Bogies and wheelsets: Expert panel
As part of our Bogies & Wheelsets In-Depth Focus, Global Railway Review asked our Expert Panel: To what extent do you think Industry 4.0 has impacted the maintenance, management and operation of bogies and wheelsets?
Michal Vere?, Member of the Board of Directors and Deputy to the General Manager for Operations, Czech Railways (?D):
Through investing in new ‘predictive’ technologies and equipment, ?eské dráhy (?D) is actively moving ahead in this ‘Industry 4.0’ era. We recently purchased a U2000-400 tandem underfloor lathe which has been installed at our Prague maintenance centre and can operate in both semi-automatic and automatic modes. Once a railway vehicle is manually positioned, the lathe automatically measures the wheel tread profile and selects the optimum machining mode. After the final cut it electronically measures and sends the results.
The subsequent processing of output data makes it possible to predict further operation of vehicles, planning of a future annex supplementary building for lathe turning operations, and makes it easier to predict defects on other parts of vehicles.
In the future, ?D will purchase automatic measuring equipment using defectoscopy methods, which, during the passing of a set of railway vehicles, can identify their number and can check tread profiles on individual wheels. This equipment is also capable of predicting the mileage that can be achieved until the next lathe turning intervention is required. This equipment will become a logical technological complement of the underfloor lathe, enabling ?D to manage their rolling stock more cost-efficiently and to carry out maintenance work more effectively.
Jorge Moral, LEAN Manager, CAF MiiRA:
Through Industry 4.0, CAF MiiRA has laid the foundations for constant improvement. We have created a digital platform to gather the data from products during their entire lifecycle; from design to maintenance tasks including manufacturing and operations.
In CAF MiiRA, we turned the typical maintenance system into a condition-based maintenance process, allowing us to improve maintenance costs, product quality and cycle time. The alarm for real-time events that has been designed as a warning system, shortening the response time and enabling continuous data gathering combined with artificial intelligence to make it possible to have the information to anticipate failures. Thanks to business intelligence tools, the customer can monitor the status of their products in real-time, making it easier and safer to take decisions.
Furthermore, Industry 4.0 allows us to monitor and connect the production process in different production plants, thereby optimising our planning and production and providing better customer service. The continuous technological progress, Internet of Things (IoT), machine learning, digital twins, correlation analytics or other artificial intelligence methods will give us opportunities to continuously improve the product and the service to end-users.
Thus, CAF MiiRA has shown unyielding commitment to Industry 4.0; a system that allows us to provide a more flexible and agile response to new market necessities.
Filip Rosengren, Manager Railway Segment, SKF:
Digitalisation assists rail operators to address their most pressing maintenance and reliability challenges: Higher train availability, lower costs and fewer early failures or unplanned stops. On-board condition monitoring technologies for bogies and wheelsets have proved their value over almost two decades of service around the world. SKF? has supplied more than 17,000 axlebox condition monitoring sensors of different types to rail customers since 2001. Today, cloud-based systems are taking the power of condition monitoring technologies to the next level. Operators can bring all the data relating to a bearing or other components together in one place, creating a ‘digital twin’ which reflects the performance and service history of every asset. They can apply expert remote monitoring, machine learning and advanced analytics technologies to identify potential problems earlier. They can gain new insights from the aggregation of data across their fleet, and then use that data to optimise operating standards, maintenance scheduling and inventory planning. The impact of these new approaches can be significant. For example, an inter-city operator may use SKF Insight Rail system and methodology to extend bogie maintenance intervals from 1.2 to 1.8 million kilometres, while simultaneously reducing the occurrence of unplanned stops across its fleet and optimising its approach to component refurbishment. Together, those changes can lead to a reduction of more than 21 per cent in overall bogie maintenance costs.