Environmental Interference Gas Sensor Anomaly Dataset

#Time-series Anomaly Detection #Environmental Factor Correction #Anomaly Classification #Anomaly Confidence Evaluation #Gas Sensor Monitoring #Industrial Environment Monitoring #Equipment Condition Diagnosis #Sensor Data Quality Management

6530
0.4GB
JSON
CATL
MOBIUSI INC

Updated:2026-11-03

AI Analysis & Value Prop

This dataset links gas sensor readings with environmental variables such as temperature, humidity, and air pressure in JSON time-series records. It covers both pseudo-anomalies caused by environmental changes and genuine sensor anomalies under relatively stable environmental conditions. Raw measurements, environmental change features, adjusted readings, anomaly categories, and scores support the analysis of multivariate temporal dependencies and the training and evaluation of anomaly detection models.

Dataset Insights

Sample Examples

{}

Technical Specifications

Field	Type	Description
file_name	string	File name
is_anomaly	boolean	Indicates whether the current sensor record is classified as anomalous.
anomaly_score	number	A sensor anomaly severity score calculated from multivariate temporal dependencies and environment-adjusted readings.
raw_measurement	object	Raw JSON measurement records from gas sensors and environmental sensors.
anomaly_category	string	An anomaly classification for the current record based on the relationship between environmental changes and sensor behavior.
anomaly_confidence	number	The model's confidence in the anomaly detection result, with values ranging from 0 to 1.
environment_change_features	object	Environmental change features calculated from the current record and historical environmental variables within a time window.
environment_adjusted_gas_readings	object	Gas sensor readings adjusted to remove or reduce the effects of temperature, humidity, and air pressure.

Compliance Statement

Authorization Type	Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What types of data are included?: JSON time-series records link gas sensor measurements with environmental variables. Fields include environmental change features, adjusted gas readings, anomaly categories, anomaly scores, and confidence values.

How can environmental pseudo-anomalies be distinguished from genuine anomalies?: Records include environmental variables and time-window change features, along with adjusted readings and anomaly categories. These data can support training and evaluation of models that distinguish environmental effects from sensor faults; the exact decision criteria depend on the task definition and model design.

Which training and evaluation tasks does the dataset support?: It can support multivariate time-series anomaly detection, anomaly classification, environmental factor correction, and anomaly confidence evaluation. It is also suitable for hard example mining focused on environmental confounding factors.

How can the dataset be obtained, and what licensing terms apply?: Check the dataset product page for available access methods, specifications, and licensing terms. Permitted use, redistribution rights, and commercial use are subject to the applicable license agreement.

Can't find the data you need?

Post a request and let data providers reach out to you.

Get this Dataset

Verified for Enterprise Use

Cite this Work

@dataset{Mobiusi2026,
  title={Environmental Interference Gas Sensor Anomaly Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/0f0ef7f2fae324db44468184ca167d6d},
  urldate={2026-10-28},
  keywords={gas sensor anomaly dataset,sensor anomaly detection,environmental interference data,multivariate time series,gas sensor readings,temperature humidity pressure monitoring,environmental correction,industrial sensor data},
  version={1.0}
}

Using this in research? Please cite us.

placeholder

Scene

Domain

Modality

Task