In the era of the IIoT, edge computing optimizes mobile assets operations

For many business sectors, maintaining mobile assets in operational condition (vehicles, machinery, rolling stock…) is extremely critical.

Thought, it is almost impossible to know precisely, in real time and on continuity, the health and the reality of the operating conditions of these equipment’s.

To extend these assets life cycle and guarantee their integrity, it is necessary to be able to define their operating conditions during their journeys or operations.

Yet, these mobile assets natively integrate multiple devices, components and systems, which complicates the installation and the deployment of data collection and monitoring tools. Indeed, the proper functioning of these assets requires the lowest degree of intrusion from the monitoring systems. Also, often the physical access to the data collection point can be difficult. Besides, to be valuable, the data capture should take place throughout the duration of the journeys or operation phases.

Still, continuous monitoring and real-time failures detection are the sinequanone conditions to achieve real operational agility and promote the greatest continuity of operation possible.

Indeed, it is the understanding of the type of events that can impact vehicles during their operations / journeys that will allow genuine proactive maintenance. The ability to characterize running conditions, identify impacting events and locate them, is a key element in bringing useful information to maintenance operations. As a bonus, geolocating the mobile asset during the event occurrence can trigger interventions at the right time and in the right place.

A Better knowledge of running conditions

These requirements impose a monitoring of the health and usage of the mobile asset, on-board. The objective is to collect the data closest to the critical elements and to monitor the parameters (acceleration, pitch, roll …) which will allow:

  •  calculating in real time indicators to warn about possible anomalies,
  •  detecting and locating failures to trigger proactive actions at the right time,
  •  characterizing the driving conditions to ensure continuous monitoring over the operating period.

This information is crucial to gain agility to prevent asset damage, avoid downtime and additional costs of corrective maintenance. In the case of passenger transport, it helps also to ensure the occupants comfort and safety.

At the same time, data from continuous and long-term monitoring should make it possible to carry out several types of in-depth analyzes concerning the variety of grounds typology covered. In the case of fleet management for example, this precise knowledge of the health of each vehicle is a core element that informs the decisions related to the fleet use and maintenance.

Indeed, to understand the degree of stress of a vehicle, it is necessary to know the running time spent at different levels. Depending on the driving time spent on a more or less regular pavement, a more or less severe course and a more or less important level of gradient, it will then be possible to define at a T time, the level of stress overwent by each vehicle and better order the vehicles between them according to the journeys or operations to be realized.

Furthermore, in the case of passenger transport, the continuous and long-term monitoring of vibration, shock, acceleration, speed, noise and temperature generates the information to characterize the actual conditions of transport to evaluate and improve the service.

Take up all the challenges

Today, such continuous monitoring of operating conditions, coupled with real-time monitoring of equipment health and usage, is possible.

In the IIoT era, thanks to microelectronics capabilities, combined with wireless technologies and power management progress it is possible to reach previously inaccessible data collection points. Many parameters are collected as close to the need, to be processed in real time, on site to directly provide the useful information by avoiding the hypothetical availability of access to a cloud.

We are talking about Appliances or wireless low-power systems of Edge computing, in other words, about computing and processing “at the edge of the network”. Of optimized size, installed in retrofit on the mobile or rolling asset, closer to the element to monitor, this type of Appliance has a low power management allowing a run over the total operating period, a specific operation or a defined number of vehicle paths.

Thanks to a strong computing power and advanced algorithms, the processing (correlation, aggregation, fusion …) of data performed by the appliance in situ is used to:

  • Trigger on conditions the automatic actions of third-party equipment (sending SMS, taking pictures …),
  • Continually feed the health and usage monitoring,
  • Complete models of preventive maintenance actions programs,
  • Enriches the study of the impact of the real operational conditions on vehicles.

A better management of data, continuously

The power of computing is deported on the field, “at the edge of the network” and these Appliances are so powerful that they can process the data of their internal sensors in addition to those of other connected sensors.

The large amount of data recorded during the journeys are pre-processed and formatted to be transmitted continuously to a platform hosted in the cloud, for storage, further in-depth analysis and the generation of dashboards.

In a shorter loop of interaction with local devices, when the Edge Computing Appliance detects an event exceeding the threshold limit, the information is used to immediately trigger the consequent operation.

All captured data is processed on-board, the more critical information is immediately used by local equipment without delay or latency, and the rest of the information is intended for cloud computing a posteriori for in-depth analysis. Data processing and traffic are organized to be as efficient as possible in terms of bandwidth and energy consumption.

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