Predictive Maintenance in Transportation and Manufacturing

Machine-learning based predictive maintenance is critical for any business with high-capital assets, especially the transportation and manufacturing sectors, as it harnesses the power of AI to control rising equipment maintenance costs. Where a machine learning approach adds value is that the system can take data from a variety of sources, combine it, and apply algorithms that anticipate equipment failure before it occurs, often more accurately than a human.

Predictive maintenance often allows the detection of impending failures that could never be detected by human eyes. With predictive maintenance, downtime and repairs are directly tied to likely failure, minimizing cost  (e.g. less labor time, less chance of unexpected failure) and maximizing asset life. By contrast, traditional maintenance techniques (run-to-failure, preventative, or some combination of the two) inevitably mean unexpected repair, which leads to longer downtime on top of unnecessary downtime due to regular inspection.

PwC defines Predictive Maintenance 4.0 as continuous real-time monitoring of assets, with alerts sent based on predictive techniques, such as regression analysis. They estimate that only 11 percent of companies have already achieved this level of predictive maintenance.

The Technology

Machine learning-based predictive maintenance stands out because it doesn’t only deal in the past tense, but rather focuses on future behavior and how that can impact an organization. Models are able to use recent data – sometimes even real-time data – to predict future asset performance or issues at any given time and have a feedback loop in place to act on those predictions.

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Once data from a variety of sources is combined, predictive algorithms, analytics, and machine learning can be applied to produce an accurate model that attempts to predict failures. For example, a predictive maintenance model for a vehicle would (at least) contain information on previous maintenance records, any failure events that have occurred, and amount of time in service.

Even once a predictive maintenance model is pushed into production, that’s not the end. Models should always be improving (i.e., retrained) based on how their predictions match up with reality. Also, while machine learning models alone are powerful, they are often unable to generate value unless they are well activated and integrated into the broader organization, so a data-driven culture is critical to successful predictive operations and maximum value creation.

Dataiku and Predictive Maintenance

Dataiku is the platform democratizing access to data and enabling manufacturing and transportation organizations to build their own path to AI. Practically speaking, Dataiku provides the following features for predictive maintenance projects:

• Whether data comes from physical sensors, structured maintenance reports, or human commentary (natural language), Dataiku supports regular automatic updates and data ingestion.
• The Dataiku visual flow makes cleaning and processing datasets easy, even for those that are not data scientists. Structured elements like dates or emails are automatically identified for easier processing.

• While historical records documenting failures or clean maintenance logs would indicate known (labeled) data points, new data coming in is unknown (or unlabeled) since users need the model to indicate whether it points to an imminent failure. Dataiku helps teams create supervised and unsupervised machine learning models, with or without coding.
• With ranked scoring, prediction maintenance models can help teams prioritize what to repair or replace first. The whole data pipeline, from cleaning to analysis, can occur within the centralized Dataiku environment. Depending on the quantity of data, retraining can take anywhere from a few seconds to a few hours; however Dataiku supports automation so this training can run, for example, at night, so teams can analyze fresh insights in the morning.