Development and Analysis of AI-Driven Anomaly Detection and Predictive Maintenance Algorithms for Robotic Systems in Industrial Environments: Leveraging Electrical and Sensor Data
Industrial robotic systems are essential to modern manufacturing, but their reliability is threatened by progressive mechanical and electrical degradation. Traditional reactive and preventive maintenance strategies are inadequate for the complex, high-dimensional sensor environments of contemporary industrial robots. This study developed and evaluated AE-LSTM-ATT (Attention-Enhanced Hybrid LSTM Autoencoder), an LSTM encoder-decoder architecture with Bahdanau-style additive attention, for unsupervised anomaly detection in industrial robotic systems. The model was evaluated on the public Industrial Robot Anomaly Detection (IndRAD) dataset (robot joint positions, velocities, torques, and motor currents) against an attention-free LSTM Autoencoder and an Isolation Forest baseline, across five independent training runs and four synthetically injected anomaly types (spike, step, freeze-to-zero, and freeze-to-last-value). AE-LSTM-ATT and the attention-free baseline performed comparably on abrupt anomalies (spike, step; AUC-ROC (Area Under the Receiver Operating Characteristic Curve) = 1.000 for both). For the subtler, gradually manifesting anomaly types, attention provided a small, directionally positive advantage: for freeze-to-last-value, AE-LSTM-ATT achieved AUC-ROC = 0.518 ± 0.059 and F1-score = 0.682 ± 0.020 (95% CI, 5 seeds), versus 0.491 ± 0.046 and 0.670 ± 0.017 for the baseline; freeze-to-zero showed overlapping confidence intervals. An independent replication of this comparison with a paired significance test across five matched seeds (section "Ablation: Contribution of the Attention Mechanism") did not find the difference to be statistically significant, and the direction of the per-seed difference was not uniform. These results indicate that any attention-related benefit in this architecture is, at most, small and anomaly-type dependent, and is not established as statistically significant at the sample sizes evaluated here; attention showed no distinguishable advantage for abrupt, high-amplitude faults already near-perfectly separable by reconstruction error alone. The framework operates in an unsupervised paradigm requiring no labelled fault data, supporting deployment in settings where run-to-failure data are scarce. Overall, this study finds no evidence that attention-enhanced temporal autoencoders offer a universal advantage over simpler deep learning baselines for industrial robotic predictive maintenance; any benefit appears concentrated in detecting subtle, temporally extended fault signatures, is small in magnitude, and was not confirmed as statistically significant in the present sample.