Predictive Maintenance — a dive into DB2 and Watson Studio
MarketsandMarkets forecasts the global predictive maintenance market size to grow from USD $3.0 billion in 2019 to USD $10.7 billion by 2024.
Predictive maintenance is a strategy to directly monitor the condition of equipment and detect when performance is needed to minimize unplanned failures. It is one of the top applications of artificial intelligence and machine learning. Predictive maintenance is generally thought to be most applicable to the manufacturing industry since any equipment downtime is very costly to a manufacturer. To that same point, unnecessarily servicing equipment can also be expensive, as you might be paying someone to go waste time inspecting equipment that is functioning perfectly.
For this tutorial, you will need to download this hard drive data set which includes the date, serial number, model, failure, and a number of SMART parameters represented. SMART is a monitoring system built into most modern hard drives which stands for Self-Monitoring, Analysis and Reporting Technology. These systems are in placed to detect various reliability problems at an early stage, giving warning signs well in advance before the hard drive fails. By the end of this lab, you will be able to identify the likelihood of failure for a specific hard drive model.
Below are step-by-step instructions that go over how to import that data into DB2 and connect it to Watson Studio to generate an automated machine learning pipeline — all on IBM Cloud.
1. IBM Db2: Create a DB2 instance
IBM® Db2® Warehouse on Cloud is a managed public cloud service. You can set up IBM Db2 Warehouse on premises with your own hardware or in a private cloud. As a database warehouse, it includes features such as in-memory data processing and columnar tables for online analytical processing (OLAP). These deployment options have a common database engine so your data workloads can be moved and optimized with ease.
2. IBM Watson Studio: Let’s Create a Project
Watson Studio provides you with the environment and tools to solve your business problems by collaboratively working with data. You can choose the tools you need to analyze and visualize data, to cleanse and shape data, or to create and train machine learning models.
3. IBM Watson Studio: AutoAI (Watson Machine Learning)
AutoAI is a graphical tool in Watson Studio that automatically analyzes your data and generates candidate model pipelines customized for your predictive modeling problem. These model pipelines are created over time as AutoAI analyzes your dataset and discovers data transformations, algorithms, and parameter settings that work best for your problem setting. Results are displayed on a leaderboard, showing the automatically generated model pipelines ranked according to your problem optimization objective.
4. IBM Watson Studio: Deployment
Congratulations!
You have successfully finished the lab and deployed an automated predictive maintenance model with DB2 and Watson Studio.
IBM is helping companies across industries apply predictive maintenance to improve business performance. Check out these 5 IBM client examples demonstrating how predictive maintenance in the cloud is helping businesses from five different industries excel.
Feel free to connect with me on Linkedin or email me directly at vincent.cheng@ibm.com if you have any questions about this lab.
Check out this tutorial by Parker Merritt where he goes over how to connect IBM Cloud Pak for Data to an Amazon Web Services S3 data source to prepare data for analysis, and generate a similar automated AI pipeline.
Disclaimer: All data collected from this tutorial was pulled directly from an external public source and used for informational purposes only.