During this presentation, we will unveil HTM's innovative digitalization project for their Avenio trams, equipped with advanced data boxes that provide essential measurement data in near real-time. This data is utilized by maintenance technicians through a specially developed app, enabling faster and more effective fault resolution.

We will delve into the technical specifications of the data boxes, focusing on how they collect and process vast amounts of operational data. The system generates real-time insights from numerous sensors and operational parameters, creating a comprehensive digital footprint of each trams performance.

A key future development includes the creation of an intelligent knowledge base, supported by a large language model, which will make our technical documentation more accessible and further optimize our workflows. Additionally, this year we're implementing enhanced logging methods and predictive maintenance capabilities, based on the collected measurement data. These adaptations aim to increase efficiency and minimize downtime by predicting and proactively addressing issues before they occur.

The data analytics pipeline processes information from multiple sources, including:

• Real-time operational metrics
• Sensor data from critical components
• Historical maintenance records
• Performance patterns and anomalies

This presentation provides a comprehensive view of how big data and smart technologies can transform urban public transport maintenance.

During this presentation, we will unveil HTM's innovative digitalization project for their Avenio trams, equipped with advanced data boxes that provide essential measurement data in near real-time. This data is utilized by maintenance technicians through a specially developed app, enabling faster and more effective fault resolution.

We will delve into the technical specifications of the data boxes, focusing on how they collect and process vast amounts of operational data. The system generates real-time insights from numerous sensors and operational parameters, creating a comprehensive digital footprint of each trams performance.

A key future development includes the creation of an intelligent knowledge base, supported by a large language model, which will make our technical documentation more accessible and further optimize our workflows. Additionally, this year we're implementing enhanced logging methods and predictive maintenance capabilities, based on the collected measurement data. These adaptations aim to increase efficiency and minimize downtime by predicting and proactively addressing issues before they occur.

The data analytics pipeline processes information from multiple sources, including:

• Real-time operational metrics
• Sensor data from critical components
• Historical maintenance records
• Performance patterns and anomalies

This presentation provides a comprehensive view of how big data and smart technologies can transform urban public transport maintenance.

During this presentation, we will unveil HTM's innovative digitalization project for their Avenio trams, equipped with advanced data boxes that provide essential measurement data in near real-time. This data is utilized by maintenance technicians through a specially developed app, enabling faster and more effective fault resolution.

We will delve into the technical specifications of the data boxes, focusing on how they collect and process vast amounts of operational data. The system generates real-time insights from numerous sensors and operational parameters, creating a comprehensive digital footprint of each trams performance.

A key future development includes the creation of an intelligent knowledge base, supported by a large language model, which will make our technical documentation more accessible and further optimize our workflows. Additionally, this year we're implementing enhanced logging methods and predictive maintenance capabilities, based on the collected measurement data. These adaptations aim to increase efficiency and minimize downtime by predicting and proactively addressing issues before they occur.

The data analytics pipeline processes information from multiple sources, including:

• Real-time operational metrics
• Sensor data from critical components
• Historical maintenance records
• Performance patterns and anomalies

This presentation provides a comprehensive view of how big data and smart technologies can transform urban public transport maintenance.