Comparative Analysis of Idler Monitoring Systems - Industrial Technologies Supply

This research continues the study “Idler Condition-Based Monitoring Systems: Review”, in which we examined existing technologies. In this section, we provide a comparative analysis of conveyor idler condition-based monitoring solutions in terms of detection capabilities, ease of deployment, cost, and data analytics, as well as their applicability to surface (overland) and underground mining conveyors. The analysis outlines the practical advantages, limitations, and economic implications, offering valuable guidance for mining operators looking to implement or enhance predictive maintenance programs.

Each technology above has advantages and limitations. Below is a structured comparison across key criteria:

Detection Capabilities

Systems that directly measure vibrations and acoustics at the source – such as frame-mounted sensors and embedded Smart-Idler – tend to provide the earliest and most accurate fault detection. They can identify subtle bearing faults well before heat or visual symptoms occur. For instance, distributed vibration sensor networks have demonstrated the ability to detect idler failures several weeks ahead of traditional manual inspection methods. In contrast, temperature-based monitoring systems typically detect faults later in the degradation cycle, once friction has already caused noticeable heat. Robots and UAVs that rely on thermal imaging are effective for confirming known faults but generally lack predictive capabilities. An industry review noted that mobile robotic and UAV systems “do not allow premature fault detection” and reported less than 50% monitoring effectiveness compared to continuous embedded sensors [5].

evolution-of-failure-signals-in-industrial-equipmen

Smart-Idler systems, in particular, stand out as the most advanced option, offering a combination of internal vibration, temperature, and wear monitoring (based on RPM measuring), paired with advanced analytics and predictive algorithms [13], [26]. This integrated approach enables early, accurate detection of a wide range of failure modes, making it the most comprehensive and proactive solution among current technologies [5], [21].

Cost

The cost factor breaks down into initial installation and operating cost. Frame-mounted sensor networks require purchasing hundreds or thousands of sensor nodes – the RCM system, for instance, might have a sensor per idler frame (roughly one sensor per 3–4 rollers). These sensors are typically more than a hundred USD each, plus the cost of hubs and software subscription. The per-idler cost thus could be on the order of ~$50–$80. Smart idler rollers have to replace existing rollers – Vayeron aimed for a low unit cost for the embedded electronics, but the real-world cost including the roller and software subscription is higher (though exact figures are not published). Still, smart rollers at scale may become very cost-competitive, especially as they self-powered and eliminate maintenance. Fibre optic DAS/DTS has a high upfront cost for the interrogator (hundreds of thousands of dollars for the unit), but the fibre cable itself is relatively cheap per meter, and one system covers many kilometres. So for a very long conveyor, fibre solutions can be cost-effective (and they often double as fire detection, adding value). Thermal cameras and robots/drones: A high-grade thermal camera can be expensive (several thousand dollars each), and a drone or robot platform can cost tens of thousands. However, you usually need only a few of these (maybe one drone for an entire operation, or one robot per section of conveyor). The Spidler robot is presumably very expensive (a large custom machine plus rails) – likely justified only for critical conveyors where downtime costs millions [5]. Maintenance costs (discussed next) also factor into total cost of ownership.

Ease of Deployment & Maintenance

Wireless sensor nodes (RCM) – Deployment involves physically mounting sensors on many idlers or frames. This can be labour-intensive initially (e.g. walking the belt to install each device) [24]. However, it can be done gradually and during normal operations. Deploying the Smart-Idler system involves physically replacing during a scheduled maintenance existing rollers with Smart rollers with a Smart-Idler sensor integrated during roller manufacturing [26]. Maintenance-wise, battery-powered sensors need periodic battery replacement (often every 1-4 years depending on usage). Smart-Idlers avoid that with self-power, which is a big advantage – they claim a >10-year life with no maintenance that greatly exceeds average roller life [12].

Fibre systems – Deployment can be relatively straightforward: securing a fibre optic cable along the conveyor length. This might be done during a scheduled outage or in sections. Once in place, fibre systems are extremely low-maintenance (no field power, and the interrogator is usually in a clean location). They also may require routine calibration. Fibre cables are robust, though they must be protected from mechanical damage; even if a fibre breaks, many systems can detect and function up to the break point [25]. For underground, fibre is arguably the easiest method to maintain compliance (no power in hazardous areas). Thermal cameras (fixed) – require mounting and ensuring a clear line of sight; in dusty mining environments, lenses may need cleaning. Robots and drones – These are more complex to deploy. Spidler needs rails installed along the conveyor (a significant modification). The Idler Predict robot needs a vehicle path alongside the conveyor. Drones need trained operators or advanced autonomous flight programming, plus considerations like weather and no-fly zones. Maintenance of robots/drones involves mechanical parts, batteries, etc., which is non-trivial. In summary, distributed sensors, Smart-Idler and fibre are more “install and forget” (aside from battery changes for some), whereas robotic systems require ongoing mechanical upkeep and operational oversight [3], [5].

