HERZOG presents a new sensor-based approach for monitoring the mechanical condition of the HP-SST screening tower. The solution ensures reproducible screening results, enables early fault detection, and increases process reliability in automated laboratories.
Particle size analysis plays a crucial role in iron ore processing, as it directly influences process control, plant performance, and product quality. The HP-SST screening tower has established itself as an industry standard worldwide because it enables compliant screening according to ISO 3082 and ISO 4701 and can be seamlessly integrated into automated laboratory environments. However, a stable, three-dimensional vibration pattern is essential for high screening efficiency. Even slight mechanical changes such as loosened base screws, wear on the eccentric, misalignment, or imbalance can affect the motion behavior and thus reduce the accuracy of the sieve analysis.
To make these changes visible at an early stage, HERZOG has developed a sensor-based tool condition monitoring system. A 3D acceleration sensor, mounted directly on the vibration frame of the HP-SST, records kinematic motion data during short idle runs under real operating conditions. The data is logged via the PLC and analyzed in PrepMaster Analytics. This results in characteristic acceleration patterns that precisely describe the machine’s behavior in its normal state. Even slight deviations from the reference pattern such as those simulated by insufficient floor anchoring show clear changes in both x/y rotation and x/z vibration. These additional vertical and lateral movements directly affect the throw parabola of the particles, the loosening of the material bed, and the separation efficiency. As a result, the likelihood that borderline particles reach the sieve openings decreases, while the risk of clogged apertures increases, and finer fractions may be underestimated.
By integrating the system into PrepMaster Analytics, a single measurement becomes a powerful condition-monitoring tool. The system automatically performs regular idle runs, evaluates sensor patterns using statistical process control, and can optionally be enhanced with AI models trained to detect typical anomalies. This makes it possible to identify mechanical deviations before they lead to measurement errors, process instabilities, or unplanned downtimes. The result is higher operational safety, reduced measurement uncertainty, and consistently high quality in particle size analysis in high-throughput laboratories.
Read Application Note 69 now to learn how sensor-based condition monitoring and intelligent analytics can elevate your automation to the next level.
