A game-changer tech for the rail sector
New generation of services, based on the performance reading of real-time data, become the must-have feature across all industries. Rail Transportation industry is not an exception. In a sector, where fleet reliability and availability is a key lever for increasing efficiency and reducing total cost of ownership (TCO), Condition-based maintenance (CBM) represents a quantum leap in maintenance for rail COOs.
These are huge savings. Even for the regional and cargo rail operators condition-based maintenance becomes a crucial factor to remaining competitive and growing their business. Yet, there are still significant profit drains and costly technology gaps within the rail sector’s maintenance game.
McKinsey estimates that CBM can not only reduce maintenance costs by 10 to 15%, adding up to 7.5 billion savings per year globally.
[source: McKinsey (2016): Huge value pool shifts ahead – how rolling stock manufacturers can lay track for profitable growth]
So far inspection and testing of the trains’ rolling stock have been some of the most complex and sensitive issues that rail operators and rail OEMs had to deal with. The entire rail transport industry lacks a scientific tool that defines the correct maintenance periods, calculated with respect to the specific conditions of use. This is a major source of costs for service operators and executives in charge of maintenance.
Today inspection intervals on wheelsets are not directly related to real service conditions nor are based on a crack propagation approach.
What’s more, trains are frequently brought out from service limiting their availability to verify critical components, including wheelset axles, which drives inefficiency and boosts maintenance times and costs even further.
Smartset® has become the new standard for CBM of wheelset axles. By measuring rolling stock in operation and analyzing the state of stresses of the wheelsets, it helps to optimize maintenance periods on the base of the real usage level with remarkable accuracy. This, in turn, means significantly cutting time and maintenance costs while keeping or improving the safety level of inspections.
Reduce Maintenance Cost
Our tests show that the SMARTSET’s technology could cut maintenance costs of wheelsets NDT inspections even more than 50% (depending on the type of vehicle and track routes).
Fewer inspections and much more efficient maintenance system, eliminates a huge profit drains due to unnecessary ‘train stops’ and leads to higher ROI. In addition, train OEMs can also boost their aftersales and make your offer more appealing to clients, increasing profit even further.
Improve Railway Safety
SMARTSET ® provides insights of the state of the railway lines and allows managers to identify spots where higher loads are generated. When abnormal operating conditions of the vehicle or the line are detected, the system sends warning in real time to the operator.
Data management is changing rapidly the railway industry… Artificial intelligence is the next step… The future is now…
CEO Lucchini RS Group
How Smartset Works
SMARTSET® Intelligent sensor, embedded on the axle, records continuously wheelset loads in operation in the form of bending and torsional stresses on the axle, and creates in real time the load spectrum of such loads.
These data are analyzed by STARCRACK algorithm, developed in collaboration with the Polytechnic of Milan, to estimate the optimal in service NDT inspection intervals.
By bringing together technology, big data and advanced analytics, SMARTSET® system can accurately estimate how many stress cycles a wheelset receives, their level, where it receives them and, finally, define when axles must actually be verified according to their real usage level. As a result, the optimal maintenance intervals are identified with scientific precision.
Smartset collects and analyzes the load received during the operation of the wheelset in real time.
Manager of R&D Center Zhibo Lucchini
Science Behind the Breakthrough
The STARCRACK algorithm is a derivation of the famous Nasgro equation, developed by NASA to calculate fatigue crack growth and fracture analysis within the space industry.
Based on 20 years of research and development by Lucchini RS, it has been validated by thousands of full scale experimental tests to guarantee maximum accuracy. Its effectiveness has been verified by Polytechnic of Milan.
The first results can be collected after 6 months of monitoring. Across the period of 6 to 12 months of continuous monitoring, the wheelset axle functioning is verified at all temperatures and in all load conditions (including seasonal activities).