Evaluating the output

After a model is trained, Lookout for Equipment evaluates its performance on a subset of the dataset that you've specified for evaluation purposes. It displays results that provide an overview of the performance and detailed information about the abnormal equipment behavior events and how well the model performed when detecting those.

Using the data and failure labels that you provided for training and evaluating the model, Lookout for Equipment reports how many times the model's predictions were true positives (how often the model found the equipment anomaly that was noted within the ranges shown in the labels). Within a labeled time range, the forewarning time represents the duration between the earliest time when the model found an anomaly and the end of the labeled time range.

For example, if Lookout for Equipment reports that "6/7 abnormal equipment behavior events were detected within label ranges with an average forewarning time of 32 hrs," in 6 out of the 7 labeled events, the model detected that event and averaged 32 hours of forewarning. In one case, it did not detect the event.

Lookout for Equipment also reports the abnormal behavior events that were not related to a failure, along with the duration of these abnormal behavior events. For example, if it reports that "5 abnormal equipment behavior events were detected outside the label range with an average duration of 4 hrs," the model thought an event was occurring in 5 cases. An abnormal behavior event such as this one might be attributed to someone erroneously operating the equipment for a period of time or a normal operating mode that you haven't seen previously.

Lookout for Equipment also displays this information graphically on a chart that shows the days and events and in a table.

Lookout for Equipment provides detailed information about the anomalous events that it detects. It displays a list of sensors that provided the data to indicate an anomalous event. This might help you determine which part of your asset is behaving abnormally.