# Online Sensor Fault Detection Using Machine Learning Algorithms on a Laboratory-Scale Batch Reactor: LSTM Approach

## TL;DR

The paper introduces a new machine learning model for detecting sensor faults in a lab reactor, improving accuracy and speed compared to existing methods.

## Contribution

A novel CS-IMLSTM model is proposed for real-time sensor fault detection in batch reactors, combining CNN and LSTM with attention mechanisms.

## Key findings

- The CS-IMLSTM model outperforms LSTM and CNN-LSTM in fault detection accuracy.
- The model adapts faster to changing conditions and identifies abnormal situations effectively.
- It can be used for predictive maintenance in industrial chemical processes.

## Abstract

This article presents an online fault detection system
for a laboratory-scale
batch reactor (BR) using the Convolutional Neural Network (CNN)-Squeeze
and Excitation-based Improved Multi-Layer Long Short-Term Memory (CS-IMLSTM)
model. To identify superimposed and sparse sensor faults in real time,
the system continuously monitors the BR parameters, such as reactor
temperature, coolant flow rate, and heater current. To reduce noise
and dynamic fluctuations, the CS-IMLSTM integrates a channel–spatial
attention mechanism and enhances feature significance. The performance
of the proposed model is compared with LSTM and CNN-LSTM models. The
results indicate that the CS-IMLSTM demonstrates improved accuracy,
faster adaptation of online learning, and effectiveness in identifying
abnormal circumstances compared with LSTM and CNN-LSTM models. The
proposed approach can be used for intelligent predictive maintenance
in dynamic industrial environments to enhance the reliability and
safety of chemical process operations.