Anomaly detection in a cost-effective conveyor belt testing system

The industrial manufacturing sector faces significant challenges regarding energy efficiency and operational sustainability, particularly in the management of motor driven systems such as conveyor belts. While traditional diagnostic methods effectively identify faults, a critical gap remains in closing the control loop to enable autonomous corrective actions, specifically for mechanical tension regulation y conveyor belts. This research addresses this limitation by designing, implementing, and validating a low-cost, small-scale prototype capable of soft real-time energy monitoring, anomaly detection, and automatic physical correction. The system utilizes an ESP32 microcontroller and an INA219 sensor to analyze voltage and current signals, employing unsupervised machine learning algorithms such as Isolation Forest (IF) and One-Class Support Vector Machine (OC-SVM). This serves to diagnose tension states defined by specific deflection thresholds. To execute the physical corrections, the platform integrates a custom built rack and pinion mechanism driven by a servomotor, which automatically regulates the belt tension. Methodologically, the study characterized optimal operational conditions at a 30% PWM duty cycle and applied a median filter that reduced signal variability. Four models were trained using exclusively optimal-state data under univariate and multivariate configurations. Experimental results demonstrate that electrical parameters serve as reliable indicators of mechanical tension in a small scale conveyor belt. The Univariate One-Class SVM model applied to voltage yielded the highest performance, achieving an F1-Score of 0.8624. Meanwhile, the Multivariate OC-SVM model demonstrated high reliability with a Precision of 90.51% and a Recall of 72.95%. Upon detection of anomalies (slippage or excessive tightness), the system successfully triggered autonomous adjustments via a rack and pinion mechanism. These findings validate the feasibility of using accessible monitoring to implement intelligent, self-correcting maintenance systems, offering a scalable solution to minimize downtime and energy waste in industrial environments.

https://orcid.org/0000-0001-6632-8576