Improving maintenance outcomes with machine learning

Mike Brooks proposes the use of machine learning software to improve plant reliability and to reduce unplanned downtime. There is a significant need to carry out failure prevention using data-driven truths instead of guesstimates, evidenced by the fact that a combination of mechanical and process induced breakdowns account for up to 10% of the worldwide $1.4 trillion manufacturing market, according to a 2012 report from The McKinsey Global Institute. While companies have spent millions trying to address this issue and ultimately avoid unplanned downtime, only recently have they been able to address wear and age-based failures. Current techniques are not able to detect problems early enough and lack insight into the reasons behind the seemingly random failures that cause over 80% of unplanned downtime. This is where using machine learning software to cast a'wider net' around machines can capture process induced failures.