Artificial Intelligence applied to mining: monitoring system for predictive maintenance of conveyor belts

In Chile, conveyor belts are the main mode of transport for granular and bulk materials in large volumes, such as minerals or coal. Their presence is essential in mining and port areas due to the large amount of material that needs to be moved and transported. 

Conveyor belts are a transport system formed by a continuous belt that runs between two pulleys with an intermediate take up pulley. They operate by friction between the belt and the surface of a pulley, which in turn is driven by a motor. The other pulley usually rotates freely, without being driven, and its function is to serve as a return to the belt. The belt is supported by intermediate rollers or idlers, both for guidance and return, between the two pulleys. 

Therefore, conveyor belts are made up of a series of elements such as the head or drive pulley (motor, reducer, bearings, etc.), the tail pulley, the idlers and the belt, among others.

Overview of the belt conveyor.

One of the main challenges faced by conveyors belts is the accelerated deterioration caused by the environment and operation itself, which translates into obstructions and erosion in the mechanical systems due to the deposit of dust particles or other elements, resulting from the mining or port activity. Undoubtedly, this produces a reduction in the useful life of the elements that compose them, leading on many occasions to unexpected downtimes. 

The economic investments to maintain conveyor belts in good condition are enormous. According to studies carried out by international experts, the maintenance cost of conveyor belts can represent up to 30-50% of the operating cost of a mining operation. 

To give an example showing the order of magnitude of the maintenance investments necessary also in the port terminals, at the Europees Massagoed Overslagbedrijf BV terminal in Rotterdam, maintenance cost of conveyor belts reached 5 million dollars only in 2013, for a total turnover of 175 million dollars. 

Entering into a more comprehensive analysis, about 52% of this investment is used for the maintenance of the rotating elements (drive pulley or tail pulley, among others), 18% for the intermediate idlers, 4% for the belt and 26% for the remaining components. It should be noted that of the 52% maintenance cost of the rotary elements, 46% is due to bearing failures. There was no technology on the market before our solution that could reliably detect failures in this element.

In addition to maintenance, the operator must consider the cost associated with downtime of a conveyor belt due to unscheduled stops caused by failures in different elements. According to personnel at the Queensland Gold Mine in Australia, one hour of conveyor belt downtime can result in economic losses of 40,000 – 50,000 dollars. 

If we analyse the causes of unplanned conveyor belt downtime, these are mainly due to pulley failures (54% of the total downtime), with 28% corresponding to intermediate idlers, 9% to the belt and 9% to other elements.  

Given the lack of solutions on the market capable of providing an optimum solution to this situation, we identified the need to develop a digital tool capable of characterising the defects of all the elements of the belt in early stages, making it possible to move from the current corrective maintenance approach (acting once the failure has occurred) to a predictive one (in which the damage can be anticipated and, consequently, action can be taken before the critical failure).

Our solution to the problem: real-time detection and prediction of failures using temperature and inertial response

Currently in Chile, conveyor belt maintenance, both in the port and mining areas, is traditionally carried out based on the results of visual inspections and corrective maintenance. However, new practices are emerging in the market that combine visual inspections with different non-destructive inspection technologies, based on thermography, ultrasound or vibration analysis, with the aim of reducing the limitations and subjectivity associated with traditional practices. 

However, these techniques have important limitations. For example, solutions based on thermography, which study the temperature levels of machines in order to detect electromechanical problems, have as their main limitation their inability to diagnose the problems, since they only detect them once they are in highly advanced states.

On the other hand, there are solutions based on ultrasound, which are based on the analysis of high-frequency sound waves produced by the machines when they experience a problem. However, its use is limited to bearings and is not capable of either detecting the reason for the failure or providing a diagnosis.

Finally, there are solutions based on vibration analysis, which rely on the study of the vibratory signals emitted by rotating machinery, allowing certain signal processing methods to be applied to extract the information contained in these vibration registers. Its main limitation is the masking of the signals, presenting low accuracy in detecting faults (around 40-50%).

Considering the limitations of the existing systems and the technologies presented in the state of the art, we focused on developing our own solution which, through the development of filters and advanced mathematical processing of data recorded in situ, would be capable of achieving precise diagnoses in early stages of all the key elements that compose a conveyor belt.

