The aim of this project is to build a predictive maintenance support system using machine learning and artificial intelligence techniques. Throughout the project, a system will be designed and implemented to load the partner's data and build, train and deploy the artificial intelligence model. An interface will also be built to enable system users to consult and feed the model used, as well as visualizing the data with a dashboard.
APPLICATION EXAMPLES:
- Predictive maintenance system in hospitals: Machines such as MRI and CT scanners are essential for treating patients, predictive maintenance uses data and sensors to detect wear and schedule maintenance, preventing unexpected failures. This saves resources and extends the life of medical equipment;
- Predictive maintenance systems for card machines: Predictive maintenance for payment devices, such as card machines ("maquininha" or PDQ), uses sensors and machine learning algorithms to detect signs of wear and predict when maintenance is required;
- Predictive maintenance system for productive machines: Predictive maintenance on industrial equipment helps reduce costs and prevent equipment failures by analyzing sensor data and using machine learning algorithms to predict when maintenance is required.
MANDATORY INFRASTRUCTURE RESOURCES:
- Programming languages and technologies for the machine learning application, frontend, backend and cloud deployment: C, C++, Python, JavaScript, HTML, CSS and others that are relevant to the application developed;
- Access to a cloud platform with functionality for data engineering and machine learning pipelines;
- Version control system: Git with distribution and collaboration on the Github platform;
- The proof of concept must necessarily have a backend, frontend and a data engineering pipeline with the application of machine learning;
- Frameworks, libraries and/or software for implementing data visualization systems in dashboard format;
- Access to a dataset with annotations and of a size suitable for training machine learning models for the application in question.
COMPLETION AND DELIVERY OF THE PROJECT:
All the prototypes generated during the project are delivered at the end of the 10th week.
