Predictive Maintenance: Maintenance Before the Filling Line Shuts Down

Scheduled maintenance has two possible outcomes, neither of which is ideal: Either a component is replaced that could have lasted for weeks or months—a waste of materials, downtime, and labor. Or the component fails before the next scheduled maintenance date—and the unplanned downtime occurs precisely as the maintenance strategy was designed to prevent.

Predictive maintenance resolves this dilemma by making the component’s actual wear condition the trigger for maintenance—not the calendar. In bulk material bagging systems, sensors on vacuum pumps, dosing units, and sealing units provide the data: vibration patterns, pressure curves, temperatures, and torques that reveal wear before it leads to failure.  

This article demonstrates how predictive maintenance is implemented in bulk material filling systems—as the technological operationalization of TPM for every maintenance strategy.

There are three fundamentally different strategies in maintenance—and each comes at a cost:

Agustiady and Cudney position predictive maintenance within the maintenance workflow between scheduled maintenance and condition monitoring: Scheduled maintenance defines the basic framework (which components are monitored), predictive maintenance optimizes the timing (when maintenance is performed), and condition monitoring provides the data (how the condition is measured).

For a bagging system, this means in practice: Preventive maintenance remains the basic framework—oil changes, general cleaning, and safety inspections continue to follow fixed intervals. However, the components subject to high wear—screw conveyor, vacuum pump, sonotrode, load cells—are additionally monitored by sensors. Their maintenance schedule is based on the measured condition, not on the calendar.

Every critical component of a bagging system generates signals during operation that reveal its wear condition—if you measure them:

The fourth column—typical lead time before failure—is the key factor for maintenance planning: It shows how much time elapses between the early warning signal and the actual failure. A two- to six-week lead time for the vacuum pump means there is enough time to order the replacement part, schedule the maintenance appointment during a low-production phase, and prepare for the replacement—rather than paying for express shipping and interrupting the shift in an emergency.

Raw data alone does not trigger maintenance. The path from sensor reading to maintenance decision follows a four-step process:

Data acquisition: Sensors measure continuously—vibration, temperature, pressure, torque, current consumption. In networked filling systems serving as a data foundation in the spirit of Industry 4.0, these values flow into a central system that maps the plant status in real time.

Trend analysis: It is not the individual measured value that is decisive, but its change over time. A vacuum pump with a vibration amplitude of 4.2 mm/s is unremarkable. The same pump, which was at 3.1 mm/s four weeks ago and has been rising steadily since then, shows a trend—and trends are the language in which wear and tear announces itself.

Threshold comparison: The trend is checked against defined limits. In simple systems, these are fixed thresholds: Vibration above 6 mm/s → maintenance alert. In advanced systems, machine learning models calculate the probability of failure based on the entire measurement history—not just the current value, but its trend, the environmental conditions, and the processed product.

Maintenance notification: The system generates a prioritized recommendation: which component, what degree of wear, and when action should be taken. The maintenance technician decides whether to act immediately or whether the next scheduled maintenance window is sufficient. Sensors for condition monitoring provide the data foundation—the technical article on condition monitoring will delve into which sensor types can measure which wear mechanisms in bagging systems.

The economic benefits of predictive maintenance stem from three interrelated factors:

First: Reduction of unplanned downtime. Every hour of unplanned downtime avoided on a bagging line with a rated capacity of 300 bags per hour corresponds to 300 bags that would otherwise not have been produced. With a product value of 500 euros per ton and 25 kilograms per bag, that amounts to 3,750 euros in saved contribution margin—per hour, per event. The availability of the bagging line as an OEE factor increases directly because unplanned downtime minutes are converted into planned maintenance minutes, which are shorter and more predictable.

Second: Optimization of spare parts consumption. Preventive maintenance replaces parts at set intervals—regardless of whether the component is worn out or not. Predictive maintenance replaces parts based on condition, thereby utilizing the full remaining service life of each component. For a sonotrode that is replaced preventively after 50,000 cycles but only reaches the critical wear limit after 68,000 cycles, this saves 18,000 cycles of service life—a significant cost savings when multiplied across all wear parts in a system.

Third: Remote diagnosis for rapid initial assessment. When sensor data is transmitted to the machine manufacturer via a secure connection, the service technician can assess the system’s condition remotely before traveling to the site. This shortens response times, prevents misdiagnoses, and reduces mean time to repair. Remote service is not a substitute for on-site maintenance, but rather its intelligent preparation: The technician arrives with the right spare part, the right tool, and the right diagnostic picture—instead of having to start troubleshooting on-site.

Together, these three effects lower maintenance costs as a TCO lever over the entire lifecycle of the system—not through less maintenance, but through better maintenance: at the right time, on the right part, with the right effort.

Predictive maintenance for bagging systems is not a vision of the future, but rather available technology. The sensors exist, data transmission is handled, and the algorithms for trend analysis and threshold monitoring have been proven.  

What determines success is not the technology itself, but the combination of sensor data with knowledge of the specific wear mechanisms of a bagging system: Anyone who knows that increasing vibration at the vacuum pump indicates bearing wear and that decreasing torque at the dosing unit points to screw abrasion can take action before the system comes to a standstill. The systematic optimization of filling and bagging in production begins where data meets experience.

Sources

Agustiady, Tina Kanti / Cudney, Elizabeth A.: Total Productive Maintenance: Strategies and Implementation Guide. CRC Press, Boca Raton 2016.

Kletti, Jürgen / Schumacher, Jochen: Die perfekte Produktion. 2. Auflage, Springer Vieweg 2014.