Artificial Intelligence in Railways
Artificial Intelligence is deemed as the next big technology disruptor and rightly so. AI is being applied to all spheres of the industry with incredible business impact and now, the railway industry is emerging to tap the potential of AI.
The rail industry is so huge that when it is running thousands of trains per day, there is a good number of chance that errors may arise due to heavy dependence on humans. Any solution that keeps the train running without delays and accidents and one which enhances customer experience are gladly accepted.
The major challenge in the railway industry
Signal mishaps are the most frequent challenges that the railway industry faces. AI can be effectively used to predict signal failures thus preventing them from happening. Railways have already introduced remotely controlled monitoring with the aid of sensors. This supervises various signals, axle counters and their parts leading to interlocking and power supply.
How AI solutions can be used in the railway industry
AI essentially consists of condition-based maintenance (CBM) and predictive maintenance (PM). For example, when a defect is found in the doors with a diagnostic monitoring tool, there is bound to be some amount of delay and it causes inconvenience to the customers. In an ideal situation, such defects should be taken care of beforehand, rather than during production or operation stages.
Predictive maintenance avoids such scenarios. It gives abundant time to verify the defect and take timely action.
Other benefits of AI which can be used in the railway industry are:
- Optimization of the routes
- Tracking and surveying the train movements in real time
- Assisting the railway crew
- Making cost benefits of rail freight better
- Enhancing the existing logistics chain integration
- Management of resources with the help of predictive maintenance
- Chatbots for better smooth interaction with customers without any hindrances of languages
- Sensors for detecting locomotive or track defects
This can be used by accumulating real-time data from the trains under operational status and reviewing it by various parameters like spatial, nodal and temporal. The spatial parameter provides actual location details of the trains. So it gives an idea about the train systems, where they are located and their performance. Temporal factor provides details of the performance with regards to the minute by minute account. The nodal factor predicts how the subsystems are connected to one another and also points out the causes of failure in the overall system behavior.
Data is used to refine the rolling stock maintenance. The right data at the right time can help in achieving this. This also helps in fleet monitoring. This type of maintenance reduces the cost, improves overall safety and rail engines stay out of service for a lesser time, thus maximizing the services and their availability.
Thus, AI has the potential to develop automated trains that slowly and steadily assist the driver, and can also lead to a pont in the future where AI can replace the driver. Though there are certain apprehensions about AI application, if it is done correctly, AI is here to stay.