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Posted by / 28-Sep-2019 04:58

When building predictive models, we use historical data to train the model which can then recognize hidden patterns and further identify these patterns in the future data.

These models are trained with examples described by their features and the target of prediction.

The majority of these problems can be categorized to fall under the following business questions: Predictive maintenance solutions can provide businesses with key performance indicators such as health scores to monitor real-time asset condition, an estimate of the remaining lifespan of assets, recommendation for proactive maintenance activities and estimated order dates for replacement of parts.

It is important to emphasize that not all use cases or business problems can be effectively solved by predictive maintenance.

The playbook covers both high-level aspects of the different types of predictive maintenance solutions and details of how to implement them.

One of the most popular of these solutions is called Predictive Maintenance which can generally be defined as but not limited to predicting possibility of failure of an asset in the near future so that the assets can be monitored to proactively identify failures and take action before the failures occur.

These solutions detect failure patterns to determine assets that are at the greatest risk of failure.

The first half of the playbook covers an introduction to predictive maintenance applications, how to qualify a predictive maintenance solution, a collection of common use cases with the details of the business problem, the data surrounding these use cases and the business benefits of implementing these predictive maintenance solutions.

These sections don’t require any technical knowledge in the predictive analytics domain.

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This early identification of issues helps deploy limited maintenance resources in a more cost-effective way and enhance quality and supply chain processes.

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