Data Analytics & Integration

Proactive analytics are a strong suit of systems like Smart-Idler and RCM – they upload data to cloud or local software that often includes trending, predictive algorithms, and user dashboards [7], [12]. These platforms can integrate with mine maintenance systems (issuing work orders or alarms). Fibre optic systems also produce a huge amount of data and rely on pattern recognition algorithms; vendors provide software that filters the raw signals and presents actionable alerts (e.g. location 3.2 km: idler acoustic anomaly detected). Simpler temperature-threshold systems might just give an alarm output to a PLC or control room when a threshold is exceeded [9], [15], [16]. AI-based vision systems are data-heavy and typically run on powerful computing hardware (either on-site or cloud) to analyse images; their integration is still evolving, but they can be set up to alert the same way (alarm or highlight on a map the location of a suspected fault). Overall, the data capability ranges from basic threshold alarms to advanced predictive analytics. Mines increasingly prefer solutions with rich analytics – for example, knowing not just that an idler is bad now, but which idlers are trending towards failure in the next month. Smart-Idler’s multi-sensor approach is explicitly aimed at predicting failures in advance, not just detecting them [2], [5], [21], [26]. Some newer platforms also leverage machine learning across fleet data – if a site has thousands of instrumented rollers, the data can train models to improve detection accuracy and reduce false positives. In contrast, a drone that takes photos will rely on either a human inspector or an offline AI to interpret the data, which is a more manual process.

Suitability for Overland vs Underground

Different mining environments have unique requirements:

Overland (Surface) Conveyors

These often span difficult terrain and long distances, making wireless and fibre solutions attractive (less need for physical access). Solar panels can be used to power remote gateways or repeaters for wireless sensor systems [3]. Overland conveyors are exposed to weather, so any outdoor sensors must be rugged and waterproof (most modern devices are IP67+ rated). Wireless communication is generally feasible in open air (mesh radio or cellular links). Fibre optic systems work equally well outdoors (temperature extremes need to be accounted for in calibration). Drones are especially suited to overland conveyors – as there is open sky and room to fly alongside conveyors. Overland conveyors also can benefit from robotic solutions like Spidler if the terrain makes manual replacement dangerous or slow [5], [22]. In terms of safety, surface conveyors don’t have methane or coal dust explosions to worry about, so standard electronics are fine. Thus, all technologies (sensors, fibre, robots, drones, cameras) can be applied in overland settings, and the choice will hinge on effectiveness, cost and maintenance preferences.

Overland conveyor with Idler Monitoring System

Underground Conveyors

Underground coal conveyors require intrinsically safe or explosion-proof equipment due to gas and dust. This heavily favours solutions with no powered components in the drift. Fibre optic DAS/DTS provides continuous coverage with the interrogator located outside the hazardous area, making it well suited for use in coal mines [9]. Similarly, temperature-sensing idlers like Küpper’s can be designed to be Intrinsically Safe if using appropriate wiring and barriers (since they are low-power sensors). Smart-Idler explicitly notes it improves safety by reducing fire risk in underground coal [2] and aids compliance with regulations [17]. That suggests it was designed with hazardous area use in mind (the antenna and generator are contained within the roller). Drones in underground mines are currently very limited – navigation without GPS and the risk of igniting methane make them unsuitable in coal mines. Tethered inspection robots could be used underground (perhaps running along the conveyor structure), but any electronics/actuators must be permissible, which generally limits widespread use. Another factor: underground conveyors are typically shorter than giant overland systems, but they run in series through inclined roadways. Thus, a fibre or sensor network can be segmented per conveyor flight. Mines with strict fire prevention rules lean heavily on temperature monitoring (DTS cables, fusible links, etc.) – these are simpler but effective for the narrow goal of fire prevention. For comprehensive condition monitoring underground, for early warning and for fire alarm backup a Smart-Idlers.

Conclusion

When comparing options, mines must balance factors: accuracy vs. complexity, and cost vs. benefit. High-accuracy systems (vibration/acoustic sensors, Smart-Idler) tend to cost more upfront but can prevent expensive downtime [5], whereas simpler approaches (temperature alarms) are cheaper but only mitigate catastrophic failures. Ease of deployment can determine feasibility – a fibre cable or a drone might be favoured for an existing 20 km conveyor, whereas new conveyors could be built with embedded smart rollers from day one, as was done in the Dune Express project [21]. Data and analytics capabilities are equally important: the raw sensor data is only valuable if it’s translated into clear, actionable information for maintenance planners.

