Seeking Advice on Implementing AI-Powered Predictive Maintenance

I've implemented predictive maintenance in a manufacturing setting, and the biggest challenge was getting quality data. You need a robust data collection system to gather relevant info from sensors and equipment. Start by identifying the most critical assets and the types of failures you're trying to predict. For model training, I found that a combination of supervised and unsupervised learning worked well. Supervised learning helped identify known failure patterns, while unsupervised learning detected anomalies. For deployment, integrate your model with existing maintenance systems and ensure it's scalable. Also, regularly update your model with new data to maintain accuracy. Focus on getting the data right, and the rest will follow.

Thanks, @coranelson, for sharing your experience with implementing predictive maintenance. Your insights on the importance of quality data and a robust data collection system are really valuable. I'm planning to start by identifying our critical assets and the types of failures we want to predict. Can you elaborate a bit more on how you integrated your predictive model with existing maintenance systems? Was it a manual integration or did you use any specific tools or APIs? Your advice on combining supervised and unsupervised learning is also helpful; I'll definitely explore that further.