When Every Minute Counts: Predictive Maintenance in Modern Agriculture

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At Systems & Components, currently taking place as part of Agritechnica in Hanover, visitors can learn about new technologies for mobile work machines. A central theme is smart sensors and AI-powered maintenance predictions for agricultural machinery – because during harvest and sowing, a machine failure can become a threat to existence.

Unlike in industry, where production lines often run around the clock and maintenance windows are predictable, agriculture is subject to the unpredictable whims of nature. When the weather is right and the harvest is ripe, often a few days or even hours decide the success of an entire season. A combine harvester that fails in the middle of the grain harvest, or a tractor that breaks down during time-critical sowing, can quickly become an existential threat to farmers. Therefore, it is all the more important not only to repair machine failures, but to prevent them in advance.

This reality makes predictive maintenance – the foresightful maintenance based on sensor data and AI analyses – a key technology for modern agriculture. While in other industries the focus is on optimizing maintenance expenses, here it is about much more: securing the food supply and the economic existence of the farms.

How Predictive Maintenance Works

The basic principle is based on continuous monitoring of machine conditions by sensors and intelligent evaluation of the data obtained. First, normal values are defined that characterize fault-free operation. Sensors then continuously capture parameters such as vibrations, temperatures, pressures, or torques on critical components.

Machine learning algorithms process these data streams, compare them with empirical values, and recognize patterns that indicate upcoming maintenance needs. They learn from deviations and identify wear and tear early on – long before failures occur.

Sensors in continuous use under extreme conditions

Agricultural machinery is exposed to extreme conditions: dust, moisture, large temperature fluctuations, and permanent vibrations put a strain on the technology. During the harvest season, the machines often have to run for 16 hours a day – downtimes are hardly tolerable.

Modern systems therefore use various intelligent sensors. Vibration sensors on gearboxes and bearings detect the smallest irregularities, temperature sensors monitor engines and hydraulic systems, and pressure sensors detect leaks or clogged filters.

Caterpillar provided an example last year with sensor-supported track drives. The system recommends a first inspection at 40% wear and replacement at 100% wear. According to Andreas Kurz, Senior Product Manager Tracks at Zeppelin, states that “currently […] 64 Cat machines are already in use in Germany with this system. One sensor has already triggered, thus preventing a machine failure and a repair.”

AI Learns the Peculiarities of Each Machine

The real revolution lies not in the sensors themselves, but in the data-driven evaluation. Algorithms recognize individual wear patterns – a combine harvester on stony ground behaves differently than one on loamy soil.

The goal is to predict the time at which a failure is likely based on current and historical data. In addition to sensor data, factors such as operating hours, weather conditions, or driving behavior are included. The longer a system is in operation, the more precise the predictions become. Even a slightly elevated vibration value combined with a minimally increasing temperature can trigger an alarm weeks before a failure.

Digital Twins and Networking

According to DLG Brand Manager Petra Kaiser, digital twins are considered one of the most important innovations in the off-highway sector. As virtual simulations of real machines, they also enable the analysis of failure scenarios for previously unobserved cases.

Machines networked via telematics can be integrated into service ecosystems. Workshops can be automatically alerted and spare parts ordered even before the farmer notices the problem.

“Recognizing malfunctions and defects in advance and taking timely maintenance measures – these are decisive factors for efficient work, not only for agricultural machinery manufacturers, contractors, and farmers,” says Kaiser.

Development is moving towards greater networking and automation. In the future, systems will be able to coordinate entire fleets of machines. If the system detects a maintenance need, a replacement machine can be automatically scheduled.

Integration with smart farming data will also increase: weather forecasts, soil sensors, and satellite information can be incorporated into maintenance planning. If optimal harvesting conditions are indicated, the system could proactively issue maintenance recommendations.

The foundations were laid as early as a decade ago. At that time, SAP demonstrated using the example of the Los Angeles waterworks how technicians with suitable spare parts could be specifically dispatched through foresightful maintenance.

Success Story from Public Transport

The effectiveness of predictive maintenance is demonstrated by a current example from public transport. A transport company with over 10 million passengers per month has systematically introduced AI-powered maintenance predictions. A machine learning model continuously analyses diagnostic data from the vehicles, detects defects early, and automatically triggers information processes for passengers and dispatchers.

The architecture includes machine learning models, data pipelines, infrastructure for Machine Learning Operations (MLOps), and processes for monitoring and continuous training – concepts that can also be applied to agricultural machinery.

(vza)