AI-Powered Autonomous SPC for Mining Conveyor Systems

The autonomous layer ingests all monitored conveyor variables — belt speed, motor current, feed rate, load depth, belt alignment, bearing temperature, and vibration — and maintains a rolling statistical model of the current operating baseline. Control limits are recalculated continuously against this model using a moving data window. When the process is stable and capability is high, limits tighten to increase sensitivity. When a legitimate regime change is detected (ore type change, moisture shift, grade transition), limits transition to the new baseline within a configurable window — generating zero false alarms during the transition. The quality manager sees live control charts where every Western Electric rule violation, every Cpk movement, and every limit breach reflects a genuine departure from the current operating norm.

For quality programmes deploying machine vision on conveyor lines, iFactory integrates vision inspection outputs as quality data streams feeding the autonomous SPC control charts. Vision-detected defects — belt surface anomalies, material spillage, oversized material, misalignment, and foreign objects — are logged against the production record and contribute to the Cpk calculation for physical quality characteristics. The AI vision layer uses deep learning models trained on mining conveyor conditions, achieving over 120 frames per second with 93%+ detection accuracy in high-dust and low-illumination environments. This gives quality leaders a 100% inspection record for visual quality alongside the sample-based mechanical data — closing the coverage gap between periodic inspection and continuous monitoring.

Every autonomous limit change, every vision-detected defect, every quality leader action, and every test result is logged with a timestamp and the process context data — ore type, belt segment, shift, and load profile. This creates the documentation chain that ISO 9001 Clause 10.2 nonconformance requirements demand: a record showing what the autonomous system detected, what intervention was taken, and what the outcome was. The adaptive limit change log — showing every recalculation, the process data that triggered it, and the statistical justification — demonstrates that the quality programme actively maintains current, defensible control limits. Cpk trend reports, CAPA linkage records, and process capability histories are generated automatically and exportable for any date range, product grade, or conveyor zone.

ISO 9001 Clause 7.5 requires documented information to be controlled and maintained. For control limits, every change must have a documented rationale. iFactory addresses this through an automatic limit change log that records every autonomous recalculation — the timestamp, the triggering event (ore change, moisture shift, load regime transition, statistical baseline shift), the previous limit values, the new limit values, and the statistical basis for the recalculation (the data window and algorithm applied). This log is exportable in a structured format for direct inclusion in QMS documentation and is searchable by belt segment, ore type, and date range. Auditors reviewing the adaptive limit history see a controlled, documented process — not a system that changed limits without traceability. The key argument for autonomous limits in an ISO 9001 context is that limits calibrated on outdated process data are less defensible than limits that are demonstrably current, with the recalculation logic fully documented. Talk to an expert about configuring the limit change log format for your QMS documentation requirements.

The autonomous engine initialises using historical conveyor process data from the control system historian — belt speed, motor current, feed rate, and load measurements — paired with quality test records from the LIMS. Two to three months of paired process-to-outcome data is sufficient for the initial model on primary quality characteristics. The system deploys with limits calculated from the available data window and begins recalibrating from the first live data point. The self-tuning algorithm stabilises within two to four weeks as it accumulates enough data across ore changes and load transitions to distinguish between normal variation and genuine process drift. Quality managers typically validate the autonomous limits against a parallel static control chart for the first two weeks before relying on the autonomous system for decision support. Book a Demo to see a deployment timeline modelled against your conveyor network configuration.

iFactory's AI vision layer connects to existing IP camera networks already installed on conveyor lines — no new camera hardware required. The deep learning model runs on an edge computing device at each camera station, processing video at over 120 frames per second for real-time defect detection. Detected events — belt surface anomalies, material spillage, oversized material, misalignment, foreign objects — are transmitted to the autonomous SPC platform as structured quality data and logged against the production record. The vision output feeds directly into the control chart as an additional quality characteristic, contributing to the Cpk calculation for the affected belt segment. For operations without existing camera coverage, iFactory provides industrial-grade camera kits designed for high-dust and low-illumination mining environments. The vision model is pre-trained on mining conveyor conditions and is fine-tuned on site-specific data during the two-week commissioning period. Talk to an expert about a camera infrastructure assessment for your conveyor network.

Yes. iFactory's segment and product architecture registers each conveyor zone as a separate monitoring unit with its own specification profile — belt speed range, load limits, moisture tolerance, and defect rate targets. When the material type changes or a new ore blend begins, the active specification profile for each affected segment switches automatically and the autonomous SPC limits transition to the new baseline. The quality manager sees which ore type is currently active on each segment, which specification profile is in use, and the Cpk for each quality characteristic against the current segment's limits. Historical Cpk data is segmented by ore type and belt zone automatically, enabling comparison of performance across different material grades without manual data sorting. For operations running multiple ore types across a shared conveyor network, the system maintains separate defect histories, CAPA records, and Pareto analyses by segment and product type. Book a Demo to see multi-segment autonomous SPC configured for your conveyor network and ore profile.

The autonomous detection engine uses a dual-mode approach. For gradual drifts such as belt wear or slow moisture changes, the moving-window model updates limits incrementally with each data point, tracking the process as it shifts without generating alarms. For abrupt transitions such as ore type change or feed rate step-change, the regime detection algorithm identifies the break point within 5 to 15 data points (typically 2 to 10 minutes depending on data collection frequency) and initiates a controlled limit transition. During the transition window — configurable from 15 minutes to 2 hours — the limits widen to the new baseline range without generating SPC alarms, then settle to the standard calculation after the transition completes. The result is that a process change that would have generated 15 to 20 false alarms under static SPC produces zero false alarms under autonomous SPC, while genuine deviations occurring within the same period are still detected because they trigger the Western Electric rules against the appropriate transition limits. Talk to an expert about configuring transition windows for your conveyor process dynamics.