Factory IoT Sensor Efficiency Log
This dataset provides granular, time-stamped logs from factory IoT sensors, detailing machine efficiency, downtime, energy consumption, and environmental conditions. It enables manufacturing managers to monitor operational performance, identify bottlenecks, and implement predictive maintenance strategies for improved supply chain resilience and energy optimization.
Sample rows
preview · 8 of 85 rows · all 15 columns| log_idstring | operational_statusstring | efficiency_ratefloat | maintenance_flagboolean | machine_idstring | sensor_idstring | factory_idstring | timestampdatetime | energy_consumption_kwfloat | downtime_minutesfloat | error_codestring | temperature_celsiusfloat | humidity_percentfloat | operator_idstring | shift_codestring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| LOG-7G8F5B2C | running | 95.4 | false | MCH-1001 | SENS-0012 | FACT-NY01 | 2024-06-01T06:15:32 | 35.2 | blank | blank | 71.2 | 38.1 | OP-023 | A |
| LOG-2K9J4F6Q | maintenance | 0 | true | MCH-1003 | SENS-0084 | FACT-TX02 | 2024-06-01T07:22:09 | 2.1 | 47 | E-MAINT-01 | 59.3 | 45.2 | OP-019 | B |
| LOG-5T7U8S3H | idle | 18.7 | false | MCH-1023 | SENS-0103 | FACT-NC03 | 2024-06-01T08:03:58 | 8.9 | 20 | blank | 64.5 | 49.4 | OP-031 | C |
| LOG-6Y3M9N8D | running | 92.6 | false | MCH-1001 | SENS-0029 | FACT-NY01 | 2024-06-01T09:15:44 | 33.8 | blank | blank | 73.2 | 36.4 | OP-023 | A |
| LOG-J6L3D8F2 | stopped | 0 | false | MCH-1005 | SENS-0051 | FACT-TX02 | 2024-06-01T10:43:21 | 1.5 | 89 | E-STOP-03 | 58.1 | 47.7 | OP-014 | B |
| LOG-P2F4V6Q7 | running | 87.5 | false | MCH-1017 | SENS-0093 | FACT-IL04 | 2024-06-01T11:11:17 | 29.6 | blank | blank | 75.4 | 40.9 | OP-027 | C |
| LOG-R8J1B4E6 | idle | 12.9 | false | MCH-1008 | SENS-0047 | FACT-NC03 | 2024-06-01T12:36:40 | 6.7 | 15.6 | blank | 68.3 | 44.1 | OP-032 | Night |
| LOG-9F2K3L4Q | running | 97.1 | false | MCH-1001 | SENS-0012 | FACT-NY01 | 2024-06-01T13:04:22 | 37 | blank | blank | 70.7 | 39.2 | OP-023 | A |
| LOG-U3N7S2B8 | running | 88.2 | false | MCH-1013 | SENS-0066 | FACT-TX02 | 2024-06-01T14:25:09 | 29.9 | blank | blank | 76 | 41.6 | OP-020 | B |
| LOG-2H7R6M9N | maintenance | 0 | true | MCH-1019 | SENS-0073 | FACT-IL04 | 2024-06-01T15:41:42 | 2.4 | 50 | E-MAINT-02 | 60.1 | 48.3 | OP-028 | C |
| LOG-3V5B4J2K | stopped | 0 | false | MCH-1005 | SENS-0039 | FACT-TX02 | 2024-06-01T16:51:18 | 1 | 63.5 | E-STOP-02 | 57.9 | 46.5 | OP-014 | Night |
| LOG-5L8C2F1D | running | 91.2 | false | MCH-1002 | SENS-0017 | FACT-NY01 | 2024-06-01T17:13:56 | 32.7 | blank | blank | 72.5 | 37.3 | OP-024 | A |
| LOG-N6M5J4D8 | idle | 22.3 | false | MCH-1010 | SENS-0042 | FACT-NC03 | 2024-06-01T18:59:07 | 9.7 | 30.2 | blank | 65.8 | 50.1 | OP-033 | C |
| LOG-V5K1R3F7 | running | 89.8 | false | MCH-1018 | SENS-0068 | FACT-IL04 | 2024-06-01T19:44:53 | 30.2 | blank | blank | 77.2 | 42.7 | OP-028 | Night |
| LOG-F4H9D2B3 | maintenance | 0 | true | MCH-1004 | SENS-0031 | FACT-NY01 | 2024-06-01T20:21:41 | 3.1 | 66 | E-MAINT-03 | 62.9 | 44.6 | OP-026 | A |
| LOG-H1P7G6K3 | idle | 15.5 | false | MCH-1002 | SENS-0021 | FACT-NY01 | 2024-06-01T21:35:14 | 7.4 | 18.5 | blank | 66.2 | 48.7 | OP-025 | B |
| LOG-S3D8F2L4 | running | 90.6 | false | MCH-1015 | SENS-0091 | FACT-TX02 | 2024-06-01T22:09:52 | 33.1 | blank | blank | 74.6 | 39.9 | OP-021 | C |
| LOG-X2J8M3N1 | stopped | 0 | false | MCH-1006 | SENS-0033 | FACT-NC03 | 2024-06-01T23:56:08 | 1.8 | 74.2 | E-STOP-01 | 56.3 | 45.8 | OP-015 | Night |
| LOG-M3B9F7K5 | running | 93.9 | false | MCH-1014 | SENS-0089 | FACT-IL04 | 2024-06-02T00:42:35 | 34.4 | blank | blank | 75.9 | 41.2 | OP-029 | A |
