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A conveyor belt system for the heavy industry often includes several components, each playing a crucial role in the transportation of goods or materials. This machinery plays a critical role in many heavy industries including mining, construction, agriculture, and manufacturing. The following list of components is what one would find in industrial conveyers:
This is the heart of the system, where the actual transportation happens. It is usually made of a couple of layers of rubber – one layer for providing the necessary strength and durability and the second layer for transportation. These layers are often made up of different materials, including PVC, nylon, or rubber. For heavy industries, belts could be reinforced with steel or other metal weaves for added strength and resilience.
While this component is essential for the main purpose of the system, from the perspective of vibrational analysis it is not a very interesting one. Vibrations due to faults in the rotation components would present themselves here at a much later stage of degradation, at which point a critical failure might be unavoidable.
A conveyor belt system needs at least two pulleys to work effectively: a drive pulley and a tail or return pulley. The drive pulley, which is typically motor-driven, moves the belt. The tail pulley is often adjustable and is used to keep the belt tight and well-positioned. In a heavy industry environment, these pulleys are usually robust and made to withstand high loads.
As they are directly driven by the motor/gearbox they are a good indicator of faults in the machine, be it misalignment of the shafts or bearing degradation, etc. It is considered a good practice to have vibrational sensors installed in the bearing area and perform real time vibration monitoring.
The motor powers the drive pulley via the gearbox to move the conveyor belt. The horsepower needed will depend on the weight of the material being moved, the speed at which it needs to be moved, and the length of the conveyor. For heavy industry, large and powerful electric motors coupled with a gear box of comparable size are usually employed.
These are sets of rollers that provide support to the conveyor belt and the load carried on the belt. Idlers ensure the belt maintains the desired shape to carry the load and ensure smooth belt movement. They can be flat or trough-shaped, depending on whether they are carrying or returning the belt.
Bearings allow for smooth rotation of the pulleys. They are very important for the smooth operation of the system.
There are additional components that might or might not be present in the conveyer, like belt cleaners, a take-up unit and various control and safety devices. While important they are out of the scope of this article and they health is not considered as something that can easily result into critical failure, so we will not be looking into them in detail. Assuming regular maintenance is performed and timely visual inspection is carried out they should not cause any faults in functionality of the conveyer.
Conveyor belt failure analysis systems, like any other mechanical systems, are prone to various failures. Many of these failures can be prevented or minimized through regular maintenance, inspections, and staff training. In order to identify the problem, one must first know where to look, below are some of the common issues that arise during the operational cycle of an industrial conveyer system.
Over time, conveyor belts can become worn, torn, or damaged from the constant movement of materials, especially in heavy industries. Regular inspections can help identify areas of wear and tear early. Using belt cleaners can help reduce wear by keeping the belt clean. Replacing worn out parts promptly can also prevent catastrophic failures.
These are mostly monitored on sight as they are relatively easy to identify via visual inspection, however as conveyers can sometimes be miles long it would be a good practice to try to minimize the number of these inspection and only perform them when needed (as indicated by other faults that might cause them).
Misaligned pulleys can lead to uneven wear on the conveyor belt, causing it to run off track and potentially leading to system breakdowns. Regular inspection and maintenance can ensure pulleys are properly aligned and functioning correctly. Additionally, tilt/alignment sensors could be utilized as a way to minimize the need of on-site inspection only when an alarm is triggered.
This can occur due to a lack of lubrication, contamination, or overloading. Early stages of this type of fault are hard to detect visually, this on-site inspection does little to help in this scenery, where at later stages it might be too late. Frequency-domain vibrational analysis is an amazing too for early fault prediction in bearings, as it can spot irregularities in the vibrational spectrum in early stages, when the bearing has not been significantly damaged, still.
Overheating or overloading can lead to motor failures. This would present itself in increased load in the form of increased thermal output, unstable or increased current consumption and increased vibrational levels. By monitoring the aforementioned metrics in real-time, a potential impending failure can be prevented and a maintenance interval can he scheduled in a safe and efficient manner.
There are more additional faults that can occur in a conveyer belts system, especially if it is under high load/stress. For example, there could be slippage in the conveyer belt, some of the transported material could spill. It is also possible that the idlers or the entire structure fails, however these are generally at a point where the system has been experiencing issues for some time and should not be unexpected. Additionally, skilled personal can easily spot these issues and take on-time actions to prevent critical system failure.
Thus, it is a lot more interesting to look into how one could monitor the rotational components (pulleys, bearings, motor and gearbox) and what data can be obtained that is useful in identifying the possibility of a failure.
Predictive maintenance is a strategy that uses data-driven, proactive monitoring techniques to predict when equipment failure might occur. It is at the core of any Industry 4.0 system, as it allows for maintenance to be planned before the failure occurs, increasing equipment lifespan and reducing downtime.
Conveyor systems typically generate a large amount of operational data. This can include things like motor current, conveyor speed, belt tension, load weight, and more. This data can be collected and analyzed to identify patterns and trends.
For example, an increase in motor current could indicate that the conveyor is being overloaded, which can lead to premature wear and tear. Similarly, changes in conveyor speed could indicate issues with the drive mechanism. Analyzing this data over time can allow you to spot trends and identify potential issues before they become serious problems.
This type of analysis is a good starting point for a predictive maintenance system, it provides overall condition monitoring and can be an early indication of failure, however on its own it is not sufficient to identify the exact component the issue is in. For the aforementioned reason it is mainly used in correlation with vibrational analysis data.
