The need for the maintenance of railway track systems have been increasing. Traditional methods that are currently being used are either inaccurate, labor and time intensive, or does not enable continuous monitoring of the system. As a result, in-service train vibrations have been shown to be a cheaper alternative for monitoring of railway track systems. In this paper, a method is proposed to detect different maintenance needs of railway track systems using a single pass of train direction. The DR-Train dataset that is publicly available was used. Results show that by using a simple classifier such as the k-nearest neighbor (k-NN) algorithm, the signal energy features of the acceleration data can achieve 76\% accuracy on two types of maintenance needs, tamping and surfacing. The results show that the transverse direction is able to more accurately detect maintenance needs, and triaxial accelerometer can give further information on the maintenance needs. Furthermore, this paper demonstrates the use of multi-label classification to detect multiple types of maintenance needs simultaneously. The results show multi-label classification performs only slightly worse than the simple binary classification (72\% accuracy) and that this can be a simple method that can easily be deployed in areas that have a history of many maintenance issues.

The Economic Times has reported that Indian Railways will put artificial intelligence to use to manage track maintenance blocks. An official from the railways said that the artificial intelligence technology, which is capable of detecting the condition of railway tracks, would soon be deployed to prepare a repair and replacement calendar. Indian railways accounts its unplanned track maintenance work for its notoriety in the world for its delayed operations. With the use of artificial intelligence, it is expected that at least 90% of trains will run on time since the maintenance work would be planned in advance. The AI will help in creating a calendar according to which the maintenance work can be scheduled beforehand leading to trains running punctually.

NEW DELHI: Artificial intelligence (AI) that can diagnose the condition of rail tracks will be used by the railways to prepare a repair and replacement calendar and improve punctuality of trains, according to an official. In India, unplanned track maintenance work is often cited as a reason why train operations descend into chaos. Use of AI, the official said, will ensure that at least 90% of trains run on time as routine maintenance work would be planned in advance on the basis of the AI-aided calendar. All large maintenance blocks will be taken up only on Sundays to minimise the impact of train delays, the official said, adding that the national transporter is already procuring automatic track detection machines which, through AI, can predict the life of tracks and track joints. The move is also seen substantially reducing the number of train accidents.

A computer that learns to play a'scissors, paper, stone' by observing and mimicking human players could lead to machines that automatically learn how to spot an intruder or perform vital maintenance work, say UK researchers. CogVis, developed by scientists at the University of Leeds in Yorkshire, UK, teaches itself how to play the children's game by searching for patterns in video and audio of human players and then building its own "hypotheses" about the game's rules. In contrast to older artificial intelligence (AI) programs that mimic human behaviour using hard-coded rules, CogVis takes a more human approach, learning through observation and mimicry, the researchers say. The older approach is fraught with problems, as computers struggle when faced with situations that fall outside the remit of these rules and when new rules are introduced. "A system that can observe events in an unknown scenario, learn and participate just as a child would is almost the Holy Grail of AI," says Derek Magee from the University of Leeds.

Bengaluru: Wipro Ltd, which launched its artificial intelligence (AI) platform Holmes 18 months ago, is now making its biggest push to embrace automation by allowing more of its managers to identify work which will not require engineers in each of the over 20,000 projects currently underway. This "bold" development, according to one executive who did not want to be identified will mean Wipro doesn't just save on costs (thereby arresting falling profitability), but fundamentally alters the traditional model of deploying armies of engineers to undertake maintenance work. To be sure, that could also mean that its current workforce needs to learn new technology skills swiftly to stay relevant. Wipro is in the process of appointing individual leaders in each of the six industry-serving segments (which the company calls strategic business units) and five solution offering verticals or practices, who will be entrusted with the job of automating mundane maintenance work. Until now, Wipro's chief technology officer K.R. Sanjiv, in consultation with various segment heads used to decide which projects could use Holmes.