Solutions | XAPIKA

Overview

Moving away from the conventional system where assets such as trains, maintenance crews, engineers, and storage hubs are handled separately, our smart maintenance strategy centralizes everything under one integrated platform, driven by real-time data. This data is available for remote access on any digital device. Our solutions in railway maintenance revolves around unified solutions such as smart transportation systems and predictive maintenance tools. Our methodology encompasses condition-based maintenance to extend maintenance intervals and pioneers in innovative maintenance services. It's crucial to recognize that trains, with their thousands of moving parts, aren't impervious to failures. Cutting-edge maintenance tools combat these potential hitches, adopting a gamut of methodologies from condition-based to predictive maintenance. This modernized approach to maintenance is not only cost and time-efficient but also adept at integrating innovative solutions, ensuring trains continue their journey safely and promptly. What sets our system apart is its capacity to monitor in real-time and generate AI-driven assignments based on the train's health status. Furthermore, it's designed to instantly notify engineers and technicians through alerts, and dispense Automated Work Orders, ensuring uninterrupted operations while heightening passenger safety and comfort.

Smart Supply Chain

One of our pivotal digital innovations in railway operations is the smart management of our warehouse. With tens of thousands of components needed for regular replacements, efficient organization within the depot is crucial. This involves considering multiple variables and constraints, including part and labor availability, depot space, time, and expense.

Our Intelligent Supply Chain (ISC) keeps a real-time tab on inventory. Whenever a component is requested, the automated inventory system oversees every phase of the component's journey, from its retrieval from the depot to its fitting on a train. Every individual who interacts with or installs the component is logged into the Integrated Communication Technology (ICT) system for future reference. By collating information about a component's installation with the train's mileage, we can ascertain its usage duration. This data is pivotal in preemptively determining when the component might need replacement, preventing potential failures.

Moreover, the ICT can actively monitor inventory levels. Should the stock of a specific component dip below a set threshold, the system initiate an automatic procurement process, ensuring stock levels never deplete entirely. This automatic reordering mechanism, triggered by minimum stock level tracking, ensures a seamless supply chain and uninterrupted operations.

Wagon Tracking

Freight wagons are unpowered rail vehicles used to transport goods. Unlike locomotives, many of these wagons only have a physical identifier number and can number in the tens of thousands. Managing them is a challenge as they aren't actively tracked, and their maintenance often relies on reactive methods or is overlooked. This can result in significant operational disruptions and costs. In practice, some wagons are used more intensively than others, but without a tracking system, it's difficult to differentiate between them. Moreover, directing each wagon to specific stations is manually done due to their passive nature, leading to errors and misdirections. The lack of digitization makes it hard to determine a wagon's mileage, which if known, can aid in better maintenance scheduling. By implementing RFID tags to track wagons at every station, we determine their mileage based on known station distances. This system would enable timely maintenance, aligning with a Circular Economy model and ensuring more efficient operations.

Digitalization and AI

Digitalization of Maintenance by Conditional and Predictive Maintenance in the railway sector enhances train uptime, boosts efficiency, and curtails maintenance expenses by scheduling servicing based on real-time equipment conditions. Conditional Maintenance employs real-time monitoring via sensors, IoT technology, and data analysis to evaluate train equipment health and foresee maintenance needs. Its objective is to limit downtime, heightening overall train performance, efficiency, and safety.

Conversely, Predictive Maintenance uses data and IoT sensors to gauge the actual state of train equipment and schedule maintenance solely when necessary. This strategy emphasizes enhancing train performance and reliability while minimizing downtime, focusing on maintenance based on actual needs rather than fixed schedules. The result is optimized resource use, cost savings, and a more dependable train system.

Both these methods are integral to digitalization, ensuring smooth operations and lessening the chances of equipment failures. These equipment ranges from energy storage and power systems to communications, train management software, and mechanical parts like axle bearings and doors. All these components synchronize to guarantee a seamless, safe, and comfortable passenger experience.