CHARGEMENT

IIoT and edge computing technologies serving the public transport offering

The density of cities is increasing, the pace of life is accelerating, fossil fuels are becoming scarce and the climate transition is under way… These are serious challenges for urban mobility! In this context, guided public transport is more than ever a lever for the future. It has the potential to meet the challenges of the city of tomorrow and the economic, practical and ecological expectations of citizens. New modes, such as cable transportation, are appearing in urban uses to complete a public transport offer adapted to the challenges of the city.


On the other hand, the current networks of regional trains, trams, metro, trolleys are subject to degradation or wear likely to deteriorate the reliability and the quality of the service. This raises the question of the maintenance of aging rolling stock but also modern equipment. This is to anticipate for less / better repair, extend the operating time, eliminate unnecessary interventions impacting the operation and users to meet the best level of service expected.


Interface equipment between infrastructure and rolling stock is critical for the smooth running of the operation. Moreover, the consequences of malfunctions are multifaceted. They are legal when accidents, damages or deterioration are caused to third parties (legal entities or individuals), financial when it comes to damage management or expensive corrective maintenance interventions. It is also for the operator a risk of deterioration of image with a negative perception of users and a tenser relationship with the organizing authority. The IIoT has a role to play in reducing the impact of malfunctions, both at the level of operations and maintenance: efficiency, continuity of service and at least limited interruption to preserve the quality of service provided to the user.


Monitor critical organs most difficult to access

It is the use of “the right data” which will allow a fine analysis of the conditions of occurrence of defaults or malfunctions and will allow to reach the appropriate level of maintenance.

For railway systems such as trains, train-tram, Metro and tram, bogies are the components used to guide, support the load, allow traction and braking, ensure comfort and capture track signals. It is an organ with a considerable safety impact.

For urban gondolas, detachable or fixed attachments and the roller batteries are difficult to access ultra-critical elements.
Similarly, the pantograph, articulated device that allows an electric locomotive, a tram or a trolley to capture the current by friction on all the carrier cables and overhead conductors is a key element. During the movements, because of shocks and vibrations intrinsic to their use, these different organs are subjected to strong repeated stress.

Capturing data to monitor these vibratory phenomena on these critical organs is not easy: the measurement points for the collection of the targeted data are in a constrained zone and without easy access to a power supply. However, these data are crucial to be able to characterize the phenomena, to detect the anomalies (which are on the side of the infrastructure or the rolling stock) and to inform the decisions in operations and maintenance cycle.

This is where the wireless and low power “Edge Computing” provides a powerful and adapted response. Thanks to wireless technologies, the miniaturization of the components they integrate and their low energy consumption, Edge Computing devices allow easy retrofitted installation and offer a suitable autonomy. Data acquisition and processing can be done in situ, as close as possible to the critical components of the rolling stock.


Deliver only relevant and usable information

Edge Computing is the process by which computing capacity is placed closer to objects, extracting the value of sensor data in real time, and transmitting only useful information to the cloud. The precious data (GPS position, speed, accelerations, vibrations, shocks …) from the sensor network are processed by the Edge Computing appliance in situ to:

  • geolocate and characterize extraordinary events (degradations, incidents and anomalies),
  • conditionally trigger the automatic actions of local equipment,
  • feed the predictive maintenance models.

At the same time, the continuous or periodic power supply of the information systems smartly managed by the Edge Computing appliance minimizes data traffic, enables smart data to be informed about the operating and maintenance processes in order to:

  •  Plan predictive maintenance as accurately as possible (at the right time and in the right place),
  • Increase the relevance and operational agility,
  • Promote continuity of service.

Edge Computing therefore offers a new approach to operation and maintenance, based on embedded data processing to provide the necessary efficiency and responsiveness.

The information generated, such as the vibration level indicators, the clamping force, the balancing … resulting from the real-time and short-time processing of the raw data measured by sensors, makes it possible to quickly detect threshold exceedances or abnormal behaviours to trigger urgent actions that will ensure a reliable operating condition over time.

The goal is to deliver useful and targeted information to the technical centres. This way, the continuous supply of useful data allows a better allocation of resources, the reduction of costs due to the elimination of unnecessary interventions and the extension of the remaining useful life of the monitored equipment.

Thus, edge computing simultaneously contributes to superior operational agility and optimized predictive maintenance.


Improve level of service and end-customer experience

Edge computing proposes a new paradigm based on the continuous construction of a better understanding of the reality of operation.

By correlating the characterized and geolocated events with the health status data of the infrastructure, it is possible to determine the causes of the failures, anticipate the breakdowns and optimize the maintenance process to limit the rolling stocks immobilization and ensure the assets availability and reliability as well as the fluidity of journeys.

Ultimately, the features of Edge Computing appliances enriches the devices connectivity of rolling stock to build an unpublished knowledge of the reality of operation.

This computational force, on the field, is the guarantee of a better understanding of the running conditions and enables as well to take action on the performance of the operations as on the improvement of the comfort during the journeys.

Furthermore, if the definition of comfort is complex and subjective, it is legitimate to affirm that the driving comfort of a vehicle, that is to say the shocks, accelerations and oscillatory movement felt by the travellers as well as the acoustic comfort (emission of sound vibrations audible by man) are an integral part of the general concept of comfort.

The Edge Computing Appliance allows to measure, and continuously check over all paths that frequencies and amplitudes do not exceed the limits of materials, to help identify sources of vibration, to increase the knowledge of rolling conditions to build or feed computer models.

Today with Edge computing, IIoT sees its potential strongly increased for more efficient operations than ever before and a punctual, reliable and sustainable public transport service.

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