Vibration analysis is a key technique used in predictive maintenance. This involves monitoring the vibrations produced by machinery during operation. Each machine produces a unique vibration signature when operating under normal conditions. Changes to this signature can often indicate that a component has begun to fail.
By using vibration sensors attached to key points on the conveyor system (such as the bearings, motor, or pulleys), you can collect vibration data that can be analyzed to detect early signs of failure. These sensors can provide real-time data, allowing for immediate alerts when abnormal vibrations are detected.
Moreover, specific frequencies where vibrations occur can indicate failure in a specific component, allowing to insulate the issue and repair/replace the component in the most time-efficient way.
Combining vibration analysis with operational data analysis can provide a more holistic view of the conveyor system’s health. For example, if both vibration analysis and operational data indicate a potential issue, this can provide stronger evidence that maintenance is required.
These two techniques can be integrated into a predictive maintenance system through the use of advanced data analytics and machine learning techniques. The data collected from both vibration sensors and operational data can be fed into a machine learning model that’s trained to recognize the signs of potential failure.
As the model receives new data, it can continue to learn and improve its predictions. This can allow for more accurate predictions of when a component might fail and when maintenance should be performed.
Note: It is important to have a well-established baseline for what would be normal operating conditions in order for the model to accurately evaluate any change in conditions. Thus, it is important to have high accuracy, well-calibrated sensors that produce error free measurements.
For example, a machine learning model might identify a correlation between specific vibration patterns and an increase in motor current, indicating an impending motor failure. A combine sensor can be used to measure both metrics like NCD’s Industrial IoT Wireless Predictive Maintenance Sensor V3, that combines vibration, temperature and current probes.
Note: When selecting your sensor look for one that is wireless, environmentally protected, has good transmission range and measurement accuracy. It should have a long battery life (preferably up to 10 years) and be easy to install. The aforementioned NCD sensors satisfied all the aforementioned criteria.
The correlation in the measured data will trigger a maintenance alert, allowing maintenance to be performed before the motor fails and causes downtime. In addition, frequency spectra analysis can be carried in order to provide insight on which component exactly is the failing one (for example a specific bearing).
In this way, combining vibration analysis and operational data analysis can provide a powerful tool for predictive maintenance, reducing downtime and extending the lifespan of the conveyor system.
Let us look at an example theoretical scenario. A conveyor belt driver system that consists of a 750W 1494.7RPM motor and a 5-shaft gearbox (Hansen P4 Multistage – Horizontal). The system exhibits abnormal behavior, unstable/increased current consumption, excessive heating, increased vibration/noise.
This data can be obtained easily by using a good industrial grade sensor like the NCD Industrial IoT Wireless Predictive Maintenance Sensor V3. It is easy to install, measures all the aforementioned parameters, has up to 10 years of wireless battery life operation and works with a wide-range of modem and gateway connecting to platforms like AWS IoT Core, Azure IoT Hub, Losant, etc.
As a result, the normal operation of the conveyor is disturbed and there is an increased change of failure. Thus, a vibrational analysis needs to be carried out.
Upon measuring vibrations on the motor and gearbox the following data has been produced.
The data has been obtained form an I-care case study on conveyor belt predictive maintenance.
As expected, there is a strong component at the motor rotational frequency, however there are a number of low frequency components that have even greater levels, with the highest peak being at 1.373Hz. This indicates an issue, most likely in the gearbox. Upon further analysis We can see the following.
The data has been obtained form an I-care case study on conveyor belt predictive maintenance.
Upon closer examination of the plot, we can identify three strong vibrations at 1.373Hz, 2.74Hz and 4.11Hz. As these are equidistantly apart, they have to be harmonics of the same vibration.
Interestingly enough if we calculate the chain mesh frequency based on the mechanical parameters, we get exactly 4.11Hz, which is one of the measured components, furthermore if we look at the technical data of the gearbox, we have the following table where we have calculated the frequencies based on the RPMs of the corresponding shafts (from the official gearbox documentation, based on the corresponding reduction numbers for each shaft).
Note: It is important to have the correct information for the system, type of bearings, gearbox, etc. Vibration analysis relies on looking at specific frequency components in the vibration spectrum that are tied to the physical parameters of the equipment under test. If the parameters are erroneous, one might discard certain vibrational frequencies, assuming they are not fundamental fault ones, when in reality they are.
As it can be seen the 3rd, 6th and 9th harmonics of Shaft 4 directly coincide with the measured frequencies of the strongest vibrations. As these are all harmonics of the main rotation frequency of the 4th shaft, this leads us to believe that the fault is in the gearbox shaft 4 zone.
At this point it is a certainty that there is an issue and maintenance need to be schedules and performed. A good strategy would be to first check if the oil has metal particle content that is higher than the norm. More likely than not this would be the case and it would confirm that there is a gear issue. At this point the gearbox should be taken apart and examined, as it will probably need component replacement.
Utilizing vibrational analysis is a powerful tool for preventive maintenance. We have shown that conveyor belts are a system with numerous components that experience stress that can easily lead to failure. Utilizing wireless vibration sensors can make monitoring system components a lot easier and provide valuable data in order to evaluate system performance in real time. Utilizing advanced analytical tools one can even pinpoint the source of the vibration and suggest which component in particular is the failing one, based on the vibrational analysis performed.
Such a predictive maintenance system can be invaluable in preventing failure, reducing downtime and optimizing long term system performance (leading to reduced operational costs).