Keeping industrial systems operational is like spinning plates – you’re kept busy trying to avoid any falling and breaking. While preventative maintenance regimes, optimally implemented, work very well, critical equipment must still be taken offline regularly for servicing. Additionally, wastage due to replacing parts and lubricants earlier than necessary is costly and bad for the environment. Predictive maintenance is increasingly being used to optimize this essential function, ranging from static, dynamic, and statistical analysis to the use of machine learning to prognose pending failure promptly. The overall goal: to maximize uptime and reduce maintenance costs.