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Electrification is expected to reduce the need for maintenance. However, modelling and simulation are needed, as well as more information on field experience to avoid pitfalls and understand the benefits throughout the value chain.
The value chains in modern society involve mobile work machines. Such value chains include, for instance, mining and construction, forestry, material handling, and port operations. These value chains also face the global demands for sustainability, and machinery suppliers are challenged to contribute to their customers´ efforts to decarbonize their operations. Replacing fossil-fuel-based power sources with electrical ones seems to offer a solution for this. Further solutions are provided by digital technology and sensors that will enable automatic and autonomous functions and change work procedures and value creation mechanisms.

Light vehicles like cars are the forerunners of electricity in mobility. Similar development is also expected to take place for mobile machines (MM). However, MMs can be configured for several purposes during the work cycle, and it carries a tool or an end effector which requires power at the same or even higher level than the drivetrain. MM is often a part of a work process, and its operation has to be adjusted to the workflow. An interesting question then is how to align the recharging and maintenance needs of the machinery with the operational and performance criteria.

Electrification is also a key enabler in the transformation towards increasingly automated and eventually autonomous machinery. In some machine types, electrified machines have already been commonplace for years, but the increasing capabilities of battery-electric machines enable increased flexibility of operations when compared to cable-supplied electric machines or previous machine generations. The increased flexibility offers new opportunities for optimization of operations and provides the basis for developing autonomous operations.

Finland aims to be the best place for developing mobile machines through efficient and effective collaboration between industry and research. The SIX cluster contributes to the achievement of company-specific and sector-wide ‘digital green’ goals. In our SIX cluster project the focus is in autonomous operations, electrification and in data-intensive lifecycle services.

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Figure: The roadmap towards efficient electrified and autonomous fleet maintenance.

Electric vehicles in general have fewer moving parts, do not need oil and filter changes and have regenerative braking systems that reduce the wear and tear components. Grosso et al. (2021) have estimated that the maintenance cost for electric vehicles is at least 30% lower than that of conventional vehicles. However, the literature also provides examples on increasing maintenance costs of novel machine types that are often more complex to operate and maintain compared to the previous machine generations (Hane Hagström, 2021). There is limited literature on how electrification and automation will transform the maintenance of MM fleets. For example, there are optimal charging profiles for electric vehicles, which could affect maintenance planning if the owner wants to maximize battery life. A swappable battery structure would reduce the downtime caused by recharging, but the same outcome might also be achievable with more efficient batteries and faster recharging solutions.

In addition to electrification, the development towards higher levels of automation changes the practices for how the machines will be maintained. Access of maintenance workers to areas where automated machines operate may be restricted, making it important to predictively optimize maintenance work to avoid disturbances to the operations. An increasing level of autonomy also requires the use of technology that enables sufficient situational awareness, including sensors, data processing and connectivity - all of which need to be considered in maintenance planning. These new technologies will also require new skills from the maintenance personnel. System level management of maintenance activities is needed to ensure that not only the machines but also the supporting infrastructure perform reliably.

Electrification and automation will also impact the maintenance needs of MMs, and novel maintenance strategies have to be developed. The close collaboration with the partners in the SIX cluster offered the possibility to discuss the industrial state of the art as well as future needs and common visions of participants. Participatory methods like World Café rely on collaborative action and one of its strengths is its creation of an opportunity to share lived experiences and to create dialogue. Due to the COVID-19 restrictions, remote collaboration tools were applied in our World Café workshop. In the workshop, views on life cycle service business, availability performance, scheduling of machine operation and traffic, and IT architecture to support the operation in the field were collected. The outcome was summarized in a roadmap (Figure 1).

Some companies have already electrified their machines and implemented swappable battery technology in their MMs. Electrification introduces novel needs for real-time data collection (e.g. status and health of the battery) and for modelling and forecasting (e.g. lifetime expectation of the batteries). Autonomous operations call for real-time location data and information on the environment, surroundings, and safety. Electrification is expected to reduce maintenance: the components are expected to be service free, but this expectation needs to be proven in the field. However, more information is needed to understand the failure modes and ageing behaviour.

Autonomous operations also require efficient information sharing between different actors, e.g. in port terminal operations, and collaborative scheduling between the organisations responsible for operations, services and maintenance. In some cases, it could also be possible to carry out minor maintenance or service tasks during the recharging. The benefits arise on the system level - and success is possible only if the issues dealing with the data ownership can be solved.

An autonomous mobile work machine needs an automated MRO-system (maintenance, repair, and operations) which will enable MM fleet to be managed automatically or autonomously and to be optimized to production process demands. This will include consumable and spare and wear part usage prediction and fleet maintenance, maintenance timing based on machine condition and production planning.

Text: Eetu Heikkilä, Helena Kortelainen and Jesse Tervo, VTT Technical Research Centre of Finland Ltd. ,
Matti Vilkko, Jouko Laitinen and Kari T. Koskinen, Tampere University
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