This is a customer post jointly authored by ICL and AWS employees.
ICL is a multi-national manufacturing and mining corporation based in Israel that manufactures products based on unique minerals and fulfills humanity’s essential needs, primarily in three markets: agriculture, food, and engineered materials. Their mining sites use industrial equipment that has to be monitored because machinery failures can result in loss of revenue or even environmental damages. Due to the extremely harsh conditions (low and high temperatures, vibrations, salt water, dust), attaching sensors to these mining machines for remote monitoring is difficult. Therefore, most machines are manually or visually monitored continuously by on-site workers. These workers frequently check camera pictures to monitor the state of a machine. Although this approach has worked in the past, it doesn’t scale and incurs relatively high costs.
To overcome this business challenge, ICL decided to develop in-house capabilities to use machine learning (ML) for computer vision (CV) to automatically monitor their mining machines. As a traditional mining company, the availability of internal resources with data science, CV, or ML skills was limited.
In this post, we discuss the following:
How ICL developed the in-house capabilities to build and maintain CV solutions that allow automatic monitoring of mining equipment to improve efficiency and reduce waste
A deep dive into a solution for mining screeners that was developed with the support of the AWS Prototyping program
Using the approach described in this post, ICL was able to develop a framework on AWS using Amazon SageMaker to build other use cases based on extracted vision from about 30 cameras, with the potential of scaling to thousands of such cameras on their production sites.
Building in-house capabilities through AWS Prototyping
Building and maintaining ML solutions for business-critical workloads requires sufficiently skilled staff. Outsourcing such activities is often not possible because internal know-how about business process needs to be combined with technical solution building. Therefore, ICL approached AWS for support in their journey to build a CV solution to monitor their mining equipment and acquire the necessary skills.
AWS Prototyping is an investment program where AWS embeds specialists into customer development teams to build mission-critical use cases. During such an engagement, the customer development team is enabled on the underlying AWS technologies while building the use case over the course of 3–6 weeks and getting hands-on help. Besides a corresponding use case, all the customer needs are 3–7 developers that can spend more than 80% of their working time building the aforementioned use case. During this time, the AWS specialists are fully assigned to the customer’s team and collaborate with them remotely or on-site.
ICL’s computer vision use case
For the prototyping engagement, ICL selected the use case for monitoring their mining screeners. A screener is a large industrial mining machine where minerals dissolved in water are processed. The water flows in several lanes from the top of the machine to the bottom. The influx is monitored for each of the lanes individually. When the influx runs out of the lane, it’s called overflow, which indicates that the machine is overloaded. Overflowing influx are minerals that are not processed by the screener and are lost. This needs to be avoided by regulating the influx. Without an ML solution, the overflow needs to be monitored by humans and it potentially takes time until the overflow is observed and handled.
The following images show the input and outputs of the CV models. The raw camera picture (left) is processed using a semantic segmentation model (middle) to detect the different lanes. Then the model (right) estimates the coverage (white) and overflow (red).
Although the prototyping engagement focused on a single type of machine, the general approach to use cameras and automatically process their images while using CV is applicable to a wider range of mining equipment. This allows ICL to extrapolate the know-how gained during the prototyping engagement to other locations, camera types, and machines, and also maintain the ML models without requiring support from any third party.
During the engagement, the AWS specialists and the ICL development team would meet every day and codevelop the solution step by step. ICL data scientists would either work independently on their assigned tasks or receive hands-on, pair-programming support from AWS ML specialists. This approach ensures that ICL data scientists not only gained experience to systematically develop ML models using SageMaker, but also to embed these models into applications as well as automate the whole lifecycle of such models, including automated retraining or model monitoring. After 4 weeks of this collaboration, ICL was able to move this model into production without requiring further support within 8 weeks, and has built models for other use cases since then. The technical approach of this engagement is described in the next section.
