Crusher Bridge Detection

Application

Blockages in crusher bowls can be a significant source of unscheduled downtime. Detecting their position and extent can support truck-tipping decisions, primary feedrate changes and rock-breaking workflows.

The solution can use standard CCTV or purpose-built machine-vision cameras and combine visual data with additional inputs such as depth sensors or LiDAR. It can run on industrial edge hardware and publish blockage status to approved operator and control interfaces. Traffic-light control, rock-breaking and feeder responses require a separate site integration, validation and safety case.

Bridge Detection Features

Our camera-based bridge detection systems over crushers offer several advantages over radar-based systems. First and foremost, camera systems can provide high-resolution, real-time visual feedback, enabling more precise characterisation of material properties and behaviour. This is particularly useful for detecting the early stages of bridging, where granularity and texture can be important indicators. Additionally, modern computer-vision algorithms can be trained to recognise various types of anomalies in the ore flow, providing more comprehensive monitoring capabilities.

In contrast, radar-based systems may struggle to differentiate between normal ore levels and potential bridging incidents, especially when the material characteristics are heterogeneous. They can also be affected by environmental conditions such as dust, humidity and temperature, requiring frequent calibration for optimal performance. Moreover, radar systems only provide distance or depth information, which may not be sufficient to analyse the nature or cause of a potential bridging scenario.

Camera-based systems can provide visual evidence for data-driven analysis and real-time decision support. Their usefulness depends on camera placement, lighting, dust, training data and the response that the site can safely execute.

Technology

By training a machine-learning model on labelled images of typical and atypical ore conditions, Minealytics can achieve a high level of granularity in real-time monitoring. For example, the algorithm can be trained to recognise and differentiate between ore, empty space, and potential bridges. When the system detects a bridge forming, it can alert operators to take corrective action immediately, thus preventing potential jams, equipment damage, or inefficiencies.

Semantic segmentation can support real-time bridge detection and, when combined with time-series process data, may support predictive analysis. The prediction horizon, event definition and response are validated against the site data before they are used operationally.