Predictive Maintenance—the First Step Toward Self-Maintenance and AI

How to pave the way for artificial intelligence and self-maintenance, by first optimizing your operations with predictive maintenance
By Kenneth Sanford

To get started, data science company Dataiku has published a free whitepaper titled "How To: Future-Proof Your Operations with Predictive Maintenance," which outlines the steps every organization needs to embrace to make predictive maintenance effective within the short term, and to prepare for the long-term changes and benefits it can bring:

1. Understand the Need
The first step in moving toward predictive maintenance is to understand pain points (namely, drivers of costs, waste or inefficiency) and identify the best use case for your business.

2. Get Data
Of course, the proliferation of the IoT plays a large role in predictive maintenance, especially with cheap sensors and data storage combined with more powerful data processing that has made the technology accessible. But there are other data sources out there, which might include data from programmable controllers; manufacturing-execution systems; building-management systems; manual data from human inspection; static data, such as manufacturer service recommendations for each asset; external data from application programming interfaces (APIs), like weather, that could impact equipment conditions or wear; geographical data; equipment usage history data; and parts composition.

3. Explore and Clean Data
After identifying relevant data sets, it's time to dig in. Ensure you really understand all the data you're dealing with and that you know what all of the variables mean, what is being measured and where all the data is coming from.

4. Enrich Data
Manipulating data at this stage means adding more features and joining it in meaningful ways so that each data set, or data from multiple sensors, can be taken as a whole instead of in parts.

5. Get Predictive
It is precisely this combination of a variety of sources and data types that allows for the most robust and accurate predictive models. The more sources and types of data available, the better the complete picture of a particular asset in general, and the better the prediction.

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