Shaft inspection is a critical component of underground mine safety and operational reliability. Conventional inspection methods rely heavily on manual, visual and rope-access techniques that expose personnel to hazardous environments and produce variable, non-standardised datasets. This paper presents the integration of a high-resolution 3D shaft scanning system (Lazaruss™) into the ABB Ability™ Smart Hoisting digital platform. The combined system enables automated shaft condition mapping, digital twin generation and cross-domain analytics linking structural integrity data with hoist mechanical and electrical performance parameters. The integration supports predictive maintenance strategies, enhances inspection repeatability and reduces downtime associated with manual shaft assessments. The architecture, data workflows and operational benefits of the system are examined in the context of lifecycle asset management for underground hoisting infrastructure.
1. Introduction
Mine hoisting systems are among the most safety-critical installations in underground mining operations. Shaft infrastructure — including guides, buntons, sets, services and linings — is subjected to dynamic loading, vibration, environmental degradation and progressive wear. Maintaining structural integrity is essential to ensure conveyance stability, regulatory compliance and production continuity.
Traditional shaft inspection methodologies typically involve scheduled shutdowns, visual assessments and manual measurement procedures. These processes are labour-intensive, expose personnel to confined-space and fall hazards, and may result in inconsistent data capture. Furthermore, the limited frequency and qualitative nature of manual inspections constrain the implementation of predictive maintenance strategies.
Recent advances in digital sensing, 3D scanning and industrial data integration offer opportunities to transform shaft condition monitoring. This paper describes the integration of Point Laz’s Lazaruss™ 3D shaft scanning system into ABB Ability™ Smart Hoisting 4.0, creating a unified digital environment for hoist and shaft integrity monitoring.
2. Background: Limitations of Conventional Shaft Inspection
Manual shaft inspections present several operational and engineering limitations:
- Safety exposure: Personnel entry into shafts introduces inherent risks associated with height, restricted access and suspended infrastructure.
- Inspection variability: Visual assessments depend on inspector experience and may lack measurement precision.
- Limited temporal resolution: Inspections are typically conducted at fixed intervals, reducing the ability to detect progressive deterioration between shutdowns.
- Fragmented datasets: Structural data is often stored separately from hoist operational data, limiting integrated analysis.
These constraints can delay detection of structural misalignment, progressive deformation or localised damage, increasing the risk of unscheduled downtime.
3. System Architecture
The integrated solution comprises three principal layers: data acquisition, data processing and system integration.
3.1 Data Acquisition
The Lazaruss™ system employs a high-resolution 3D laser scanning platform deployed within the shaft during controlled operational windows. The system captures dense point cloud data representing shaft geometry and structural elements, including:
- Guide rails
- Buntons and cross members
- Shaft steelwork
- Services and utilities
- Shaft lining surfaces
Spatial referencing is aligned with shaft coordinate systems to enable consistent repeatability between scanning cycles.
3.2 Data Processing
Captured point cloud datasets undergo:
- Noise filtering and artefact removal
- Surface reconstruction
- Feature extraction and segmentation
- Dimensional analysis and deviation measurement
Successive scans are registered to a common reference frame, enabling time-series comparison and deformation mapping.
3.3 Integration with ABB Ability™ Smart Hoisting
Processed structural data is integrated into the ABB Ability™ Smart Hoisting 4.0 platform via secure digital interfaces. The platform consolidates:
- Hoist drive performance metrics
- Brake system diagnostics
- Rope condition monitoring data
- Vibration and load cycle measurements
- PLC and SCADA operational parameters
This unified architecture enables cross-domain analytics linking structural condition with operational performance.
4. Digital Twin and Time-Series Analysis
Each scan generates a geometrically accurate 3D representation of the shaft, forming a dynamic digital twin. Over successive inspection cycles, the system enables:
- Deviation vector analysis
- Quantification of wear rates
- Monitoring of guide alignment and clearance envelopes
- Detection of structural displacement or deformation
Time-series datasets allow engineering teams to establish degradation trends and define threshold-based intervention criteria.
5. Predictive Maintenance and Risk Modelling
Integration of structural condition data with mechanical and electrical performance parameters enhances predictive maintenance capabilities. For example:
- Guide misalignment detected through 3D scanning may be correlated with abnormal vibration signatures.
- Progressive deformation may be evaluated alongside braking force distributions and load cycle frequency.
- Clearance reductions can be analysed relative to conveyance geometry and dynamic loading conditions.
By combining these datasets, maintenance teams can move beyond time-based inspection regimes toward condition-based and risk-informed maintenance planning.
Machine learning algorithms may be applied to historical datasets to improve anomaly detection and refine degradation forecasting models over time.
6. Operational and Safety Impacts
The integrated system offers several measurable benefits:
- Reduced personnel exposure: Minimisation of manual shaft entry for inspection.
- Improved repeatability: Standardised, high-resolution digital inspections.
- Earlier defect detection: Identification of structural deviations before critical thresholds are reached.
- Reduced downtime: Streamlined inspection workflows and automated reporting.
- Enhanced compliance: Improved documentation and traceability of inspection results.
From a lifecycle perspective, the availability of quantitative degradation data supports long-term capital planning for shaft rehabilitation and infrastructure upgrades.
7. Implications for Lifecycle Asset Management
Shaft infrastructure represents a long-life, capital-intensive asset. The integration of high-resolution structural scanning into a broader hoist monitoring ecosystem enables:
- Quantitative lifecycle modelling
- Risk-weighted capital forecasting
- Improved shutdown scheduling
- Data-driven asset integrity management
By embedding structural intelligence within digital hoisting platforms, mining operations can enhance reliability while optimising total cost of ownership.
8. Conclusion
The integration of 3D shaft scanning technology into a unified hoist digital monitoring platform represents a significant advancement in underground mining asset management. By combining high-resolution structural data with mechanical and electrical performance metrics, operators gain a comprehensive system-level understanding of shaft and hoist integrity.
This approach supports the transition from reactive inspection practices to predictive, model-based maintenance strategies, contributing to improved safety, reliability and operational efficiency in underground mining environments.
