Choosing the right application
Choosing the right application of Lookout for Equipment involves finding the right combination of business value, equipment operations, and available data. You determine this by working directly with a subject matter experts (SME) on your equipment. Your team should consider the following:
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The high cost of downtime – Equipment that can either be costly to fix or that is critical to a process is a prime candidate for monitoring.
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Consistency in operations – Lookout for Equipment works best on equipment that is stationary and primarily does a continuous, stable task. A heavy duty pump that is permanently installed in a location is a good example.
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Relevant data – Having data that is relevant to the critical aspects of the equipment is essential. Your equipment should have sensors that monitor these critical aspects, so that they can provide data that is relevant to how your equipment could fail. Having this data can make the difference between inference results that can effectively catch potential failures and abnormal behavior, and results that don't.
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Significant historical data – Ideally, the data you use to train the machine learning (ML) model should represent all of the equipment's operating modes. For instance, when creating a model for a pump with variable speeds, the dataset should contain measurements that include an adequate amount of historical data for all of the pump speeds. For effective analysis, Lookout for Equipment should have at least six months of historical data, although a longer history is preferred. For equipment affected by seasonality, at least one year of data is highly recommended.
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List of historical failures (that is, labels) – Lookout for Equipment uses data on historical failures to enhance the model's knowledge of normal equipment conditions. It looks for abnormal behavior that occurred ahead of historical failures. With more examples of historical failures, Lookout for Equipment can better develop its knowledge of healthy conditions and the unhealthy conditions that occur prior to failures. The definition of a failure can be subjective, but we have found that looking for issues that cause unplanned downtime is a good method to identify failure. For best results, give Lookout for Equipment label data for every known time period where the equipment had issues or abnormal behavior.
Note
Lookout for Equipment is ultimately dependent on your data. We cannot guarantee that there are patterns in your data that will enable Lookout for Equipment to detect failures. Determining the right set of inputs might require multiple iterations through the Lookout for Equipment model training and monitoring process. For the greatest chance of success, we highly recommend working with a subject matter expert to identify the right application and data.
