Railways to use artificial intelligence for preventing signal failures

By IANS
21st Nov 2017
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Aiming to reduce the possibilities of signals failing, Indian Railways has undertaken remote condition monitoring of the system. This is a new approach for the national transporter to predict failures through the effective use of artificial intelligence (AI).

The signalling system is vital for safe train operations and the railways completely depend on the health of its signalling assets along with real time information.

Currently, the railways follow a manual maintenance system and adopt find-and-fix methods. A key reason to introduce AI is to effectively follow a predict-and-prevent approach.

Image: Wikimedia Commons
"Now, we are introducing remote condition monitoring using non-intrusive sensors for continuous online monitoring of signals, track circuits, axle counters and their sub-systems of interlocking, power supply systems including the voltage and current levels, relays, timers," said a senior official from the Railway Ministry who is involved in the project.

The system entails the collection of inputs on a pre-determined interval and sending this to a central location.

As a result, any flaws or problems in the signalling system would be detected on a real time basis and rectified in order to avoid possible delays and mishaps.

The failure of signals is one of the major reasons for train accidents and delays.

Currently, remote monitoring of signalling is operational in Britain.

The system envisages data transfer through a wireless medium (3G, 4G and high-speed mobile) and data based on these inputs will be utilised, with help of Artificial Intelligence (AI), for predictive and prescriptive Big Data analytics.

This will enable prediction of signalling asset failures, automated self-correction and informed decisions on intervention strategies, said the official.

The railways have decided that trial be taken up in two sections of Western Railway and South Western Railway at Ahmedabad-Vadodara and Bengaluru-Mysuru.

Depending upon the feedback, the system would gradually be extended to other sections.

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