Anomaly Detection and Fault Diagnosis Based on Action States for Excavators

Anomaly detection has been a challenging subject in many industrial fields. In industrial machinery such as hydraulic excavators, sensor data distributions are inherently multimodal because different operating conditions produce distinct sensor signatures, and conventional algorithms struggle to establish clear normal–abnormal boundaries when these conditions are mixed. We propose an action-state decomposition framework that partitions multimodal sensor data into homogeneous subsets based on discretized control inputs, thereby reducing the ambiguity of normal–abnormal boundaries by learning state-conditional distributions. The approach comprises a reactive method that evaluates each sample within its action state, and a history-based method that incorporates temporal context from previous action states. This decomposition is algorithm-agnostic and can improve detection performance across diverse anomaly detection algorithms. The framework is further extended to Bayesian fault diagnosis that identifies the root cause of failures using action-state-conditional detection probabilities. Experiments on simulated excavator data and two real-world benchmark datasets (UCI Hydraulic Systems and SKAB) demonstrate the generalizability of the proposed mode decomposition and provide insights into factors that may influence its effectiveness. The history-based method achieves a mean AUC of 0.89 across sensor fault types, outperforming all baseline algorithms, and the Bayesian fault diagnosis achieves 86.7% accuracy in identifying the root cause among six action fault types. For the proposed GMM-based methods, the decomposition also substantially reduces per-sample inference time by approximately 10× (from 8.68 μs to 0.75 μs), enabling real-time deployment in industrial settings.