Scrapers are essential assets for conveyor belts and, as their name suggests, are responsible for cleaning belts by scraping off dirt. They are usually classified as primary, when they are located at the end of the belt, secondary and tertiary, depending on their location. Proper maintenance is directly linked to the good performance of other machine components and consequently to production.
In this success case, the relationship between vibration levels and scraper failure modes was identified. Among the main failure modes of interest are wear and loss of tension of scraper blades on conveyor belts.
This identification process was carried out using Dynamox‘s maintenance solutions. Wireless sensors, vibration analysis data and sensing data were used, as well as the logic rule for automated alerts.
Check out how each solution worked in the project:
HF+ model sensors were used to monitor the scrapers, one on each side of the scraper. The use of two sensors is important to check for differences in rigidity on the two sides of the scraper, which directly affects vibration levels.
The sensors were glued and configured as follows: triaxial collection, telemetry collection every minute and spectral collection once a day, maximum frequency of 3200 Hz with 8192 resolution lines.
Combination of vibration and sensitive inspection data (DYNASENS)
DYNASENS is DYNAMOX’s service that makes it easier for technicians to inspect assets in the field. With this service, it is possible to establish routes and fill in monitoring checklists. In addition to its own app, this information can be integrated into the DYNAPREDICT platform.
In the case of scrapers, during inspections with DYNASENS, technicians fill in checklists stating the thickness value and indicating whether the blade has been changed or adjusted, as well as stating the criticality level of the asset when necessary.
VIBRATION & SENSING
In order to evaluate the scrapers and validate the study, it was important to join forces between the vibration signals and the information from DYNASENS. In the example on the right, the reported thickness values are cross-referenced with the vibration data. In this case, there was also a blade change, represented by the green dotted line.
You can see that, in general, lower vibration levels represent lower blade thickness. Unlike what one is used to, higher vibration levels indicate better blade conditions and, therefore, better scraping conditions.
Each scraper can work with different loads, which significantly alters the level of vibration, however, the trend behavior of the signal is maintained. For this reason, it is interesting to use standardized metrics and present the condition of the asset by criticality range. In addition, the display of the curves in the three signal directions (vertical, horizontal, and axial) allows the analyst to understand the source of the behaviors presented in more detail.
The conclusion of this study was that it would be possible to optimize scraper changes and better understand when and which scraper needs more immediate attention. It is estimated that the blades of a secondary scraper reach their half-life in 25 – 30 days and are then replaced. By monitoring the behavior of vibration signals, this replacement interval can be extended to 40 – 45 days.