Focus on predictive maintenance
Maximizing uptime, ensuring mobile machinery remains fully operational and available, is a key topic at the Systems & Components trade fair, this coming November in Hanover, Germany. The show takes place concurrently with Agritechnica. Condition monitoring, remote diagnostics and predictive maintenance are among the key topics at the trade fair. On display are solutions that […]
Maximizing uptime, ensuring mobile machinery remains fully operational and available, is a key topic at the Systems & Components trade fair, this coming November in Hanover, Germany. The show takes place concurrently with Agritechnica.
Condition monitoring, remote diagnostics and predictive maintenance are among the key topics at the trade fair. On display are solutions that use intelligent sensors and data analysis to draw attention to faults early, enabling more efficient maintenance planning and optimization of the use of spare part inventories. The B2B platform is organized by the DLG (German Agricultural Society).
Off-highway machines are indispensable for heavy-duty tasks in the field or on the construction site. The harsh operating conditions require components that can withstand the highest loads. But sooner or later the point comes when wear eventually sets in. The in-cab operator, however, may notice the signs of wear too late. Because vehicle damage can lead to costly repairs and prolonged downtime, effective prevention is essential. The classic ‘run-to-failure’ approach of reactive maintenance reaches its limits here. At Systems & Components, the focus will firmly be on prevention.
“Detecting faults early and taking prompt maintenance action are key to efficiency, not just for farmers, but also for contractors and machinery manufacturers,” says Petra Kaiser, Brand Manager for Systems & Components, DLG. Predictive maintenance, the combination of modern sensor technology and real-time data analytics, utilizes algorithms to predict potential failures based on current and historical data, helping to determine the optimal time for repairs on construction sites.
“Many of the companies exhibiting in Hanover are working intensively on solutions that leverage predictive maintenance strategies to identify mechanical, hydraulic and electronic faults before they lead to significant costs,” says Kaiser.
The total cost of ownership throughout a vehicle’s lifecycle is always a key consideration. Beyond customer benefits, the data collected through monitoring and diagnostic apps holds significant potential for engineering, sales, and service. For instance, machine designs can be optimized long before production using real load spectra and usage data.
Machine information in real time
Digitalization is a key driver in the development of predictive maintenance systems in the off-highway sector. Digital technologies make it possible to collect and analyze large amounts of information from various data sources along the entire drivetrain. Smart sensor technologies expand the available database, while high-performance telematics units with numerous interfaces can ensure secure data transmission in demanding application scenarios. The exhibitors at Systems & Components have taken on the challenges and are offering systems that promise maximum diagnostic reliability despite adverse conditions.
Harsh environments
Smart, integrated, and remote, these sensors are small tools with a big impact. In addition to their core function, measuring physical values, they also provide additional information. For example, algorithm-based sensors that deliver initial recommendations directly to the operator in the cab. These systems can operate autonomously. With these capabilities, maintenance needs can be predicted with high precision, making service planning easier and better aligned with inspection schedules.
Once the groundwork under the hood is done, that is, a future-proof Telematic Control Unit (TCU) has been selected, the sensors and software for efficient data collection and transmission have been implemented, and device management is all set, including remote diagnostics and monitoring, predictive maintenance can begin. The next step is to process and visualize the data. Even if condition monitoring already uses sensor data to supervise the condition of machines: This condition-based maintenance only becomes truly predictive, in the sense of predictive maintenance, when artificial intelligence comes into play.
Digital twins look further ahead
Systems & Components not only showcases the latest technologies, but also facilitates professional exchange and discussion of key future topics. Data-driven forecasting models of predictive maintenance are becoming increasingly important as also demonstrated by the expert presentations that will be held as part of the Expert Stage Systems & Components under the key theme of “Digital Services”. However, as these trends are extrapolated based on past events, there is a lack of data on failure scenarios that have not yet occurred.
The digital twin, a virtual replica of a mobile machine or its components, plays a pivotal role in predictive maintenance. By integrating real-time and historical data, it enables the simulation of potential failure scenarios, even those not previously encountered, allowing for proactive maintenance planning.
“The demands placed on mobile machinery in the ag industry are constantly increasing. Predictive maintenance makes it possible to achieve productivity increases that would otherwise be impossible,” Petra Kaiser concludes.
