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A Multi-Working States Sensor Anomaly Detection Method Using Deep Learning Algorithms.

Di Wu¹, Kari Koskinen¹, Eric Coatanea¹

¹Faculty of Engineering and Natural Sciences, Tampere University, 33720 Tampere, Finland.

Sensors (Basel, Switzerland)

|September 27, 2025

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This study introduces a novel method for sensor anomaly detection and isolation using Long Short-Term Memory (LSTM) networks, effectively handling unlabeled data from varying machine operational states to improve data quality.

Area of Science:

Data Science
Machine Learning
Sensor Technology

Background:

Sensor data often contains anomalies due to malfunctions or communication issues.
Existing data-driven methods struggle with inadequately labeled sensor data and changing operational states.

Purpose of the Study:

To propose a sensor anomaly detection and isolation method robust to unlabeled data and varying machine operational states.
To enhance the accuracy and reliability of sensor data processing.

Main Methods:

Utilized Long Short-Term Memory (LSTM) networks to predict sensor measurements based on historical data.
Implemented an input-selection strategy based on prediction errors from a small dataset to optimize LSTM performance.
Calculated the residual between predicted and actual measurements to identify anomalies.

Keywords:

LSTM data-driven deep learning sensor anomaly detection

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Main Results:

The proposed LSTM-based method accurately detected drift and stall anomalies in real-world truck sensor data.
The input-selection method improved prediction accuracy and reduced the impact of redundant sensors.
The approach effectively addressed challenges posed by unlabeled data and dynamic operational states.

Conclusions:

The developed sensor anomaly detection and isolation method is effective for handling unlabeled data in dynamic environments.
LSTM networks offer a promising approach for improving the integrity of sensor data in industrial applications.
The method demonstrates practical applicability in real-world scenarios, such as mining operations.