Monitoring mining screeners using CV models with SageMaker
SageMaker is a fully managed platform that addresses the complete lifecycle of an ML model: it provides services and features that support teams working on ML models from labeling their data in Amazon SageMaker Ground Truth to training and optimizing the model, as well as hosting ML models for production use. Prior to the engagement, ICL had installed the cameras and obtained pictures as shown in the previous images (left-most image) and stored them in an Amazon Simple Storage Service (Amazon S3) bucket. Before models can be trained, it’s necessary to generate training data. The joint ICL-AWS team addressed this in three steps:
Label the data using a semantic segmentation labeling job in SageMaker Ground Truth, as shown in the following image.
Preprocess the labeled images using image augmentation techniques to increase the number of data samples.
Split the labeled images into training, test, and validation sets, so that the performance and accuracy of the model can be measured adequately during the training process.
To achieve production scale for ML workloads, automating these steps is crucial to maintain the quality of the training input. Therefore, whenever new images are labeled using SageMaker Ground Truth, the preprocessing and splitting steps are run automatically and the resulting datasets are stored in Amazon S3, as shown model training workflow in the following diagram. Similarly, the model deployment workflow uses assets from SageMaker to update endpoints automatically whenever an updated model is available.
ICL is using several approaches to implement ML models into production. Some involve their current AI platform called KNIME, which allows them to quickly deploy models developed in the development environment into production by industrializing them into products. Several combinations of using KNIME and AWS services were analyzed; the preceding architecture was the most suitable to ICL’ s environment.
The SageMaker semantic segmentation built-in algorithm is used to train models for screener grid area segmentation. By choosing this built-in algorithm over a self-built container, ICL doesn’t have to deal with the undifferentiated heavy lifting of maintaining a Convolutional Neural Network (CNN) while being able to use such a CNN for their use case. After experimenting with different configurations and parameters, ICL used a Fully Convolutional Network (FCN) algorithm with a pyramid scene parsing network (PSPNet) to train the model. This allowed ICL to finalize the model building within 1 week of the prototyping engagement.
After a model has been trained, it has to be deployed to be usable for the screener monitoring. In line with the model training, this process is fully automated and orchestrated using AWS Step Functions and AWS Lambda. After the model is successfully deployed on the SageMaker endpoint, incoming pictures from the cameras are resized to fit the model’s input format and then fed into the endpoint for predictions using Lambda functions. The result of the semantic segmentation prediction as well as the overflow detection are then stored in Amazon DynamoDB and Amazon S3 for downstream analysis. If overflow is detected, Amazon Simple Notification Service (Amazon SNS) or Lambda functions can be used to automatically mitigate the overflow and control the corresponding lanes on the affected screener. The following diagram illustrates this architecture.
This post described how ICL, an Israeli mining company, developed their own computer vision approach for automated monitoring of mining equipment using cameras. We first showed how to address such a challenge from an organizational point of view that is focused on enablement, then we provided a detailed look into how the model was built using AWS. Although the challenge of monitoring may be unique to ICL, the general approach to build a prototype alongside AWS specialists can be applied to similar challenges, particularly for organizations that don’t have the necessary AWS knowledge.
If you want to learn how to build a production-scale prototype of your use case, reach out to your AWS account team to discuss a prototyping engagement.
About the Authors
Markus Bestehorn leads the customer engineering and prototyping teams in Germany, Austria, Switzerland, and Israel for AWS. He has a PhD degree in computer science and is specialized in building complex machine learning and IoT solutions.
David Abekasis leads the data science team at ICL Group with a passion to educate others on data analysis and machine learning while helping solve business challenges. He has an MSc in Data Science and an MBA. He was fortunate to research spatial and time series data in the precision agriculture domain.
Ion Kleopas is a Sr. Machine Learning Prototyping Architect with an MSc in Data Science and Big Data. He helps AWS customers build innovative AI/ML solutions by enabling their technical teams on AWS technologies through the co-development of prototypes for challenging machine learning use cases, paving their path to production.
Miron Perel is a Principal Machine Learning Business Development Manager with Amazon Web Services. Miron advises Generative AI companies building their next generation models.