Predictive maintenance solutions: why are they important and how to implement them?
This article explains the importance of predictive maintenance solutions and how to implement them
For companies that have been collecting machine data for years; there is an incredible opportunity to make that data actionable. Using actionable data can provide an invaluable competitive advantage by allowing companies to streamline operational processes, optimize demand forecasting, and better understand their customers' propensity to buy.
In particular, predictive maintenance (PdM) solutions are key benefits of making machine data actionable; as it can reduce downtime and waste, leading to greater organizational efficiency.
Indeed, turning the idea of predictive maintenance into an actual deployment can be complex; but there are several best practices that can help achieve results early in the process. For example; it is best to start small to learn a repeatable process on a dataset focused on a single use case. This exposes all stakeholders to the required steps and can help frame future discussions about predictive maintenance projects.
So if you are an organization that wants to create a PoM pilot, start by following these six steps:
1. Determine the scope of use of predictive maintenance solutions
First, the goal of any PdM pilot should be to show that you have a dataset with a high probability of providing actionable insights that can lead to a specific business outcome. Otherwise; your PdM use case will not make it out of research and development. To determine if a dataset exists to support your use case, ask yourself the following questions:
- Do we have enough data - both historically and currently generated - to tell the full story of the machine? (This may involve data sets from a few machines running for a few years or data sets from many machines running for a shorter period of time.)
-Can we access this data from the factory floor? (For example, can we download historical data or connect machines via IoT gateways to start publishing data?)
-Do we have other data sources that can augment this data; such as log files; maintenance records or weather data?
-Do we have experts available who can describe the success or failure patterns of a particular machine?
-What is the desired business outcome? (For example, is our goal to increase margins; reduce downtime; or provide new offerings to customers?)
2. Aggregate and organize relevant data sets
Once your use case is defined, the next step is to aggregate your data in a centralized location.
This process generally involves two phases:
-The first is to download historical data sets to populate the models. These data may live in various locations and generally require a unique effort for each dataset.
-The second phase is to set up the systems to display the data continuously. Depending on connectivity, this can be done in batches or readings as they come in.
3. Explore the data for insights
Now it's time to start exploring the questions you can answer with your data sets.
Thus, the presence of subject matter experts is essential here, because no one else will know the behavior of machines better. Work with them to establish what patterns can be represented by the data in question and determine what real-world problems your experts face can be helped or even solved by having a predictive model.
4. Develop, test and iterate machine learning models of machine learning ML
Once you have defined your use case and asked the necessary questions about your data, you can start developing machine learning models, test those models, and repeat this process for as many scenarios as possible.
Indeed, the result of this phase may be to realize that the questions asked of the data are not appropriate for predictive models, or it may be to discover a model that is worth testing on a live machine.
5. Deploy to a controlled group of machines
Now it's time to validate the success of your machine learning model by deploying it on a group of machines. Depending on what you are trying to prove; this may involve a few models running on multiple clusters or the same model running on a single cluster.
So no matter how the deployment process looks, it is essential to have a measurable result.
Notably, also keep in mind that the goal of creating a PoM pilot should not be simply to produce a deployable machine learning model. Rather, it should help refine the internal processes needed to turn concepts or ideas into actual models; there will undoubtedly be other projects in your company's future that require the development of more machine learning models for other use cases.
6. Put your predictive maintenance model into production
After going through the steps above to complete your pilot project and get success metrics for your machine learning model, it's time to deploy your machine learning model to all machines in your organization.
Then, you will begin to receive actionable information that can directly impact both daily tasks and long-term goals.
Depending on the amount of historical machine data available and the complexity of the questions you want to answer.
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