Our methodology for developing the solution

Through our methodology, taking as a starting point the requirements defined as a result of the analysis and research carried out, we decided to develop a global monitoring system for the different elements of the belt that would make it possible to carry out a diagnosis in a simple, automatic, wireless and real-time manner, while generating a predictive maintenance plan.

Based on this approach, and after an exhaustive analysis of the state of the art, we determined that the best approach was to combine two different technologies: 

  • Vibration analysis of the drive pulley, tail pulley and take up pulley; and
  • Temperature analysis of the intermediate idlers.

Starting from these premises, and after designing and assembling sensor nodes with accelerometers of the necessary sensitivity and robustness, we developed algorithms to characterize the vibratory signal of certain rotating elements (drive pulley, tail pulley and take up pulley), including a pre-processing (filtering) that would allow removing the noise from the registered signal. 

Thus, in order to unmask the signals that present noise from various sources and could induce mistakes, a self-adaptive filter was developed to eliminate the noise that hinders the analysis of the vibratory signals. This filtering also incorporated an algorithm capable of updating the filtering parameters to minimise error and get as close as possible to the real signal of the elements whose signal is masked (e.g. bearings). 

Finally, after obtaining the processed signal, a pattern identification algorithm was developed, by means of which, with a comparison of vibratory patterns, our solution is capable of determining the existence of a defect, recognising the type of fault that exists and the element in which it occurs.


Installation configuration of the accelerometer nodes in the drive pulley

At the same time, a mathematical algorithm was generated so that the system would be able to obtain, through the registered temperature, if there was an abnormal operation of any intermediate idler.  

The reasoning behind this approach is that when an intermediate idler is in a bad condition due to the wear of the bearings that make it rotate properly, it vibrates. These vibrations produce a temperature increase that exceeds the normal operating threshold. Thus, normal operating temperatures range from 20°C to 50°C, depending on the surrounding temperature. If the temperature of a idler increases at higher temperatures, which range from 80°C to 120°C, then that is a clear sign of a potential malfunction. By incorporating these temperature sensors into the idlers, it was possible to monitor this overheating so that it was possible to detect intermediate idlers that were in poor condition. 

Thanks to the numerous tests carried out during the development of the solution, it was possible to obtain and calibrate different temperature threshold values depending on the position of the idler, its speed of rotation and the external temperature, making it possible to diagnose the defect and its cause.

Installation configuration of temperature sensor nodes in intermediate idlers.

As a final stage in our methodology, the solution was validated on several conveyor belts in Puerto Panul and Puerto Mejillones, being able to demonstrate the high reliability of our solution and its advantages over the competition in both cases.

Image of one of the nodes installed in the drive pulley (left) and installation of the router node by one of our engineers to ensure secure data transmission


Our team has developed an integrated system composed of innovative software and hardware units that makes it possible to detect defects in any of the key elements of the conveyor belts, so that a predictive maintenance strategy can be implemented that minimises the necessary investment and avoids unexpected downtime in all cases.

Thanks to the application of Artificial Intelligence technologies, the characterisation and analysis of vibration patterns and temperature patterns of the different elements makes it possible to diagnose hidden faults at early stages, something that was impossible until now.  

For more information on this innovative solution, please download our specific brochure. 

In addition, if you are interested in improving the productivity of your industrial sector, do not hesitate to check other solutions developed by our team or contact us to jointly develop a tailor-made technological solution that responds to your specific requirements. 


Ašonja, A., & Adamović, Ž. (2010, September). The Economic justification of the Automatic lubrication Using. In 14th International Research/Expert Conference” Trends in the Development of Machinery and Associated Technology” TMT 2010, Mediterranean Cruise (pp. 11-18). 

Thieme, K. R. (2014). Economic Justification of Automated Idler Roll Maintenance Applications in Large-Scale Belt Conveyor Systems; Economische rechtvaardiging van geautomatiseerde applicaties voor rollenonderhoud in grootschalige systemen van bandtransporteurs.

Zimroz, R., & Król, R. (2009). Failure analysis of belt conveyor systems for condition monitoring purposes. Mining Science, 128(36), 255.

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