maintenance (DBOM) cycle of railways to become much more efficient by decreasing the cost during the defects and liability period and therefore increasing the project’s Return on Investment (ROI) and Return of Equity (ROE).
Rail Systems Engineering Model (RSEM) is a monocentric model that is made to: (a) Facilitate the systems engineer’s dilemma on complexity and synchronization; (b) Facilitate the understanding of rail systems understanding with regards to system design, construction, and engineering project management; (c) Aid in decision making (e.g., “what if scenarios”); (d) Explain, control and predict events; and (e) Save substantial money and time by identifying omissions and defects during the analysis and design stages. The main characteristic of the RSEM is that it enhances the Rail Systems Engineering (RSE) organization capabilities during the design, construction, and Verification and Validation (V&V) stages. This approach means that RSEM involves all the RSE domains and their requirements. Additionally, RSEM includes additional functions such as trade studies, program and engineering changes, quantitative risk analysis, and management. In RSEM, there are four primary RSE domains:
1. The RSE Requirements Domain.
2. The RSE Behavior Domain.
3. The RSE Architectural Domain.
4. The RSE V&V Domain.
The abovementioned domains must be interrelated. It is RSEM’s objective to keep all these relationships, of the various databases, inside the RSEM’s monocentric database. Thus, a single model can integrate all these different types of information into a single underline repository. This single model can help the rail systems engineer to perform accurate Rail Systems Engineering Analytics (RSEA) based on digital analytics.
RSEA helps systems engineering models to harness their data and use them to identify new opportunities. RSEA also includes Search Engine Optimization (SEO), which tracks keyword searches and uses that data for engineering purposes. RSEA is broadly divided into exploratory data analysis (EDA), in which new features in the data are discovered, and confirmatory data analysis (CDA), in which existing hypotheses are proven true or false.