A Systematic Approach for Predictive Analytics
In the journey towards Smart Manufacturing through Digitization, we find ourselves mired in too many variables to analyze and too much data to deal with. Big Data gets bigger and soon we are swimming in our own Data Lake. Often there are self-doubts as to the direction we are going, and we tend to have second thoughts about the end results. It starts to feel like we are boiling the ocean especially if the results are not promising. It is crucial that we focus on the concrete steps to move from the start to finish of a Predictive Analytics Project. The result is a successful deployment of Predictive Maintenance (worry-free uptime) and Predictive Quality (near-zero defects). These two areas are the low hanging fruit and the quickest wins for Smart Manufacturing today. The time has come where the research in Artificial Intelligence and Data Analysis Techniques are now available as a packaged product with solution templates that can be applied in practical applications. The six steps towards a successful implementation of a “Predictive Analytics” project in manufacturing are the following:- Data Collection (what are the critical machine or sensor parameters for that application?)
- Data / Signal Processing (how do we clean and prepare the data for analysis?)
- Feature Extraction and Selection (what are the statistics derived from the raw data?)
- Health Assessment (what is your machine health?)
- Prediction (what is the remaining useful life of your machine?)
- Diagnostics (what is the root cause of failure?)
