Under intense pressure to maximise fleet availability? New condition monitoring technologies managed in the cloud are creating a paradigm shift in maintenance efficiency and rolling stock reliability. STEVEN DICKSON explains
Over the past 20 years, UK rail passenger numbers have more than doubled, while the passenger fleet has increased in size by only 11 per cent. To keep up with demand, operators need to ensure that rolling stock can spend as much time as possible in service. But that pressure can lead to conflicts. On the one hand, companies must avoid equipment failures and unplanned downtime wherever possible, on the other they need to minimise both the frequency and duration of scheduled overhauls.
The rail sector is not the only industry seeking to maximise asset uptime and reliability, however, and across sectors there is growing interest in the potential of information technology to squeeze additional value and productivity out of machinery. One area of particular interest is the internet of things (IoT), a term coined to describe the use of network-connected devices to collect and communicate rich information on asset location, status, performance and health.
Management consultancy McKinsey estimates that the market for products and services based on IoT technologies was $900 million in 2015, and forecasts it to reach $3.7 billion by 2020. Research company Forrester, meanwhile, highlights fleet management in transportation sectors as a key area where the IoT is likely to create value in the coming years. In the aviation sector, for example, Airline Lufthansa has announced plans to transform the way it plans and operates its maintenance and repair activities based on IoT technologies and smart data analysis.
Sense and respond
Are these technologies likely to help the rail sector address its availability challenges? Many of the key technologies are already available. Axlebox bearings are increasingly fitted with speed sensors as part of Automatic Train Protection (ATP), traction control and braking systems. Modern systems, like the SKF Axletronic range, can also be equipped with additional sensors to monitor bearing temperature and vibration. Depending on the application, these sensors can be integrated directly into the bearing unit, or installed on special end cap that can be fitted to any bearing.
Linked to appropriate analysis software, data from these sensors can provide information on the condition of bearings, wheels and bogies. Today, these systems are highly effective at detecting failures, like worn out bearings or flat spots on wheels. They can work faster and more reliably than alternative approaches, like track side bearing temperature sensors. That simplifies inspection and diagnosis, and allows operators to intervene more quickly to fix things when they go wrong.
Spotting problems after they have already occurred only captures some of the potential of IoT technologies, however. Bigger benefits come from systems that are sensitive enough to detect the early warning signs of emerging issues, allowing operators to intervene to prevent problems, or even taking automatic action to do so. In the case of bearings, for example, subtle changes in operating temperature or vibration levels can indicate poor lubrication conditions. A system capable of detecting this situation could be linked to an automated lubrication device, triggering corrective action and extending the life of the component.
The most advanced condition monitoring systems available on the market today go a long way along the road to automated predictive and preventive maintenance. Developed exclusively for railway applications, the SKF Multilog Online System IMx-R, for example, uses modular sensors that monitor and transmit a range of operating condition data. Vibration sensors detect the dynamic frequencies of bearing elements such as rollers and inner ring raceways. The Multilog IMx-R uses this data, along with information about specific bearing geometry and shaft speed, to identify bearing problems very early on, enabling more cost-effective maintenance planning and optimised bearing lifecycles.
SKF Multilog IMx-R bearing vibration sensors can monitor traction motors, gearbox bearings and toothed wheels, and cardan shafts and couplings. Along with vibration frequencies, the system processes speed, load and gear-box ratios to detect unbalance, misalignment, shaft bending, loose parts, damaged bearings or gear wheels, and resonance. Gearbox oil temperature, level and condition can be included as part of the system, or function in a stand-alone mode. Along with early fault detection, the Multilog IMx-R generates automatic advice for correcting existing or impending conditions.
The condition monitoring approach can provide a valuable early warning of developing problems, allowing operators to act before a failure occurs. But as importantly, it also helps operators understand when things are operating properly. That allows components like bearings to run for longer, reducing the need for time consuming, and potentially unnecessary, scheduled replacement.
From the train to the cloud
Systems like the Multilog IMx-R can also use radio communication to transmit alarms, warnings or condition data to networks beyond the train. That allows them to link directly to maintenance management systems for scheduling, spare part and work order management, for example.
This cloud-based approach to condition monitoring offers some other important advantages, compared to systems that rely on on-board data collection and analysis tools. Moving the analysis to a remote location allows the use of greater computing power, for example, and simplifies the software update process, allowing operators to test and adopt smarter and more sensitive algorithms as their understanding of equipment performance evolves.
Centralising data from a fleet of rail vehicles also helps operators build a better understanding of overall fleet reliability. Such an approach can reveal that components from particular suppliers, or those installed in particular workshops, are more likely to suffer early failures, for example. That information can be used to inform design, procurement or training decisions.
Fleet wide data has other potential too. In thesteelmaking sector, companies are already combining vibration data collected from the bearings in overhead crane wheels with information on the position of the machine to detect cracks and other problems in the rails on which they run. Aggregating fleet data on vibration and rolling stock location could offer similar insights to rail operators.
Wireless network technology
One important challenge for rail applications until now has been that installing the array of sensors required to collect data from key components requires complex networks of additional wiring. Those cables are costly to install, and their presence makes routine maintenance more time consuming and difficult.
Now, working with academics from two leading Italian institutions, a team of engineers has shown how a network of sensors can operate using low power wireless communications, greatly simplifying design, installation and maintenance. The group presented its research work at the 11th World Congress on Railway Research in Milan in 2016.
Making a wireless network suitable for rail condition monitoring applications is difficult for several reasons. First, the sensors must be able to operate for long periods without being recharged or replaced. The axleboxes on some modern passenger trains may be expected to travel more than a million kilometres between overhauls, for example, and operators have ambitions to double that figure. The need to generate and store their own power means the sensors must be extremely energy efficient, greatly limiting the power available for the transmission of wireless signals.
Set against this need for low power is the large size of rail vehicles. A sensor mounted at the axlebox might have to transmit to a receiver almost 20m away in the centre of the vehicle, for example. Rail carriages are difficult environments for wireless transmission too. Large quantities of conductive material in the bogies, chassis and body of the vehicle can all block or interfere with signals.
The SKF team has developed a special antenna design specifically for sensors installed on rail vehicles. The team has shown how sensors can be equipped with both transmitter and receiver antennae, with each sensor communicating with its neighbours to send information along the train to a data logging unit in the driver’s cab. The approach, which has been proved in detailed simulations and real world tests, has the potential to deliver a simple and extremely robust wireless network. In the event of the failure of a sensor, for example, responsibility for relaying data can be automatically taken by other nodes in the network.
Current and emerging network technologies are paving the way for a transformation in rail fleet reliability and availability. The ability to access detailed, real time data on fleet performance will allow operators to run their assets for longer between scheduled overhauls, and to respond faster to emerging issues, minimising in-service failures and unplanned downtime.
Steven Dickson is strategic account manager railway at SKF www.skf.com