Reviewing inference results in a JSON file

The JSON file containing the inference results is stored in the Amazon Simple Storage Service (Amazon S3) bucket that you've specified.

For the sensor data that your asset sends to Amazon S3, Amazon Lookout for Equipment marks the group of readings as either normal or abnormal. For each group of abnormal readings, you can see the sensors that Lookout for Equipment used to indicate that the equipment is behaving abnormally.

The following shows example JSON output.

{"timestamp": "2021-03-11T22:24:00.000000", "prediction": 0, "prediction_reason": "MACHINE_OFF"}
{"timestamp": "2021-03-11T22:25:00.000000", "prediction": 1, "prediction_reason": "ANOMALY_DETECTED", "anomaly_score": 0.72385, "diagnostics": [{"name": "component_5feceb66\\sensor0", "value": 0.02346}, {"name": "component_5feceb66\\sensor1", "value": 0.10011}, {"name": "component_5feceb66\\sensor2", "value": 0.11162}, {"name": "component_5feceb66\\sensor3", "value": 0.14419}, {"name": "component_5feceb66\\sensor4", "value": 0.12219}, {"name": "component_5feceb66\\sensor5", "value": 0.14936}, {"name": "component_5feceb66\\sensor6", "value": 0.17829}, {"name": "component_5feceb66\\sensor7", "value": 0.00194}, {"name": "component_5feceb66\\sensor8", "value": 0.05446}, {"name": "component_5feceb66\\sensor9", "value": 0.11437}]}
{"timestamp": "2021-03-11T22:26:00.000000", "prediction": 0, "prediction_reason": "NO_ANOMALY_DETECTED", "anomaly_score": 0.41227, "diagnostics": [{"name": "component_5feceb66\\sensor0", "value": 0.03533}, {"name": "component_5feceb66\\sensor1", "value": 0.24063}, {"name": "component_5feceb66\\sensor2", "value": 0.06327}, {"name": "component_5feceb66\\sensor3", "value": 0.08303}, {"name": "component_5feceb66\\sensor4", "value": 0.18598}, {"name": "component_5feceb66\\sensor5", "value": 0.10839}, {"name": "component_5feceb66\\sensor6", "value": 0.08721}, {"name": "component_5feceb66\\sensor7", "value": 0.06792}, {"name": "component_5feceb66\\sensor8", "value": 0.1309}, {"name": "component_5feceb66\\sensor9", "value": 0.07735}]}
{"timestamp": "2021-03-11T22:27:00.000000", "prediction": 0, "prediction_reason": "NO_ANOMALY_DETECTED", "anomaly_score": 0.10541, "diagnostics": [{"name": "component_5feceb66\\sensor0", "value": 0.02533}, {"name": "component_5feceb66\\sensor1", "value": 0.34063}, {"name": "component_5feceb66\\sensor2", "value": 0.07327}, {"name": "component_5feceb66\\sensor3", "value": 0.03303}, {"name": "component_5feceb66\\sensor4", "value": 0.18598}, {"name": "component_5feceb66\\sensor5", "value": 0.10839}, {"name": "component_5feceb66\\sensor6", "value": 0.08721}, {"name": "component_5feceb66\\sensor7", "value": 0.06792}, {"name": "component_5feceb66\\sensor8", "value": 0.1309}, {"name": "component_5feceb66\\sensor9", "value": 0.07735}]}
{"timestamp": "2021-03-11T22:28:00.000000", "prediction": 0, "prediction_reason": "NO_ANOMALY_DETECTED", "anomaly_score": 0.24867, "diagnostics": [{"name": "component_5feceb66\\sensor0", "value": 0.04533}, {"name": "component_5feceb66\\sensor1", "value": 0.14063}, {"name": "component_5feceb66\\sensor2", "value": 0.08327}, {"name": "component_5feceb66\\sensor3", "value": 0.07303}, {"name": "component_5feceb66\\sensor4", "value": 0.18598}, {"name": "component_5feceb66\\sensor5", "value": 0.10839}, {"name": "component_5feceb66\\sensor6", "value": 0.08721}, {"name": "component_5feceb66\\sensor7", "value": 0.06792}, {"name": "component_5feceb66\\sensor8", "value": 0.1309}, {"name": "component_5feceb66\\sensor9", "value": 0.07735}]}
{"timestamp": "2021-03-11T22:29:00.000000", "prediction": 1, "prediction_reason": "ANOMALY_DETECTED", "anomaly_score": 0.79376, "diagnostics": [{"name": "component_5feceb66\\sensor0", "value": 0.04533}, {"name": "component_5feceb66\\sensor1", "value": 0.14063}, {"name": "component_5feceb66\\sensor2", "value": 0.08327}, {"name": "component_5feceb66\\sensor3", "value": 0.07303}, {"name": "component_5feceb66\\sensor4", "value": 0.18598}, {"name": "component_5feceb66\\sensor5", "value": 0.10839}, {"name": "component_5feceb66\\sensor6", "value": 0.08721}, {"name": "component_5feceb66\\sensor7", "value": 0.06792}, {"name": "component_5feceb66\\sensor8", "value": 0.1309}, {"name": "component_5feceb66\\sensor9", "value": 0.07735}]}

For the prediction field, a value of 1 indicates abnormal equipment behavior. A value of 0 indicates normal equipment behavior.

If the value of prediction_reason isn't MACHINE_OFF, Amazon Lookout for Equipment returns an object that contains a diagnostics list, regardless of the value of prediction. The diagnostics list has the name of the sensors and the weights of the sensors' contributions in indicating abnormal equipment behavior. For each sensor, the name field indicates the name of the sensor. The value field indicates the percentage of the sensor's contribution to the prediction value. By seeing the percentage of each sensor's contribution to the prediction value, you can see how the data from each sensor was weighted.

The anomaly score is a value between 0 and 1 that indicates the intensity of the anomaly.

The prediction reason can be ANOMALY_DETECTED, NO_ANOMALY_DETECTED or MACHINE_OFF.