10143186Train condition Reporting and Automated Cleaning Kinetics (TRACK)ClosedSmall Business Research InitiativeInnovate UK**EYYA LTD**, a UK-based specialist company in **IoT (Internet of Things)**, is leading a project to develop a cutting-edge platform for the transport industry. Headed by **CEO Dr. Niazy Kioufi**, EYYA brings expertise in IoT adoption, and its team excels in **project management**, **IoT program implementation**, **AI and Machine Learning.** Current methods of **train condition reporting** are inefficient, manual, and often ineffective. To address these challenges, EYYA has proposed the development of **Train condition Reporting and Automated Cleaning Kinetics (TRACK)**, an innovative and streamlined solution for inspecting the condition and cleanliness of rolling stock before they arrive at depots. This custom-built train monitoring system leverages **image capture**, **Machine Learning (ML)**, and **Artificial Intelligence (AI)** to provide near real-time dashboards, analytics, reports, and alerts, delivering actionable insights for network managers and **Train Operating Companies (TOCs)**. **TRACK** automates external inspections by capturing images of trains and their carriages while in transit. These images are uploaded to a cloud platform, where an ML and AI engine evaluates the **cleanliness** of each carriage and identifies any damages (such as graffiti, scratches, dents, or other anomalies). **TRACK** is trained to identify and report issues with the external condition of trains, while also rating their cleanliness to determine when they should be scheduled for washing. This streamlined process informs depot and network managers whether a train meets cleanliness standards and highlights any anomalies or damages. **TRACK** is a **data-driven solution** that optimises maintenance and cleaning schedules, helping managers prioritise trains for service and maintenance. An added feature of **TRACK** is its ability to be deployed alongside **depot train wash systems**. By integrating a TRACK system at depots, the platform can analyse the cleanliness of a train post-wash and use a scoring system to predict when the next wash will be required. This proactive approach enables network managers and operators to maintain cleanliness and condition standards, generating **predictive maintenance schedules** for individual trains and carriages. By automating inspections, **TRACK** reduces the need for manual evaluations, minimising the risk of fines and external audits. **TRACK** delivers near real-time operational and performance data, essential for **preventative maintenance**. **TRACK** records train wash frequency, cleanliness levels, and carriage conditions, offering detailed analytics to ensure optimal system performance. Additionally, the **TRACK Cloud Platform** provides visual documentation of train cleanliness and external conditions both before and after washing, including an **audit trail** and wash frequency history.