Use Anomalies to Track Deviations in Machines and Factories

When the set parameters of a machine or factory do not conform to a regular pattern, an anomaly occurs. An anomaly can help you identify and resolve potential problems with your machines and factories.

Use anomalies to detect deviations from normal behavior, and to flag and address issues in time. As a factory manager, you can also detect anomalies for data streams where the data is not normally distributed (non-Gaussian distribution).

You can define the following types of anomalies:

  • Automatic Anomaly: Use an automatic anomaly to automatically look for deviations in a sensor or metric (KPI) value. For example, you can raise an anomaly when the total number of idle machines in a factory exceeds the acceptable value. See Define an Automatic Anomaly for more information on defining automatic anomalies.

  • User-Defined Anomaly: Create a user-defined anomaly to look for telltale patterns in sensor or metric data generated by a machine. For example, you may create anomalies to look for vibration anomalies in a packaging machine. User-defined anomalies are based on acceptable or anomalous data patterns. You train the system by providing it with samples of acceptable data or anomalous data. These samples can come from sensor or metric data.

    For acceptable data, you specify a time window containing acceptable patterns of sensor or metric data. The time window is a period of typical operations during which your machines, and associated sensors, behaved normally. The system uses the data pattern that you select to train itself. During day-to-day operations, the system looks out for deviations in data patterns that are beyond the specified deviation percentage, and flags these as anomalies.

    For anomalous data, you train the system by providing it with samples of anomalous data patterns from existing sensor or metric data.

    See Create a User-Defined Anomaly for more information on defining user-defined anomalies.

View Anomalies

View the anomalies for a factory to identify any unusual behavior that might affect the performance of your factory.

In the Factory view, select the Anomalies Anomalies icon tab. Then select a period to view, you can view the anomalies for the last hour, the last day, the last week, or the last month. Use the arrows to view the next or the previous anomaly.

Anomalies are displayed with a different color and a dotted line. The solid lines shows the normal values. Use the normal values to understand the importance of the anomalies you are viewing.

The horizontal axis shows the time so that you can identify the time at which the anomaly occurred. Browsing through the different anomalies you can also find out their frequency.

This image shows you the available actions in the Anomalies view:

This image is described in the surrounding text.

Define an Automatic Anomaly

Define an automatic anomaly to monitor a specific attribute (sensor value or metric), and to send notifications when that metric deviates from its usual values.

  1. Click Menu Menu icon and select Configuration.
  2. Click the Anomalies Anomalies icon tab.
  3. Click Add Add icon.
  4. Enter a name for the anomaly in the Name field.
  5. (Optional) Specify an optional description text for the anomaly.
  6. Under Detection Target, select the Machine Type for your anomaly.

    The anomaly applies to all machines of the chosen machine type.

    Note that you can also select All Factories if you wish to choose a pre-defined factory metric as the attribute.

  7. Select an available Attribute to monitor.

    The list of attributes includes sensor attributes and query-type (computed) metrics.

    If you selected All Factories under Machine Type, then the following list of pre-defined factory metric attributes is available:
    • Total Machines Down: Number of unavailable machines in the factory.

    • Total Idle Machines: Number of machines in idle state in the factory.

    • Machines in Use Percentage: Percentage of machines in use in the factory.

    • Machines Idle Percentage: Percentage of machines in idle state in the factory.

    • Machines Down Percentage: Percentage of unavailable machines in the factory.

    • Total In Use Machines: Number of machines in use in the factory.

  8. Under Training Data, select Automatic Anomaly.

    Use an automatic anomaly to automatically look for deviations in a sensor or metric (KPI) value. For example, automatic anomalies can help detect a machine that is overheating intermittently.

  9. Specify a Training Window period.

    This is the amount of historical data used to train the system for anomaly detection.

  10. From the Deviation Threshold list, select the acceptable standard deviation multiple from the mean. Attribute values exceeding this will result in anomalies.
  11. Click Save.
  12. Click Republish to publish and deploy the anomaly.

Create a User-Defined Anomaly

Create a user-defined anomaly to look for patterns in sensor data generated by a machine.

  1. Click Menu Menu icon and select Configuration.
  2. Click the Anomalies Anomalies icon tab.
  3. Click Add Add icon.
  4. Enter a name for the anomaly in the Name field.
  5. (Optional) Specify an optional description text for the anomaly.
  6. Under Detection Target, select the Machine Type for your anomaly.

    The anomaly applies to all machines of the chosen machine type.

  7. Select an available Attribute to monitor.

    The list of attributes includes sensor attributes and query-type (computed) metrics.

  8. Under Training Data, select User Defined Anomaly.

    Create a user-defined anomaly to look for telltale patterns in sensor or metric data generated by a machine. For example, you may use manual anomalies to look for vibration anomalies in a packaging machine. User-defined anomalies are based on acceptable or anomalous data patterns. You train the system by providing it with samples of acceptable data or anomalous data. These samples can come from sensor or metric data.

  9. Select a Specimen Machine that provides the data pattern for anomaly detection.

    A list of all machines with the selected machine type appears. The machine with the most data is chosen by default. You can choose a different machine if required.

  10. Choose a Selection Type, and complete the corresponding steps.
    • Choose Anomalous Data to select an anomalous data pattern from existing sensor or metric data.

      1. (Optional) Change the Data End Time for the chart, if required. The current date and time are automatically populated.

      2. Click Generate Chart to display the sensor or metric data for the selected attribute and asset.

        The data plot for the selected machine attribute appears.

      3. Use the mouse to select the anomaly pattern in the data plot.


        Anomaly selection

        You can zoom in and zoom out in the data plot area. You can also navigate along the time axis using the Next and Previous buttons.

        If you wish to change the selected pattern, you can select another pattern in the data plot and the first pattern is deselected.

      4. Click Save to save the anomaly.

      5. Click Publish to deploy the anomaly.

    • Choose Acceptable Data to select acceptable or non-anomalous data from existing sensor or metric data.

      1. Select a Deviation Percentage.

        This is the percentage of deviation required to trigger an anomaly.

      2. Specify a Data Start Time and Data End Time to plot the chart.

        This is the broad time period that contains acceptable, or non-anomalous, attribute data.

      3. Click Generate Chart to display the sensor or metric data for the selected attribute and time period.

        The data plot for the selected asset attribute appears.

      4. Click within the left-half chart to select the start time.

        This marks the beginning of acceptable, or non-anomalous, data.

      5. Click within the right-half chart to select the end time.

        This marks the end of the sample (acceptable) data.


        Selecting acceptable data

      6. Click Save to save the anomaly.

      7. Click Publish to deploy the anomaly.