As mentioned in our previous post, Industry 4.0 uses data science to perform predictive maintenance: proactive decision-making based on production floor data, derived from IoT sensors that can detect what happens inside specific industrial assets.
Machine learning and deep learning models are able to identify performance issues in real time, allowing for preventive, cost-effective maintenance by fixing issues ahead of asset failure or damage.
Here are two additional aspects where optimizing AI models can lead to better predictive maintenance solutions.
Getting the metrics right
AI model metrics typically classify problems using the classic confusion matrix (true/false, positive/negative), with some models placing the importance on true instances (TP & TN) and other on false instances (FN & FP).
False or excessive alerts can mean losing the trust of the technician/engineer/manager, who naturally begins to ignore even relevant alerts that could prevent downtime and financial loss. To assure buy-in and speedy response from the user, models should be built to send alerts that are as timely and relevant as possible.
Involving the customer in development and production
To promote successful adoption of predictive maintenance technologies, one effective approach is to involve the customer – who, after all, knows his/her production floor assets better than anyone – as part of the model building process.
Customer input may include the best assets on which to alert, the desired frequency of reminders, and of course the alert threshold parameters, such as occurrence density, predicated financial loss, and more. This way, alerts and conditions defined in collaboration with the client will enjoy their trust, and ultimately lead to better outcomes for everyone.
The more alerting conditions are optimized, the higher the chances of receiving critical alerts when needed. And this collaboration is ongoing: AI companies running at scale depend on repeated client feedback to retrain the model for better performance now and in the future.