A HYBRID KALMAN–DEEP LEARNING FRAMEWORK FOR ANOMALY DETECTION IN INDUSTRIAL SENSOR DATA
Abstract
Reliable anomaly detection in industrial sensor data is challenging due to stochastic noise, nonlinear system dynamics, and complex temporal dependencies. This study proposes a hybrid multi-stage monitoring framework based on a three-channel anomaly detection pipeline that integrates statistical signal preprocessing, deep learning forecasting, reconstruction analysis, and neural classification. Initially, digital signal preprocessing and Kalman filtering are applied to obtain noise-reduced state estimates. The filtered signals are then processed through two parallel branches: an LSTM prediction model for detecting dynamic deviations and an LSTM autoencoder for identifying reconstruction-based anomalies. The resulting prediction and reconstruction errors, together with filtered signal states, are fused into a multi-channel feature vector and classified using a CNN–LSTM neural network. The proposed framework effectively combines temporal forecasting accuracy, reconstruction-based anomaly sensitivity, and local pattern detection capability, thereby improving robustness against noise and enhancing anomaly detection reliability in complex industrial environments. The novelty of this work lies in the unified three-channel anomaly detection architecture that simultaneously integrates statistical filtering, parallel deep learning analysis, and multi-objective optimization within a single monitoring framework. The proposed approach provides an effective solution for real-time industrial monitoring, early fault detection, and intelligent process control.