Challenge #1: Predictive Maintenance
We are developing a complete platform for anomaly detection which can take data for several source devices and perform analyses to detect anomalies in each of them.
Intelligently predict when equipment will fail before it does
Using AI condition monitoring to detect various anomalies, signs of deterioration, and other equipment performance issues
Solve the unexpected equipment failures with intelligent algorithms that provide maintenance managers with the information they have always dreamed of having
Lowers operational costs, minimizes downtime, & improves overall asset health & performance
We are also developing a state-of-the-art event-based data processing backend that allows us to collect data from thousands of sensors and perform analyses on them.
3. Source link code
Machine learning repository: LINK
Event streaming repository: LINK
4. Tech stack
What we have used:
Collaboration Tools: Notion (Notes/ToDo’s/Kanban Board), Slack (Communication), GitHub (share code)
Data Exploration & Machine Learning: Weka 3.8.6, Google Colab Notebooks, Keras/Tensorflow
Data Streaming/Analytics: Apache Kafka deployed on Amazon AWS or Microsoft Azure
API communication: REST API build on FastAPI for pushing collected data to Data Streaming
What we should use if this project advances:
Data-Collection: MQTT (Lightweight Protocol for M2M Communication & IoT Data) / CoAP (HTTP)
IoT-/Data-Platform: Amazon AWS Greenfield IoT / Microsoft Azure IoT
Standalone Timeseries-Database: Cassandra / Timescale / PostgreSQL
5. Target profile
We are targeting the manufacturing sector. That is, our customers could be manufacturers of industrial machines and robots. The customer could also be the users of said devices.
Dashboard with Data