As industries move towards Industry 4.0, Anais Dotis-Georgiou, Developer Advocate, InfluxData, makes the case for predictive maintenance – enabled by better control and manipulation of data.

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 double-checking with an in-person inspection), and the other is to take a more automated approach using‘ deep learning’. At its

For global manufacturers – estimated to lose almost $ 1.5 trillion every year due to unplanned downtime – the benefits of an ML-assisted deep learning approach can be game-changing.

Deep learning, deep automation

Deep learning accurately identifies historical patterns by ingesting and analyzing incredibly large and complex time series datasets( essentially metrics collected over time). These patterns are often ones that humans miss or are incapable of spotting. A time series-optimized database can also ingest and learn from unlabeled or unstructured data – meaning it can quickly adapt to changing environments and new scenarios.

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