| LOG-G4D2K8V1 | idle | 19.8 | false | MCH-1002 | SENS-0020 | FACT-NY01 | 2024-06-02T01:13:21 | 8.1 | 15 | blank | 67.9 | 50.6 | OP-025 | B |
What the 85 rows show
from the 85-row sampleRunning (operational status) stands out: mean efficiency_
- 15%maintenance_
flag = true - 22.3median efficiency_
rate - 4shift codes
- 15factories
- 8.9median energy_
consumption_ kw - 28.9median downtime_
minutes
Median 22.3, from 0.0 to 97.1.
- string 8
- float 5
- datetime 1
- boolean 1
Columns
15 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 8 columns | |||
log_id | string | Unique identifier for each sensor efficiency log entryunique | LOG-7G8F5B2C |
sensor_id | string | Unique identifier for the IoT sensor reporting the data | SENS-0012 |
machine_id | string | Unique identifier for the machine to which the sensor is attached | MCH-1001 |
factory_id | string | Unique identifier for the factory where the sensor is located | FACT-NY01 |
operational_status | string | Current operational status of the machine (e.g., running, idle, stopped, maintenance)running · idle · stopped · maintenance | running |
error_code | string | Error code reported by the sensor, if any (blank if no error)optional | E-MAINT-01 |
operator_id | string | Identifier for the operator responsible for the machine during the log entryoptional | OP-023 |
shift_code | string | Code representing the production shift (e.g., A, B, C, Night)A · B · C · Night · optional | A |
| Numbers 5 columns | |||
efficiency_rate | float | Measured efficiency rate of the machine at the time of logging (percentage, 0-100)0 to 100 | 95.4 |
energy_consumption_kw | float | Energy consumed by the machine during the logging interval (in kilowatts)0 or more | 35.2 |
downtime_minutes | float | Duration of downtime (in minutes) since the last log entry0 or more · optional | 47 |
temperature_celsius | float | Temperature reading from the sensor (in degrees Celsius)-40 to 150 · optional | 71.2 |
humidity_percent | float | Humidity reading from the sensor (percentage, 0-100)0 to 100 · optional | 38.1 |
| Dates and times 1 column | |||
timestamp | datetime | Date and time when the sensor log was recorded | 2024-06-01T06:15:32 |
| True or false 1 column | |||
maintenance_flag | boolean | Indicates if the machine is scheduled for maintenance (true/false)optional | false |
Use it for
A manufacturing dashboard
The maintenance_
flag rate, efficiency_ rate by operational_ status and a breakdown of machine_ id. Excel, Power BI or Tableau. Why do the 41 running rows have a mean efficiency_
rate of 91.8? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Logs85LOG-7G8F5B2C95.4runningLOG-2K9J4F6Q0maintena…LOG-2H7R6M9N0maintena…
A software demo
Believable logs with sensor_
id, machine_ id and factory_ id to fill a screen in front of a buyer.
This dataset85 rows15 columns
Yours1,000 rows15 columns
blueprint · factory-iot-sensor-efficiency-log
Behind this dataset
Same schema. As many rows as you need.
These 85 rows came out of a blueprint — 15 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Each row must represent a unique sensor reading event.
- Include timestamps, sensor ID, machine ID, efficiency percentage, and downtime minutes.
- Downtime events trigger a maintenance alert flag.
- Sensors exceeding energy consumption thresholds are flagged.
- Machine IDs cross-referenced to production batch records.
- Missing or dubious sensor data is marked for review.
Open the blueprint in Data Factory
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- factory-iot-sensor-efficiency-log