Is it possible to obtain optimally performing machinery without the existing ones? Without a doubt, when talking about maintenance 4.0 in this context management could solve some of its main problems for Italian companies. Between limited budgets and constant technological advances, it is difficult to react to market needs quickly and efficiently.
Implementing maintenance actions, understood as a set of activities aimed at preserving a machine or plant over time, guarantee high levels of safety at work for the operators and excellent benefits in the quality of the final product.
From planned maintenance to predictive maintenance 4.0
Scheduled maintenance is now common practice in manufacturing and processing companies. Scheduling interventions have numerous advantages for controlling assets, in terms of facilitating internal factory organization, and the storage of spare machinery parts.
Knowing when the maintenance of a particular machine will be scheduled also allows you to not waste necessary resources, plan machine downtime and generally optimize production planning.
However, planned maintenance can (and must) be supplanted by predictive maintenance policies in order to meet the standards set by Industry 4.0.
As one sees in Figure 1, predictive maintenance, enabled by Industrial IoT technologies, allows you to reduce management costs, as well as to optimize machinery availability and performance in addition to product quality.
Figure 1. Example of a Maintenance strategy.
Further, when used correctly, Industrial IoT technologies allow real-time monitoring and greater usability of data needed to extract performance indicators, such as Overall Equipment Effectiveness (OEE) or Total Effective Equipment Performance (TEEP). This information is vital for those who intend to take advantage of the opportunities for digital transformation and Industry 4.0.
With this in mind, maintenance is increasingly becoming an essential part of production strategies and the corporate ecosystem. In this instance, we are talking about “data-driven maintenance”.
Is interest in the sector growing?
According to IoT Analytics (Fig 2), there is exponentially growing interest in the predictive maintenance sector, and it is exponential with a forecast of the CAGR (compound annual growth rate) of around 39% overall in 2024 compared to 2018. The trend is companies feel the need to innovate, and to find new ways of using Industry 4.0 for the optimization of their production processes.
Figure 2. 2018-2024 annual growth forecast.
IoT technologies for plant maintenance
With IoT systems, the installation of smart sensors integrated into the machinery allows easy data extraction, collection, and transmitting useful information for an overview of the machines’ operating status, with specific graphic displays that can facilitate the entire working process.
For example, monitoring temperature, noise levels, speeds and amplitudes of vibrations, oil levels, or electrical parameters of the engine makes it possible to detect and historicize the information for strategies related to both production and maintenance aspects.
Furthermore, data analysis allows us to predict how a machine will work and how there may be significant variations where one can plan maintenance activities in a proactive and ideally predictive way.
Among the many advantages coming out of IoT technologies, being able to effectively manage an asset management system for maintenance 4.0 actions is certainly one of the most interesting features for companies investing in the Industry 4.0 sector.
Industrial IoT technologies, in fact, make it possible to create the digital twin of each asset, with which to insert the exact digital replica, creating an interactive and searchable model that contains all the information and data of the hardware and software sides.
Access from a single dashboard and integration with other IT systems via APIs or high-level protocols also simplifies data sharing between team members and between the different departments involved in managing production, maintenance, energy, and also R&D and Digital Innovation.
Predictive maintenance must be seen in a broader context of Industry 4.0. Maintenance 4.0 not only means automated data collection of downtime and alarms, but it is necessary to create synergy and integration with different data collection and analysis systems, in order to exploit and optimize their potential in a cohesive scheme.
If you are interested in learning more about the applications of Zerynth’s IoT platform or the field of predictive maintenance, you can read about the experience of Vitesco’s (Continental group) which, thanks to the Industrial IoT solutions of Zerynth, is able to predict the malfunction of pneumatic valves used in a production line, 24 hours before the actual breakdown.