ASSIST-IoT PILOTS

Pilot sites for demonstration, evaluation and assessment activities

Action will assess and verify its results in three pilots, representing: (i) port automation, (ii) smart safety of workers and (iii) cohesive vehicle monitoring and diagnostics. Each pilot will include different scenarios, in which different technological pillars and enablers will be executed and validated, some preliminary KPIs are provided from the perspective of the stakeholders involved. Moreover, experiences from pilots will be used to improve action outcomes (feedback-loop), guaranteeing quality and broad range applicability of results.

Pilot 1: Port automation Pilot

Pilot will be driven by the industrial partner and port terminal operator Terminal Link Group (TL) and will be deployed on its premises – in the Malta Freeport terminal (MFLT).

Scenario 1: Automated alignment of CHE:

The objective pursued by this scenario will be to enhance the operational performance by the automation of complex tasks (e.g. alignment of CHE or optimal twist-locks operation) and to demonstrate the feasibility of technologies like Optical Character Reader (OCR) and QR codes in order to create and release a low cost MVP (Minimum Viable Product) whose availability will help container terminals to increase the throughput volume and reduce human errors.

Scenario 2: Yard fleet assets location:

A combination of real-time telemetry from fleet assets, located in the yard, QR codes and image-based positioning, will be used to automatically report containers location to operators and reduce inventory errors that cost, each shipping line, several million euros per year.

Scenario 3: Augmented Reality and Tactile Internet HMIs for fleet yard drivers:

The aim of this scenario is to boost the digital reality paradigm through the application of AR, and immersive spatial technologies to redefine how the staff in the terminal interacts with the heavy machinery.The pilot will leverage all data obtained from the crane, to inform the machine driver with context information, e.g. (a) container data (net load, type, ID, etc.), (b) working instructions (initial and final slots, optimal stacking order), and (c) if there are error/misalignments in the current/performed operations.

Scenario 4: Remote control of CHE:

This scenario includes the completely remote operation of a CHE. Relying on the architecture of the ASSIST-IoT project, this scenario will empower the crane operators with all the extended capabilities developed in the pilot to effectively control and drive a crane from a control room. Remote operation of equipment will be done in a controlled environment, without interacting with other workers in the yard, with all due security measures.

Pilot 2: Smart Safety of workers Pilot

This pilot will be driven by the industrial partner Mostostal Warszawa S.A. (MOW) and will be deployed on its premises.It will take place at the construction site of Marshal’s Office in Szczecin (Poland).The Smart safety of workers with NGIoT solutions pilot will present the benefits of the ASSIST-IoT approach increasing OSH at the dynamic environment of a busy construction site.

Scenario 1: Optimization of safety and health plan with AR support:

Safety at the construction site can be increased by additional control of access to restricted zones, ensuring that only workers with relevant permissions, and valid safety trainings have access to dangerous locations. Each person should be identifiable, to prevent entrance of unauthorised persons to critical zones, and to limit access of suppliers/sellers.

Scenario 2: Smart actuation of intelligent IoT devices with an adjustment to individual needs:

Its aim is to demonstrate use of smart IoT devices (sensors and actuators) functioning together in a closed feedback loop of an OSH risk management process. The sensory part of the system will provide streams of real-time measurements of key workers’ health parameters (heart rate, galvanic skin response, skin temperature and acceleration) performed by personal health trackers, and environmental factors (e.g. ambient temperature or UV radiation) measured by sensors embedded into protective clothing or a helmet.

Scenario 3: Identification of suspicious and undesirable behaviours within the construction site:

The aim of this scenario is to move from traditional static and rule-based risk assessment approaches towards highly dynamic and real-time prediction of hazards and risks. Hence, identification of undesirable behaviours within construction site will be provided with use of DLT. All hazardous events’, e.g. fall from a height, impact on safety helmet, non-authorised entrance, etc., will be recorded, and including information provided by intelligent IoT devices. DLT-based system will ensure that data is not corrupted and data source is trustable; is not being supplanted.

Pilot 3: Cohesive vehicle monitoring and diagnostics Pilot

This pilot is driven by the industrial partner FORD, that is a leader Original Equipment Manufacturers (OEMs) of passenger cars, with over a 5 million vehicles produced per year. FORD will provide a state-of the-art hybrid electrical vehicle with an open access Electronic Control Unit (ECU) which will be integrated into ASSIST-IoT reference architecture, thus allowing remote access to powertrain parameters and over-the-air update of diagnostics firmware. The Cohesive vehicle monitoring and diagnostics pilot will demonstrate the benefits of the ASSIST-IoT approach for the case of vehicle fleet diagnostics, where inputs coming from different sources are combined for providing an incremental and cohesive evaluation of the vehicle condition.

Scenario 1: Advanced powertrain monitoring and diagnostics: 

Scenario includes vehicle monitoring by the OEM and its repair department for in-service conformity (ISC) verification, which regulate emission footprint of the vehicle along its use phase. Expected stricter extension of ISC regulation is a major problem for OEMs since it may force costly recalls of vehicles. Here, ASSIST-IoT will be profited for selective monitoring of powertrain parts and vehicle units, allowing when possible to replace series recalls by partial recalls or remote ECU recalibration. In particular, this scenario will be focused on identifying driving scenarios or units that are not fulfilling ISC requirements.

Scenario 2: Vehicle condition monitoring:

Scenario deals with vehicle inspection using TwoTronic’s vehicle scanning solution as illustrated on the left, where daily multiple vehicles (i.e. 40-50 vehicles) visit a repair shop for typical maintenance services. Aim of the scenario is timely identification of mechanical malfunctions and monitoring of vehicle’s aesthetic condition, in order to record deterioration of external body, facilitate driver/insurance liability, and schedule maintenance and repair interventions.