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What is a Predictive Maintenance Model?

A predictive maintenance model uses sensors and IoT technology to monitor the condition of equipment and predict when it will need repairs or replacements. This equipment may be anything from personal cars to large factory machines.

The machine learning programs used to run these models tend to take either the regression approach or the classification approach. Regression produces more accurate results but relies on more data; classification best predicts equipment failure within a set time period.

Details

Why Is It Important to Have a Predictive Maintenance Model?

Predicting maintenance needs of equipment results in more efficient—and, therefore, safer and cheaper—use of equipment. All users and owners of the equipment benefit from these models.

These models can also identify the exact cause of the equipment failure or degradation in real time, so fixing the problem takes no time at all.

What Internal Data Should I Have for a Good Predictive Maintenance Model?

Predictive maintenance models require sensors that record, in real time, the condition of all parts of the machine.

The model should also have historical data on the condition of the equipment, in as much detail as possible.

What External Data Is Essential for a Good Model?

What may be considered essential or merely useful external data depends on the company or user. Personal vehicle manufacturers, for example, may consider weather data essential whereas managers of drywall manufacturing plants will not.

What External Data May Prove Useful for a Good Model?

On the other hand, the amount of useful external data is large. Supply chain data, in particular, allows managers and users to arrange for repairs and replacements even faster.

Another useful source of external data may come from individual equipment users, particularly if a sensor fails to record something for whatever reason.

Other external data, like the location of the closest repair shop or new security threats in the area again depend on the industry.

What Are the Main Challenges of this Use Case?

One of the greatest challenges of predictive maintenance models is that they require a great deal of data to work most effectively yet the users of the equipment are usually not data scientists who are trained to cleanse the data. Carefully setting up the predictive maintenance model from the outset with trained data scientists, however, should reduce a lot of the problems of inaccurate or redundant data.

Interesting Case Studies and Blogs to Look Into

Perfectial: Predictive Maintenance: How Machine Learning Models Help Reduce Prevent/Repair Costs and Break Limitations of Traditional Maintenance Approaches
Oracle: 5 Use Cases for Predictive Maintenance and Big Data

Tangible Examples of Impact

Over the last years, shipping companies are increasingly facing the impact of a fast-changing competitive environment due to digital transformation. External factors such as regulations, cyber security threats and fluctuating TCEs have been dictating the pace of digital adoption and push shipping companies to adopt technology to date. Digitalization in shipping is still treated on an ad-hoc use-case basis, lacking a holistic strategy for digital transformation. However, advances in technology and availability of data is now allowing companies to transition down the digitization path more quickly.

Hellenic Shipping News: Digital Transformation in Shipping Operations – From planned to predictive maintenance

Relevant datasets

IBM PAIRS Services

by ibm-the-weather-company

IBM PAIRS Services provides queryable geospatial and temporal data in the form of maps, satellite images, weather data, drone data, and other data. 

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CACI Space Operations and Resiliency

by caci-logo

CACI has over fifteen years of experience in protecting space missions and related infrastructure. Their Space Operations and Resiliency program does real-time data collection of launch systems, satellite constellations, foreign signals, and more. Combined with deep learning algorithms, data fusion, and data visualization, space missions can be defended from both human error and malice. 

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