In this post, we will discuss the considerations for sensors in a stone crushing plant. It is advised that you go through 'Anatomy of a Stone Crushing Plant' and 'Operating Challenges in a Stone Crushing Plant' before reading further since that will give you all the context that you need about this post. There we discuss how a crushing plant works, what are the common operating challenges and the possible solutions to them. Selections made for the sensors in this post are considering the applications of machine learning and deep learning models that we will train and use in order to solve these operating challenges.
Why do we need sensors?
Machine learning models work by training on a dataset and hope to solve the task at hand, be it regression, classification, or clustering, when given data similar to the data that they are trained on.
In our context, machine learning models will take as input, data related to the functioning of the machines during production. Since these machines are highly specialized and there is a large degree of variation between 2 machines even in the same class, publicly available datasets are hard to come by and mostly not useful. We will need to collect data from our specific machines and train our models on them for maximum accuracy. Only then will we be able to reliably use these models for autonomous control.
Types of sensors
Vibration Sensors
One of the types of sensors that we will use is the vibration sensor. Vibration sensors are devices that measure the vibration of the machines during operation. Vibration data can be used to detect anomalies in the functioning of the parts such as electric motors, gears, fans, compressors, etc. Vibration sensors can also help to monitor the condition and performance of the machines and prevent breakdowns or malfunctions. There are three common types of vibration sensors that we will use:
Displacement Transducers These sensors measure the displacement or distance of the vibrating part from a reference point. They can measure low frequency vibrations and are temperature stable. They can also identify imbalance and misalignment in the machines. However, they are difficult to install, susceptible to shocks, and require calibration depending on the type of surface.
Velocity Transducers These sensors measure the velocity or speed of the vibrating part. They do not need external power and have constant velocity sensitivity over a specified frequency range. However, they are large in size, sensitive to magnetic interference, and have moving parts that can wear out.
Accelerometers These sensors measure the acceleration or force of the vibrating part. They are the most popular transducers and can measure frequency in one axis or three dimensions, depending on the type of accelerometer. They have greater reliability, large frequency range, and linearity. However, they have higher cost and need external power.
Acoustic sensors
Another type of sensor that we will use is the acoustic sensor. Acoustic sensors are devices that measure the sound that is emitted by the machines during operation. Sound data can be used to assess the health of the machine and detect anomalies in its functioning. For most of the plant workers, sound is the first piece of information that they rely on to make a judgment about the health of the machine, so we should also include the audio signal for the same. Therefore, sound sensors are an essential part of the sensor suite.
One major challenge is that during production, there is a lot of random noise produced that is not related to the functioning of the machine. In Tagawa et al. [2], some methods are proposed that will solve this problem and allow us to use acoustic data for predictive maintenance. A possible solution would be to record the audio input for a predefined duration and convert it into a mel spectrogram. This would be the input of an autoencoder that will have convolutional layers. To detect anomalies, at inference time, we would pass a 5-second mel spectrogram to the autoencoder. If the sound signature is that of a healthy machine, like the data that we have trained the autoencoder on, then the loss of the reconstruction would be really low. Any anomaly would be correlated with a high reconstruction loss.
Oil flow sensors for cone crusher
The cone crusher is the only machine that has an oiling system so we will have this data logged as well. The oil flow sensor ensures that the oil is flowing at an optimum rate in the cone crusher
Oil temperature sensor
The oil temperature is a vital piece of data that we have to log, as it indicates whether the friction inside the cone crusher is causing overheating or not. An overheated part in the machine can break or deform and also damage other connected parts. This is crucial for the health of the machine. We can use RTDs (resistance temperature detectors) for the most accurate and robust oil temperature measurement. RTDs are sensors that measure the resistance of a metal wire as it changes with temperature.
Power usage sensors
The power used by each machine is an important indication of the health of the machine. If the machine is offering increased resistance in terms of amperes then it is likely that there is something unusual in the machine We will use a current transducer to measure this.
Machine ambient temperature sensors
Machines' ambient temperature can also prove to be an important feature, especially if we wish to control the plant through a reinforcement learning agent. We will use typical smart thermometers for this reading.
Cameras for dust detection and suppression
Machines in a stone-crushing plant naturally produce a lot of dust, it is important to detect the amount of duct the machine makes and suppress the dust if it makes more dust than normal. Right now an employee turns on the dust suppression system if he sees that it is making too much dust or it remains on at all times. A separate AI system will detect if the machine is producing too much dust and then turn on the dust suppression system if it goes over a limit.
Lidar sensors to measure the wear on wear parts
This is something that still has to be figured out. But on a high level, the wear parts on all three crushing machines need to be replaced every 20k tons approximately. Since they are really expensive, it is important to extract the maximum life out of each cycle. Right now, an employee makes an approximation through eye measure and changes the parameters of the crushing machines. We will need to figure out a way to measure the wear after each session of production. And eventually minimize the wear.
Metso’s crusher mapper gives a 3d render of the crusher wear parts which can be used to calculate the oss and css make informed decisions. We will develop a similar system using a fixed lidar sensor that make measurements of the wear parts after every session. The reading will look a bit like the picture above, since this will be a 3d point cloud we will be able to measure the css and oss of the cone crusher.
Mass flow sensors:
Mass flow sensors measure the material’s mass flow rate on a conveyor belt. They calculate the throughput at different production stages, which is important for maximizing quality, efficiency, and profitability. There are different types of mass flow sensors, such as:
Conveyor belt scales: They use load cells and speed sensors to measure the material’s mass on a conveyor belt. They are simple and reliable, but they may need frequent calibration and maintenance.
Electrical power-based sensors: This sensor uses Faraday’s law of electromagnetic induction, which states that a voltage will be induced when a conductor moves through a magnetic field. The sensor generates a magnetic field across the conveyor belt and measures the induced voltage in the material as it flows through the magnetic field. The induced voltage is proportional to the material velocity and, thus, the mass flow rate.
We need to investigate the pros and cons of these sensors to find the best option for our system.
Cameras for cubicity/sphericity estimation
The cubicity of the produced stone aggregate is the most important factor in the perceived quality. This is because a more cubic stone aggregate results in an improvement in the performance and durability of the concrete and asphalt mixtures they are used in. This happens because high cubicity translates to enhanced interlocking and bonding between the aggregate particles. In addition to that, higher cubicity aggregates require less cement and water in the cement and asphalt mix.
This research paper explores shape estimation of stone aggregate by analyzing images of material on a conveyor belt. Similar techniques can be used for real-time cubicity estimation on our conveyor belts. However, there are concerns about the generalization of these models, since they were trained and tested in an extremely controlled environment.
Conclusion
In this post, we have discussed the types of sensors that we will use in the stone crushing plant and how they can help us to collect data for machine learning and deep learning applications. We have also explained how these sensors can help us to monitor and control the condition and performance of the machines and prevent or detect anomalies.
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