PoC Events Reporting: Trains & Metros

This project consisted of a Proof-of-Concept (PoC) aimed at demonstrating the feasibility and value of automated report and dashboard generation from event data from trains and metros. Carried out independently, the success of this PoC not only validated the technical approach but also led to the adoption of the solution by numerous international Alstom projects, particularly at key sites in Singapore and India.

Context:

The collection and analysis of event data (incidents, alerts, status changes, etc.) from train and metro fleets are crucial for predictive maintenance, operational optimisation, and passenger safety. Prior to this PoC, the reporting process could be fragmented or manual, making it difficult to obtain a quick and reliable overview of events, infrastructure and equipment health, and alert conditions.

Problem:

How to establish an efficient and fully automated system for generating reports and dashboards from complex event data? The aim was to provide clear and actionable views of the latest events, the health of infrastructure and equipment, and the alert conditions, while ensuring scalability and replicability for international deployment.

Solution:

The solution relied on harnessing the power of Tableau for visualising event data. I designed and implemented data ingestion and transformation pipelines that feed these platforms. Specific dashboards were developed to meet key needs: a report on the latest events (allowing for immediate responsiveness), a report on infrastructure and equipment health (providing an aggregated and trend view), and detailed reports on alert conditions. Full automation of the reporting process was a cornerstone, relying on tools like the HealthHub (HH) Task Scheduler and Cron, ensuring data freshness without repeated manual intervention.

Achievements:

• Autonomous Project Management and Client Coordination: I successfully led this PoC independently, from design to deployment. This included careful listening and consideration of specific client needs.

• Data Acquisition and Processing: I established a system for retrieving event data via the internal AlstomPy module and API queries in Python (using the requests library), then I processed and cleaned these events robustly using the Pandas and Numpy libraries.

• Development of Specific Reports: I designed and implemented essential Tableau and Kibana reports tailored to the needs of each client, covering the latest events, health of infrastructure and equipment, and alert conditions.

• Reporting Automation: I established the automation of the report generation and dashboard refreshing process, using scheduling tools like the HealthHub (HH) Task Scheduler and Cron, ensuring real-time availability of critical information.

• Autonomous Deployment and International Adoption: The solution developed was deployed independently on client execution platforms and has been successfully adopted in numerous international projects by Alstom (on-site, in Singapore, India, etc.), validating its effectiveness and scalability on a global scale.

Technical Stack:

• Visualisation Tools: Tableau Software, Kibana

• Languages & Libraries: Python (Requests, BeautifulSoup, Pandas, Web, internal AlstomPy module)

• APIs: HealthHub (HH) APIs

• Orchestration / Scheduling: Task Scheduler (HealthHub), Cron

• Development Environments (IDE): PyCharm, Jupyter Notebook, Linux

• English (for documentation/technical communication)