Current collected state Current
Observed since 20 Oct, 19:03 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
This is python package used detect different types of anomalies in compressors' sensors time series data.
This is a simple example package. You can use GitHub-flavored Markdown to write your content.
| Anomaly type | Type code | Description | Example in the literature and/or alternative terminology | Importance ranking (with respect to time series visualization in this case study) | Potential end-user and application |
|---|---|---|---|---|---|
| Impossible values | A | Values 6666 are highly impossible in compressor sensor data and consider as out of range value of the sensors. | - | Important, but it can be deleted easily using simple rule | - |
| Constant values within the acceptable region for long time period | B | Constant values within the acceptable region for long time period (more than one day) | - | Important can be detected using rate of change | - |
| Large sudden spike | C | The spikes are larger than the surrounding data points but less than 6666. Example one or two values much higher than previous or next values | - | At any point in the time series, used 3 times standard deviation as threshold | - |
| Drift | D | Gradual change in values in positive to zero | - | High priority (Emergency shutdown, ESD) | - |
| Sudden zero | E | Value suddenly shifts in values in positive to zero | - | High priority (Emergency shutdown, ESD) | - |
Table 1: Load current related anomaly types
| Anomaly type | Type code | Description | Example in the literature and/or alternative terminology | Importance ranking (with respect to time series visualization in this case study) | Potential end-user and application |
|---|---|---|---|---|---|
| Impossible values | A | Values 6666 are highly impossible in compressor sensor data and consider as out of range value of the sensors. | - | Important, but it can be deleted easily using simple rule | - |
| Constant values within the acceptable region for long time period | B | Constant values within the acceptable region for long time period (more than one day) | - | Important can be detected using rate of change | - |
| Large sudden spike | C | The spikes are larger than the surrounding data points but less than 6666. Example one or two values much higher than previous or next values | - | At any point in the time series, used 3 times standard deviation as threshold | - |
| Drift | D | Gradual change in values in positive to zero | - | High priority (Emergency shutdown, ESD) | - |
| Sudden zero | E | Value suddenly shifts in values in positive to zero | - | High priority (Emergency shutdown, ESD) | - |
| Step up (sudden shift) | F | Increase the mean while keeping the variance constant | - | High priority (Emergency shutdown, ESD) | - |
| Step down (sudden shift) | G | Decrease the mean while keeping the variance constant | - | Normal, indicate motor flips from OFF to ON | - |
Table 2: Suction pressure related anomaly types
| Anomaly type | Type code | Description | Example in the literature and/or alternative terminology | Importance ranking (with respect to time series visualization in this case study) | Potential end-user and application |
|---|---|---|---|---|---|
| Impossible values | A | Values 6666 are highly impossible in compressor sensor data and consider as out of range value of the sensors. | - | Important, but it can be deleted easily using simple rule | - |
| Constant values within the acceptable region for long time period | B | Constant values within the acceptable region for long time period (more than one day) | - | Important can be detected using rate of change | - |
| Large sudden spike | C | The spikes are larger than the surrounding data points but less than 6666. Example one or two values much higher than previous or next values | - | At any point in the time series, used 3 times standard deviation as threshold | - |
| Drift | D | Gradual change in values in positive to zero | - | High priority (Emergency shutdown, ESD) | - |
| Sudden zero | E | Value suddenly shifts in values in positive to zero | - | High priority (Emergency shutdown, ESD) | - |
| Step up (sudden shift) | F | Increase the mean while keeping the variance constant | - | Normal, indicate motor flips from OFF to ON | - |
| Step down (sudden shift) | G | Decrease the mean while keeping the variance constant | - | High priority (Emergency shutdown, ESD) | - |
Table 2: Discharge pressure related anomaly types
Download counts and popularity will appear here as the collector receives them.
| Version | Published | Direct dependencies | Open version |
|---|---|---|---|
| 0.0.1 | 23 Mar 2024 | 0 |
This describes our collected data; it is not a security assessment.
Observed since 20 Oct, 19:03 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
package:6102cf3f609e48aaf813d315ac471461c08df9ab82317bca37141274764f1289