AT GROUND LEVEL
As industries move towards Industry 4.0, Anais Dotis-Georgiou, Developer Advocate, InfluxData, makes the case for predictive maintenance – enabled by better control and the manipulation of data.
Ahead of the curve: Time series data and its role in predictive maintenance
W hile GenAI has captured interest over the past few years, Machine Learning( ML) remains the most advanced branch of AI.
In simple terms, ML uses algorithms to identify trends in data and predict when similar patterns will occur. Perhaps not surprisingly, financial services institutions were among the early adopters of this technology, but it is now used in a wide variety of verticals. Some of the most noteworthy applications are in the manufacturing sector, where it helps to reduce machine downtime massively by predicting when equipment failure might occur.
Broadly speaking, a manufacturer has two options when using ML for predictive maintenance. One is to take a moderated approach that relies on human intervention( essentially doublechecking with an in-person inspection), and the other is to take a more automated approach using“ Deep Learning”. At its most basic, Deep Learning enables software to define its normal operation and isolate anomalies without prompts and self-teach without human moderation.
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