In this blog, our CEO, Nick Koiza, shares his insights on how to improve decision making through sensor data on tracks.
When maintaining a rail network, it is impossible to be at the right place at the right time using manual monitoring due to lack of information on which parts of the infrastructure need repair or closer review. However, sensors lift the proverbial fog and give you the ‘visibility’ required to inform decisions on guiding and optimising maintenance operations. This type of smart monitoring becomes possible with effective end-to-end Industrial IoT (IIoT) solutions, enabling preventative and predictive maintenance to form the basis for rail safety strategies.
When looking at your options, such solutions as described above should target cost reduction and increased availability by removing unnecessary maintenance activities and potential failures. It is also beneficial to enable targeted maintenance operations only on those assets which have been identified to require attention.
Smart monitoring solutions can protect rail assets and increase their lifespan, whilst also averting catastrophic failures such as derailment. A fundamentally important factor relating to risk mitigation is an understanding of track bed condition. Without continuous examination of substructure and intervention where necessary, track bed condition can deteriorate to the point of service affecting failures. But how can you ensure you are preparing correctly to get the most from your IIoT predictive maintenance solution?
The importance of understanding substructure condition
A well-maintained substructure is critical to ensuring the smooth running of rolling stock, particularly when passing critical assets such as switches and crossings (S&C). This means the condition of the track bed needs to be regularly monitored and measurements made to inform maintenance decisions.
However, this is not always a straightforward process given the need to consider aspects such as train speeds, axle load, track stiffness, traffic patterns, the type and effectiveness of maintenance activities. Other critical factors, such as temperature, weather and other environmental conditions also come into play, all of which will have some bearing on the level of degradation to track substructure.
Assessing track bed condition and carrying out measurements
Traditionally, assessment of substructure condition has been executed using geometrical tools to measure vertical or lateral alignment and track gauge, for example, mechanical void meters measuring vertical displacement or deflection. These will generate a maximum displacement value, based on all rolling stock passing the site over the period during which the measurements were made. However, such solutions will not enable a reading for every train and will therefore limit a more detailed analysis that would take into consideration train speed, weight, type of track etc.
Void meters also require constant attention, being prone to dislodging from the track and inaccurate readings, unless properly installed and maintained. Furthermore, it is not possible to detect the presence of voids using track alignment or other geometrical methods to prevent asset degradation.
Geometrical measurement using sensors offers a considerably better approach. Specifically, low size, weight and power (SWaP) IIoT devices can enable precise measurement from the track itself using advanced sensors that are installed on sleepers in fixed positions next to critical infrastructure assets, such as S&C etc. This approach can target highly accurate and continuous vertical displacement measurement, as is the case with SCT’s SWiX solution.
Measuring vertical displacement
Railway organisations can benefit from understanding the precise level of vertical track displacement as rolling stock passes, especially at the location of critical assets. This type of deflection can be established by measuring the vertical differential before and at the exact moment each train passes. By continuously measuring deflection where such components are deployed, the condition of the substructure can also be identified; this is important as voids can be present under sleepers. In such cases, a risk can arise from the track bed not being adequately supported, which can lead to high vertical displacement and potential train derailment in the worst case; at the very least, rapid wear on the asset and a reduction in lifespan.
The risks posed by poor substructure and voids mean it is essential that vertical displacement is continuously monitored as part of an effective preventative maintenance strategy. Understanding where such issues exist can enable maintenance interventions that ensure catastrophic failures are averted and costs minimised. Information on substructure status can therefore enable the planning of tamping and other maintenance operations. Special attention should be given to measure the deflection at locations where critical components are installed to mitigate potential risk from large voids under sleepers.
In addition, monitoring the gradual increase in vertical displacement over time, e.g., as voids develop, can be critically important for predictive maintenance purposes. Mathematical algorithms can be used to process such data, giving rise to foreknowledge or ‘wisdom’ on where and when potential issues will occur if not addressed in a timely manner.
Figure 1: Deflection measurements in loaded and unloaded train conditions
Ensuring rail safety
A well-maintained track bed can be ensured through early maintenance intervention, for example, through tamping or lift and pack methods. By understanding the level of track deflection at the location of critical infrastructure, it is possible to identify where maintenance is required and ensure that assets are not subject to unnecessary degradation due to inadequate maintenance.
End-to-end IIoT solutions such as SWiX enable optimal planning of maintenance operations, such that predetermined interventions become possible. This will enable infrastructure and maintenance staff to understand the type of activity required and avoid under maintained scenarios as well as unnecessary and costly over maintained ones.
In addition to understanding when maintenance should be planned for specific assets, preventative maintenance can ensure that substructure under critical assets requiring urgent attention can be immediately actioned to avert failures, including catastrophic derailment.
When large data sets are collected over a protracted period, useful insights can be gained to guide maintenance strategy and inform decisions, particularly when maintenance log and other information is examined alongside SWiX generated data. This enables critical rail operations to be transformed.
For more information, please contact me on firstname.lastname@example.org. I'd love to hear your views on this blog.