As the industry strives for zero unplanned downtime and safer, unattended operations, condition-monitoring technologies play a crucial role. Mines are already seeing benefits in reduced roller incidents and maintenance costs by adopting these solutions [18], [20]. Going forward, continued integration of sensor data (perhaps using AI to analyse vibration, acoustic, thermal, and visual inputs together) will further improve detection confidence.

References

[1] M. J. Ibarra-Cabrera et al., “An IoT Solution to Detect Overheated Idler Rollers in Belt Conveyors,” IJACSA, 2024.

[2] Vayeron Pty Ltd., “Smart-Idler® Failure Detection Efficacy Analysis,” 2020.

[3] Delft University of Technology & Rulmeca Group, “Investigation of Smart Conveyor Idler Monitoring Technologies,” 2017.

[4] Liu, X.; Pang, Y.; Lodewijks, G.; He, D. Experimental research on condition monitoring of belt conveyor idlers. 2020.

[5] Anibal S. Morales, Pablo Aqueveque, Jorge A. Henriquez “Technology Review of Idler Condition Based Monitoring Systems,” 2017.

[6] Scott Automation, “Robotic Idler Predict System Whitepaper,” 2023.

[7] Micomo Pty Ltd., “Roller Condition Monitoring (RCM) System Description and Case Studies,” 2014.

[8] P. Dabek et al., “Automatic Detection of Overheated Idlers Using IR and RGB Imaging,” 2022.

[9] Yokogawa Electric Corp., “Fiber Optic DTS for Conveyor Fire Prevention,” 2013.

[10] Artur Küpper GmbH & Co. KG, “Sensor-Integrated Idler Roller Development,” 2019.

[11] Mining3, “Conveyor belt monitoring for wear detection,” 2023.

[12] Smartidler.com, “Technical specifications and product pages.”

[13] Vayeron Pty Ltd, “An idler, a method for monitoring a plurality of idlers, and a conveyor system”, AU2014/050246, Australia, 2015

[14] Vayeron Pty Ltd., “Smart Idler – Effect on Project Economics,” Vayeron, 2025.

[15] Hawkfiber.com / Hawk Measurement, “Fiber-based conveyor sensing.”

[16] Hawk Measurement Systems, “Conveyor Fire Prevention via Thermal Sensing,” 2020.

[17] Australian Government, “Underground Conveyor Safety Standards AS 1755,” 2020.

[18] Rio Tinto, “Smart Idler Rollout and Maintenance Savings,” Case Study, 2018.

[19] J. Widodo and B. Yang, “A review of vibration and acoustic measurement methods for monitoring rolling element bearing condition,” Mechanical Systems and Signal Processing, July 2011.

[20] Glencore, “Underground Coal Mine,” Case Study, 2019.

[21] Paul Moore, Head to Tail, International Mining, February, p.46-63, 2024.

[22] Sandpit Innovation Pty Ltd, Lewis Australia Pty Ltd, “Conveyor Belt Roller Replacement”, WO2013138841, Australia, 2013.

[23] K.R. Thieme, Report: “Economic Justification of Automated Idler Roll Maintenance Applications in Large-Scale Belt Conveyor Systems”, Delft University of Technology, Netherlands, 2014.

[24] Intium Energy Limited, “Vibration detection system, apparatus and method”, WO 2012122597 A1 | PCT/AU2012/000263, Australia, 2012.

[25] Ben Yang Yang, “Fibre Optic Conveyor Monitoring System”, MSc degree thesis, The University of Queensland, Australia, 2014.

[26] Industrial Technologies Supply, itecsu.com, “Remote conveyor rollers monitoring system – Smart-Idler, Interview with Mark Walter at IMARC”, 2023

[27] Siami-Namini, S., et al., ‘Deep learning for conveyor idler fault detection using YOLOv5,’ Conference, 2022.

[28] M. Kang, S. Lee, and J. Kim, “Acoustic-based fault diagnosis for rotating machinery in noisy environments using cyclostationary analysis,” Mechanical Systems and Signal Processing, 2020.

[29] A. Morales, S. Seshadri, and J. Brayan, “Acoustic emission-based condition monitoring of conveyor idler rollers using mobile robots,” IEEE Transactions on Industry Applications, pp. 2639–2647, May–June 2017.

[30] SKF Group, “SKF Idler Sound Monitor Kit: Field evaluation of conveyor idler condition using ultrasonic listening technology,” SKF Mining Industry Solutions, 2019.