Prepare for AI with predictive maintenance
Jennifer Roubaud, the UK and Ireland country manager for Dataiku, explains how to pave the way for artificial intelligence (AI) and self-maintenance by first optimising your operations with predictive maintenance.
Predictive maintenance is widely considered to be the obvious next step for any business with high-capital assets looking to harness machine learning to control rising equipment maintenance costs. Predictive maintenance takes data from multiple and varied sources, combines it, and uses machine learning techniques to anticipate equipment failure before it happens.
Many businesses are already using continuous monitoring technologies – like Internet of Things (IoT) connected devices – which is a good start; but the key lies in not just simply monitoring the output of various data – which is how many companies use it today, but by taking the next step and employing advanced algorithms and machine learning to take action from real-time insights.
Going one step further, the most innovative enterprises, no matter what type of high-capital assets they maintain, see the largest cost savings from predictive maintenance not only by putting a system in place that returns simple predictive outputs, but by rethinking and optimising their entire maintenance strategy as a whole from top to bottom. This means:
- Paving the way for artificial intelligence (AI) and self-maintenance by optimising for (and automating) the immediate next steps once predictive systems point to imminent failure, whether this automatically triggers a work order, notifies a technician or certain team, places an order for a replacement part, etc.
- Considering a combination of maintenance strategies to determine the optimal cost-saving combination of predictive and traditional maintenance, perhaps even on an asset-by-asset basis.
- Identifying how to best execute necessary repairs through second-order or secondary analytics, meaning having a process in place for an entire deeper layer of analysis to determine the best time to actually remove the asset from service and which additional repairs – if any – should be conducted simultaneously to minimize the cost of having to remove the asset again for a different failure within a short window.
To get started, data science company, Dataiku, has published a free whitepaper “How To: Future-Proof Your Operations with Predictive Maintenance” outlining the steps every organisation needs to embrace to make predictive maintenance effective within the short term and also to prepare for the long-term changes and benefits it can bring. The biggest initial win with predictive maintenance initiatives is cost savings. But after implementing a larger, more robust, and more mature predictive maintenance strategy, larger opportunities begin to open from a business perspective, and high-value assets can bring in some additional revenue instead of just being costs.
Predictive maintenance also lends itself to the future of AI, where operations will be entirely self-maintenance with very little human interaction whatsoever. AI in the predictive maintenance space would go one step beyond the steps discussed above, which would still require some manual analysis of models and outputs. These systems will watch thousands of variables and apply deep learning to find information that could otherwise be undetected that might lead to failure. Ultimately, predictive maintenance isn’t so far off from AI, and businesses that get started with predictive maintenance programs now will be well-poised as market leaders in the future.