Condition Monitoring for Packaging Industry dataset (CoMoPI)
<p><strong>Condition Monitoring for Packaging Industry dataset (CoMoPI)</strong></p> <p>CoMoPI dataset contains data from eight industrial packaging machines:</p> <table> <thead> <tr> <th> <p><strong>serial</strong></p> </th> <th> <p><strong>Starting Date</strong></p> </th> <th> <p><strong>Ending Date</strong></p> </th> </tr> </thead> <tbody> <tr> <td> <p>A001</p> </td> <td> <p>2022-08-10 03:45:00+00:00</p> </td> <td> <p>2023-01-09 07:00:00+00:00</p> </td> </tr> <tr> <td> <p>A005</p> </td> <td> <p>2022-09-19 13:15:00+00:00</p> </td> <td> <p>2023-01-09 15:45:00+00:00</p> </td> </tr> <tr> <td> <p>B002</p> </td> <td> <p>2022-07-14 14:00:00+00:00</p> </td> <td> <p>2023-01-09 15:50:00+00:00</p> </td> </tr> <tr> <td> <p>B005</p> </td> <td> <p>2022-10-04 12:55:00+00:00</p> </td> <td> <p>2023-01-05 01:55:00+00:00</p> </td> </tr> <tr> <td> <p>C003</p> </td> <td> <p>2022-12-15 06:50:00+00:00</p> </td> <td> <p>2023-01-03 16:35:00+00:00</p> </td> </tr> <tr> <td> <p>C004</p> </td> <td> <p>2022-07-18 07:55:00+00:00</p> </td> <td> <p>2022-12-28 08:10:00+00:00</p> </td> </tr> <tr> <td> <p>E002</p> </td> <td> <p>2022-07-14 14:20:00+00:00</p> </td> <td> <p>2022-12-03 14:00:00+00:00</p> </td> </tr> <tr> <td> <p>E004</p> </td> <td> <p>2022-11-23 15:25:00+00:00</p> </td> <td> <p>2023-01-09 10:00:00+00:00</p> </td> </tr> </tbody> </table> <p>The dataset provides sensor measurements related to a specific module involved in the watertight closure of packages. It also provides all alarms and warnings generated by the packaging equipment.</p> <p><strong>Dataset description</strong></p> <p>For the sake of simplicity, the dataset is divided into three files, one for sensor measurements, one for alarms and one for warnings.</p> <p><em>Sensor measurements</em></p> <p>This data is included in the file called <em>industrial_dataset_sensors_10m_agg.csv</em>. Each row corresponds to average sensor value in a 10-minute window. Beyond the machine identifier ('_serial') and timestamp ('_time'), the following measurements are available: 'AE', 'BE', 'AF', 'BF', 'APP', 'BPP', 'AP', 'BP', 'ALE', 'BLE', 'ALP', 'BLP', 'ADS', 'BDS', 'AES', 'BES'. Suffixes A_ and B_ relate to the two elements that can perform a specific operation required by the watertight closure of packages. For confidentiality reasons, it is not possible to provide further details.</p> <p><em>Alarm measurements</em></p> <p>This data is included in the file called <em>industrial_dataset_alarm_10m_agg.csv</em>, according to the following schema:</p> <ul> <li>'_serial' : 'machine unique identifier'</li> <li>'_time' : 'timestamp',</li> <li>'AL_1' : 'Counter of AL_1 alarms' ,</li> <li>...</li> <li>'AL_123' : 'Counter of AL_123 alarms'</li> </ul> <p><em>Warning measurements</em></p> <p>This data is included in the file called <em>industrial_dataset_warnings_10m_agg</em>.csv_, according to the following schema:</p> <ul> <li>'_serial' : 'machine unique identifier'</li> <li>'_time' : 'timestamp',</li> <li>'WR_1' : 'Counter of AL_1 alarms' ,</li> <li>...</li> <li>'WR_358' : 'Counter of WR_358 alarms'</li> </ul> <p><strong>Condition monitoring</strong></p> <p>Alarms 'AL_53' are 'AL_54' are related to faulty conditions of the components and can be used as prediction target. The following alarms are generated by the target module: 'AL_17', 'AL_18', 'AL_40', 'AL_41', 'AL_42', 'AL_43', 'AL_45', 'AL_46', 'AL_47', 'AL_48', 'AL_49', 'AL_50', 'AL_51', 'AL_52', 'AL_53', 'AL_54'.</p> <p><strong>Anonymization procedure</strong></p> <p>The anonymization procedure is the following:</p> <ul> <li>To make it impossible to identify the specific piece of equipment, its serial number was replaced with a mock equipment_ID. Moreover, no additional information is provided about the machine, its location and the processed products.</li> <li>Sensor measurements were renamed and rescaled to remove all information related to actual working setup.</li> <li>Alarms and warnings underwent an anonymization process: no description is provided, and the original alarm codes were mapped to mock codes.</li> </ul>
ShareScore
44/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 8
- Harmonization
- 4
- Access
- 20
- Reuse readiness
- 8
- Engagement
- 4